diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 237c9ed..e2a410d 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -1,21 +1,21 @@
 {
     "$schema": "https://raw.githubusercontent.com/devcontainers/spec/main/schemas/devContainer.schema.json",
     "name": "nfcore",
-    "image": "nfcore/devcontainer:latest",
+    "image": "nfcore/gitpod:latest",
+    "remoteUser": "gitpod",
+    "runArgs": ["--privileged"],
 
-    "remoteUser": "root",
-    "privileged": true,
+    // Configure tool-specific properties.
+    "customizations": {
+        // Configure properties specific to VS Code.
+        "vscode": {
+            // Set *default* container specific settings.json values on container create.
+            "settings": {
+                "python.defaultInterpreterPath": "/opt/conda/bin/python"
+            },
 
-    "remoteEnv": {
-        // Workspace path on the host for mounting with docker-outside-of-docker
-        "LOCAL_WORKSPACE_FOLDER": "${localWorkspaceFolder}"
-    },
-
-    "onCreateCommand": "./.devcontainer/setup.sh",
-
-    "hostRequirements": {
-        "cpus": 4,
-        "memory": "16gb",
-        "storage": "32gb"
+            // Add the IDs of extensions you want installed when the container is created.
+            "extensions": ["ms-python.python", "ms-python.vscode-pylance", "nf-core.nf-core-extensionpack"]
+        }
     }
 }
diff --git a/.devcontainer/setup.sh b/.devcontainer/setup.sh
deleted file mode 100755
index 2ca6343..0000000
--- a/.devcontainer/setup.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/usr/bin/env bash
-
-# Customise the terminal command prompt
-echo "export PROMPT_DIRTRIM=2" >> $HOME/.bashrc
-echo "export PS1='\[\e[3;36m\]\w ->\[\e[0m\\] '" >> $HOME/.bashrc
-export PROMPT_DIRTRIM=2
-export PS1='\[\e[3;36m\]\w ->\[\e[0m\\] '
-
-# Update Nextflow
-nextflow self-update
-
-# Update welcome message
-echo "Welcome to the nf-core/deepmutscan devcontainer!" > /usr/local/etc/vscode-dev-containers/first-run-notice.txt
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 0000000..6d9b74c
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,37 @@
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+indent_size = 4
+indent_style = space
+
+[*.{md,yml,yaml,html,css,scss,js}]
+indent_size = 2
+
+# These files are edited and tested upstream in nf-core/modules
+[/modules/nf-core/**]
+charset = unset
+end_of_line = unset
+insert_final_newline = unset
+trim_trailing_whitespace = unset
+indent_style = unset
+[/subworkflows/nf-core/**]
+charset = unset
+end_of_line = unset
+insert_final_newline = unset
+trim_trailing_whitespace = unset
+indent_style = unset
+
+[/assets/email*]
+indent_size = unset
+
+# ignore python and markdown
+[*.{py,md}]
+indent_style = unset
+
+# ignore ro-crate metadata files
+[**/ro-crate-metadata.json]
+insert_final_newline = unset
diff --git a/.github/workflows/fix-linting.yml b/.github/workflows/fix-linting.yml
new file mode 100644
index 0000000..8bda64e
--- /dev/null
+++ b/.github/workflows/fix-linting.yml
@@ -0,0 +1,89 @@
+name: Fix linting from a comment
+on:
+  issue_comment:
+    types: [created]
+
+jobs:
+  fix-linting:
+    # Only run if comment is on a PR with the main repo, and if it contains the magic keywords
+    if: >
+      contains(github.event.comment.html_url, '/pull/') &&
+      contains(github.event.comment.body, '@nf-core-bot fix linting') &&
+      github.repository == 'nf-core/deepmutscan'
+    runs-on: ubuntu-latest
+    steps:
+      # Use the @nf-core-bot token to check out so we can push later
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
+        with:
+          token: ${{ secrets.nf_core_bot_auth_token }}
+
+      # indication that the linting is being fixed
+      - name: React on comment
+        uses: peter-evans/create-or-update-comment@71345be0265236311c031f5c7866368bd1eff043 # v4
+        with:
+          comment-id: ${{ github.event.comment.id }}
+          reactions: eyes
+
+      # Action runs on the issue comment, so we don't get the PR by default
+      # Use the gh cli to check out the PR
+      - name: Checkout Pull Request
+        run: gh pr checkout ${{ github.event.issue.number }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.nf_core_bot_auth_token }}
+
+      # Install and run pre-commit
+      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5
+        with:
+          python-version: "3.12"
+
+      - name: Install pre-commit
+        run: pip install pre-commit
+
+      - name: Run pre-commit
+        id: pre-commit
+        run: pre-commit run --all-files
+        continue-on-error: true
+
+      # indication that the linting has finished
+      - name: react if linting finished succesfully
+        if: steps.pre-commit.outcome == 'success'
+        uses: peter-evans/create-or-update-comment@71345be0265236311c031f5c7866368bd1eff043 # v4
+        with:
+          comment-id: ${{ github.event.comment.id }}
+          reactions: "+1"
+
+      - name: Commit & push changes
+        id: commit-and-push
+        if: steps.pre-commit.outcome == 'failure'
+        run: |
+          git config user.email "core@nf-co.re"
+          git config user.name "nf-core-bot"
+          git config push.default upstream
+          git add .
+          git status
+          git commit -m "[automated] Fix code linting"
+          git push
+
+      - name: react if linting errors were fixed
+        id: react-if-fixed
+        if: steps.commit-and-push.outcome == 'success'
+        uses: peter-evans/create-or-update-comment@71345be0265236311c031f5c7866368bd1eff043 # v4
+        with:
+          comment-id: ${{ github.event.comment.id }}
+          reactions: hooray
+
+      - name: react if linting errors were not fixed
+        if: steps.commit-and-push.outcome == 'failure'
+        uses: peter-evans/create-or-update-comment@71345be0265236311c031f5c7866368bd1eff043 # v4
+        with:
+          comment-id: ${{ github.event.comment.id }}
+          reactions: confused
+
+      - name: react if linting errors were not fixed
+        if: steps.commit-and-push.outcome  == 'failure'
+        uses: peter-evans/create-or-update-comment@71345be0265236311c031f5c7866368bd1eff043 # v4
+        with:
+          issue-number: ${{ github.event.issue.number }}
+          body: |
+            @${{ github.actor }} I tried to fix the linting errors, but it didn't work. Please fix them manually.
+            See [CI log](https://github.com/nf-core/deepmutscan/actions/runs/${{ github.run_id }}) for more details.
diff --git a/.github/workflows/template_version_comment.yml b/.github/workflows/template_version_comment.yml
new file mode 100644
index 0000000..537529b
--- /dev/null
+++ b/.github/workflows/template_version_comment.yml
@@ -0,0 +1,46 @@
+name: nf-core template version comment
+# This workflow is triggered on PRs to check if the pipeline template version matches the latest nf-core version.
+# It posts a comment to the PR, even if it comes from a fork.
+
+on: pull_request_target
+
+jobs:
+  template_version:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out pipeline code
+        uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4
+        with:
+          ref: ${{ github.event.pull_request.head.sha }}
+
+      - name: Read template version from .nf-core.yml
+        uses: nichmor/minimal-read-yaml@v0.0.2
+        id: read_yml
+        with:
+          config: ${{ github.workspace }}/.nf-core.yml
+
+      - name: Install nf-core
+        run: |
+          python -m pip install --upgrade pip
+          pip install nf-core==${{ steps.read_yml.outputs['nf_core_version'] }}
+
+      - name: Check nf-core outdated
+        id: nf_core_outdated
+        run: echo "OUTPUT=$(pip list --outdated | grep nf-core)" >> ${GITHUB_ENV}
+
+      - name: Post nf-core template version comment
+        uses: mshick/add-pr-comment@b8f338c590a895d50bcbfa6c5859251edc8952fc # v2
+        if: |
+          contains(env.OUTPUT, 'nf-core')
+        with:
+          repo-token: ${{ secrets.NF_CORE_BOT_AUTH_TOKEN }}
+          allow-repeats: false
+          message: |
+            > [!WARNING]
+            > Newer version of the nf-core template is available.
+            >
+            > Your pipeline is using an old version of the nf-core template: ${{ steps.read_yml.outputs['nf_core_version'] }}.
+            > Please update your pipeline to the latest version.
+            >
+            > For more documentation on how to update your pipeline, please see the [nf-core documentation](https://github.com/nf-core/tools?tab=readme-ov-file#sync-a-pipeline-with-the-template) and [Synchronisation documentation](https://nf-co.re/docs/contributing/sync).
+          #
diff --git a/.gitignore b/.gitignore
index cc2b1a7..0187478 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,10 @@ testing*
 *.pyc
 null/
 .lineage/
+
+# Pipeline outputs
+test_results/
+work/
+.nextflow*
+.nf-test*
+.Rhistory
diff --git a/.gitpod.yml b/.gitpod.yml
new file mode 100644
index 0000000..83599f6
--- /dev/null
+++ b/.gitpod.yml
@@ -0,0 +1,10 @@
+image: nfcore/gitpod:latest
+tasks:
+  - name: Update Nextflow and setup pre-commit
+    command: |
+      pre-commit install --install-hooks
+      nextflow self-update
+
+vscode:
+  extensions:
+    - nf-core.nf-core-extensionpack # https://github.com/nf-core/vscode-extensionpack
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f51e1a2..d237c9a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -15,7 +15,9 @@ repos:
               .*ro-crate-metadata.json$|
               modules/(?!local/).*|
               subworkflows/(?!local/).*|
-              .*\.snap$
+              .*\.snap$|
+              assets/test_structure/.*\.pdb$|
+              assets/variant_effect_inspection_tool/3Dmol-min\.js.*
           )$
       - id: end-of-file-fixer
         exclude: |
@@ -23,7 +25,9 @@ repos:
               .*ro-crate-metadata.json$|
               modules/(?!local/).*|
               subworkflows/(?!local/).*|
-              .*\.snap$
+              .*\.snap$|
+              assets/test_structure/.*\.pdb$|
+              assets/variant_effect_inspection_tool/3Dmol-min\.js.*
           )$
   - repo: https://github.com/seqeralabs/nf-lint-pre-commit
     rev: v0.3.0
diff --git a/.prettierignore b/.prettierignore
index 63cde50..0bd1d5e 100644
--- a/.prettierignore
+++ b/.prettierignore
@@ -12,3 +12,4 @@ bin/
 ro-crate-metadata.json
 modules/nf-core/
 subworkflows/nf-core/
+assets/variant_effect_inspection_tool/3Dmol-min.js
diff --git a/CITATIONS.md b/CITATIONS.md
index 3ea1015..8136400 100644
--- a/CITATIONS.md
+++ b/CITATIONS.md
@@ -18,6 +18,26 @@
 
 > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.
 
+- [SAMtools](https://pubmed.ncbi.nlm.nih.gov/33590861/)
+
+> Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. Twelve years of SAMtools and BCFtools. Gigascience. 2021 Feb 16;10(2):giab008. doi: 10.1093/gigascience/giab008. PubMed PMID: 33590861; PubMed Central PMCID: PMC7931819.
+
+- [pysam](https://github.com/pysam-developers/pysam)
+
+> Heger A, et al. pysam: a Python module for reading, manipulating and writing genomic data sets. (htslib bindings).
+
+- [Polars](https://github.com/pola-rs/polars)
+
+> Vink R, et al. Polars: Lightning-fast DataFrame library for Rust and Python.
+
+- [Biopython](https://pubmed.ncbi.nlm.nih.gov/19304878/)
+
+> Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJ. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009 Jun 1;25(11):1422-3. doi: 10.1093/bioinformatics/btp163. PubMed PMID: 19304878; PubMed Central PMCID: PMC2682512.
+
+- [3Dmol.js](https://pubmed.ncbi.nlm.nih.gov/25505090/)
+
+> Rego N, Koes D. 3Dmol.js: molecular visualization with WebGL. Bioinformatics. 2015 Apr 15;31(8):1322-4. doi: 10.1093/bioinformatics/btu829. PubMed PMID: 25505090; PubMed Central PMCID: PMC4393523. (Bundled in the interactive variant effect inspection tool; BSD-3-Clause.)
+
 ## Software packaging/containerisation tools
 
 - [Anaconda](https://anaconda.com)
diff --git a/README.md b/README.md
index 81bf46a..7a45e11 100644
--- a/README.md
+++ b/README.md
@@ -5,8 +5,7 @@
   </picture>
 </h1>
 
-[![Open in GitHub Codespaces](https://img.shields.io/badge/Open_In_GitHub_Codespaces-black?labelColor=grey&logo=github)](https://github.com/codespaces/new/nf-core/deepmutscan)
-[![GitHub Actions CI Status](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml)
+[![GitHub Actions CI Status](https://github.com/nf-core/deepmutscan/actions/workflows/ci.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/ci.yml)
 [![GitHub Actions Linting Status](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/deepmutscan/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)
 [![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)
 
@@ -17,92 +16,110 @@
 [![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)
 [![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/deepmutscan)
 
-[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmutscan-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmutscan)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000&logo=bluesky)](https://bsky.app/profile/nf-co.re)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)
+[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmutscan-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmutscan)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)
 
 ## Introduction
 
-**nf-core/deepmutscan** is a bioinformatics pipeline that ...
+**nf-core/deepmutscan** is a workflow designed for the analysis of deep mutational scanning (DMS) data. DMS enables researchers to experimentally measure the fitness effects of thousands of genes or gene variants simultaneously, helping to classify disease causing mutants in human and animal populations, to learn the fundamental rules of protein architecture, small-molecule binding, mRNA splicing, viral evolution and many other quantifiable phenotypes.
 
-<!-- TODO nf-core:
-   Complete this sentence with a 2-3 sentence summary of what types of data the pipeline ingests, a brief overview of the
-   major pipeline sections and the types of output it produces. You're giving an overview to someone new
-   to nf-core here, in 15-20 seconds. For an example, see https://github.com/nf-core/rnaseq/blob/master/README.md#introduction
--->
+While DNA synthesis and sequencing technologies have advanced substantially, long open reading frame (ORF) targets still present a major challenge for DMS studies. Shotgun DNA sequencing can be used to greatly speed up the inference of long ORF mutant fitness landscapes, theoretically at no expense in accuracy. We have designed the `nf-core/deepmutscan` pipeline to unlock the power of shotgun sequencing based DMS studies on long ORFs, to simplify and standardise the complex bioinformatics steps involved in data processing of such experiments – from read alignment to QC reporting and fitness landscape inferences.
 
-<!-- TODO nf-core: Include a figure that guides the user through the major workflow steps. Many nf-core
-     workflows use the "tube map" design for that. See https://nf-co.re/docs/community/brand/workflow-schematics#examples for examples.   -->
-<!-- TODO nf-core: Fill in short bullet-pointed list of the default steps in the pipeline -->1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))
+![nf-core/deepmutscan workflow](docs/images/pipeline.png)
+
+The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
+
+On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/deepmutscan/results).
+
+## Major features
+
+- End-to-end analyses of various DMS data
+- Modular, three-stage workflow: alignment → QC → error-aware fitness estimation
+- Integration with popular statistical fitness estimation tools like [DiMSum](https://github.com/lehner-lab/DiMSum), [Enrich2](https://github.com/FowlerLab/Enrich2), [rosace](https://github.com/pimentellab/rosace/) and [mutscan](https://github.com/fmicompbio/mutscan)
+- Support of multiple mutagenesis strategies, e.g. by nicking with degenerate NNK and NNS codons
+- Containerisation via Docker, Singularity and Apptainer
+- Scalability across HPC and Cloud systems
+- Monitoring of CPU, memory, and CO₂ usage
+
+For more details on the pipeline and on potential future expansions, please consider reading our [usage description](https://nf-co.re/deepmutscan/usage).
+
+## Step-by-step pipeline summary
+
+The pipeline processes deep mutational scanning (DMS) sequencing data in several stages:
+
+1. Alignment of reads to the reference open reading frame (ORF) (`BWA-mem`)
+2. Filtering of wildtype and erroneous reads (`samtools view`)
+3. Read merging for base error reduction (`vsearch merge`)
+4. Mutation counting
+5. Single nucleotide variant error correction
+6. DMS library quality control
+7. Data summarisation across samples
+8. Fitness estimation (`DiMSum`, `mutscan`)
 
 ## Usage
 
 > [!NOTE]
 > If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-pipeline) with `-profile test` before running the workflow on actual data.
 
-<!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets.
-     Explain what rows and columns represent. For instance (please edit as appropriate):
-
-First, prepare a samplesheet with your input data that looks as follows:
+First, prepare a samplesheet with your input/output data in which each row represents a pair of fastq files (paired end). This should look as follows:
 
-`samplesheet.csv`:
-
-```csv
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
+```csv title="samplesheet.csv"
+sample,type,replicate,file1,file2
+ORF1,input,1,/reads/forward1.fastq.gz,/reads/reverse1.fastq.gz
+ORF1,input,2,/reads/forward2.fastq.gz,/reads/reverse2.fastq.gz
+ORF1,output,1,/reads/forward3.fastq.gz,/reads/reverse3.fastq.gz
+ORF1,output,2,/reads/forward4.fastq.gz,/reads/reverse4.fastq.gz
 ```
 
-Each row represents a fastq file (single-end) or a pair of fastq files (paired end).
-
--->
+Secondly, specify the gene or gene region of interest using a reference FASTA file via `--fasta`. Provide the exact codon coordinates using `--reading_frame`.
 
 Now, you can run the pipeline using:
 
-<!-- TODO nf-core: update the following command to include all required parameters for a minimal example -->
-
-```bash
+```bash title="example_run.sh"
 nextflow run nf-core/deepmutscan \
    -profile <docker/singularity/.../institute> \
-   --input samplesheet.csv \
-   --outdir <OUTDIR>
+   --input ./samplesheet.csv \
+   --fasta ./ref.fa \
+   --reading_frame 1-300 \
+   --outdir ./results
 ```
 
-> [!WARNING]
-> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/running/run-pipelines#using-parameter-files).
-
-For more details and further functionality, please refer to the [usage documentation](https://nf-co.re/deepmutscan/usage) and the [parameter documentation](https://nf-co.re/deepmutscan/parameters).
-
 ## Pipeline output
 
 To see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/deepmutscan/results) tab on the nf-core website pipeline page.
+
 For more details about the output files and reports, please refer to the
 [output documentation](https://nf-co.re/deepmutscan/output).
 
-## Credits
+## Contributing
 
-nf-core/deepmutscan was originally written by Benjamin Wehnert & Max Stammnitz.
+We welcome contributions from the community!
 
-We thank the following people for their extensive assistance in the development of this pipeline:
+For technical challenges and feedback on the pipeline, please use our [Github repository](https://github.com/nf-core/deepmutscan). Please open an [issue](https://github.com/nf-core/deepmutscan/issues/new) or [pull request](https://github.com/nf-core/deepmutscan/compare) to:
 
-<!-- TODO nf-core: If applicable, make list of people who have also contributed -->
+- Report bugs or solve data incompatibilities when running `nf-core/deepmutscan`
+- Suggest the implementation of new modules for custom DMS workflows
+- Help improve this documentation
 
-## Contributions and Support
+If you are interested in getting involved as a developer, please consider joining our interactive [`#deepmutscan` Slack channel](https://nfcore.slack.com/channels/deepmutscan) (via [this invite](https://nf-co.re/join/slack)).
 
-If you would like to contribute to this pipeline, please see the [contributing guidelines](docs/CONTRIBUTING.md).
+## Credits
 
-For further information or help, don't hesitate to get in touch on the [Slack `#deepmutscan` channel](https://nfcore.slack.com/channels/deepmutscan) (you can join with [this invite](https://nf-co.re/join/slack)).
+nf-core/deepmutscan was originally written by [Benjamin Wehnert](https://github.com/BenjaminWehnert1008) and [Max Stammnitz](https://github.com/MaximilianStammnitz) at the [Centre for Genomic Regulation, Barcelona](https://www.crg.eu/), with the generous support of an EMBO Long-term Postdoctoral Fellowship and a Marie Skłodowska-Curie grant by the European Union.
+
+If you use `nf-core/deepmutscan` in your analyses, please cite:
 
-## Citations
+> 📄 Wehnert et al., _bioRxiv_ preprint (coming soon)
 
-<!-- TODO nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->
-<!-- If you use nf-core/deepmutscan for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->
+Please also cite the `nf-core` framework:
 
-<!-- TODO nf-core: Add bibliography of tools and data used in your pipeline -->
+> 📄 Ewels et al., _Nature Biotechnology_, 2020
+> [https://doi.org/10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)
+
+For further information or help, don't hesitate to get in touch on the [Slack `#deepmutscan` channel](https://nfcore.slack.com/channels/deepmutscan) (you can join with [this invite](https://nf-co.re/join/slack)).
 
-An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.
+## Scientific contact
 
-You can cite the `nf-core` publication as follows:
+For scientific discussions around the use of this pipeline (e.g. on experimental design or sequencing data requirements), please feel free to get in touch with us directly:
 
-> **The nf-core framework for community-curated bioinformatics pipelines.**
->
-> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
->
-> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).
+- Benjamin Wehnert — wehnertbenjamin@gmail.com
+- Maximilian Stammnitz — maximilian.stammnitz@crg.eu
diff --git a/assets/error_correction_report/ec_report_template.html b/assets/error_correction_report/ec_report_template.html
new file mode 100644
index 0000000..70f4708
--- /dev/null
+++ b/assets/error_correction_report/ec_report_template.html
@@ -0,0 +1,1839 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>nf-core/deepmutscan · sequencing-error correction · __SAMPLE__</title>
+    <style>
+      :root {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --pos: #2f9e6b;
+        --neg: #d1495b;
+        --ring: #ffffff;
+      }
+      @media (prefers-color-scheme: dark) {
+        :root {
+          --bg: #0f1219;
+          --panel: #161b24;
+          --ink: #e8ecf3;
+          --ink2: #a8b2c1;
+          --muted: #6b7688;
+          --line: #232a36;
+          --grid: #1c222d;
+          --accent: #5b90ee;
+          --accent-soft: #22304a;
+          --pos: #3fb883;
+          --neg: #e06377;
+          --ring: #0f1219;
+        }
+      }
+      :root[data-theme="dark"] {
+        --bg: #0f1219;
+        --panel: #161b24;
+        --ink: #e8ecf3;
+        --ink2: #a8b2c1;
+        --muted: #6b7688;
+        --line: #232a36;
+        --grid: #1c222d;
+        --accent: #5b90ee;
+        --accent-soft: #22304a;
+        --pos: #3fb883;
+        --neg: #e06377;
+        --ring: #0f1219;
+      }
+      :root[data-theme="light"] {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --pos: #2f9e6b;
+        --neg: #d1495b;
+        --ring: #ffffff;
+      }
+      /* Substitution-class palette. Fixed slot order (a class never changes colour
+         with the data); validated as a set for CVD separation, lightness band and
+         chroma against this report's panel surfaces, light and dark. */
+      :root {
+        --c1: #2a78d6;
+        --c2: #1baf7a;
+        --c3: #eda100;
+        --c4: #008300;
+        --c5: #4a3aa7;
+        --c6: #e34948;
+      }
+      @media (prefers-color-scheme: dark) {
+        :root {
+          --c1: #3987e5;
+          --c2: #199e70;
+          --c3: #c98500;
+          --c4: #008300;
+          --c5: #9085e9;
+          --c6: #e66767;
+        }
+      }
+      :root[data-theme="dark"] {
+        --c1: #3987e5;
+        --c2: #199e70;
+        --c3: #c98500;
+        --c4: #008300;
+        --c5: #9085e9;
+        --c6: #e66767;
+      }
+      :root[data-theme="light"] {
+        --c1: #2a78d6;
+        --c2: #1baf7a;
+        --c3: #eda100;
+        --c4: #008300;
+        --c5: #4a3aa7;
+        --c6: #e34948;
+      }
+      * {
+        box-sizing: border-box;
+      }
+      body {
+        margin: 0;
+        background: var(--bg);
+        color: var(--ink);
+        font:
+          14px/1.5 -apple-system,
+          BlinkMacSystemFont,
+          "Segoe UI",
+          Roboto,
+          Helvetica,
+          Arial,
+          sans-serif;
+        -webkit-font-smoothing: antialiased;
+      }
+      .wrap {
+        max-width: 1080px;
+        margin: 0 auto;
+        padding: 28px 22px 60px;
+      }
+      header {
+        display: flex;
+        align-items: baseline;
+        gap: 14px;
+        flex-wrap: wrap;
+        margin-bottom: 4px;
+      }
+      h1 {
+        font-size: 20px;
+        font-weight: 650;
+        margin: 0;
+        letter-spacing: -0.01em;
+      }
+      .lead {
+        color: var(--ink2);
+        font-size: 13px;
+        margin: 6px 0 0;
+        max-width: 760px;
+      }
+      .sub {
+        color: var(--ink2);
+        font-size: 13px;
+      }
+      .badge {
+        display: inline-block;
+        padding: 2px 9px;
+        border-radius: 999px;
+        background: var(--accent-soft);
+        color: var(--accent);
+        font-size: 12px;
+        font-weight: 600;
+        border: 1px solid transparent;
+      }
+      .tiles {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+        gap: 12px;
+        margin: 20px 0 26px;
+      }
+      .tile {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 12px;
+        padding: 14px 16px;
+      }
+      .tile .k {
+        color: var(--ink2);
+        font-size: 12px;
+        font-weight: 500;
+      }
+      .tile .v {
+        font-size: 22px;
+        font-weight: 650;
+        letter-spacing: -0.02em;
+        margin-top: 3px;
+      }
+      .tile .v small {
+        font-size: 13px;
+        font-weight: 500;
+        color: var(--muted);
+      }
+      .sechead {
+        display: flex;
+        align-items: baseline;
+        gap: 10px;
+        margin: 34px 0 2px;
+      }
+      .sechead .n {
+        font-size: 12px;
+        font-weight: 700;
+        color: var(--accent);
+        letter-spacing: 0.04em;
+      }
+      .sechead h2 {
+        font-size: 16px;
+        font-weight: 650;
+        margin: 0;
+        letter-spacing: -0.01em;
+      }
+      .secsub {
+        color: var(--ink2);
+        font-size: 12.5px;
+        margin: 3px 0 14px;
+        max-width: 820px;
+      }
+      .panel {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 14px;
+        padding: 16px 18px;
+        margin-bottom: 18px;
+      }
+      .panel h3 {
+        font-size: 13.5px;
+        font-weight: 600;
+        margin: 0 0 2px;
+      }
+      .panel .desc {
+        color: var(--ink2);
+        font-size: 12.5px;
+        margin: 0 0 12px;
+      }
+      .grid2 {
+        display: grid;
+        grid-template-columns: 1fr 1fr;
+        gap: 18px;
+      }
+      @media (max-width: 760px) {
+        .grid2 {
+          grid-template-columns: 1fr;
+        }
+      }
+      svg {
+        display: block;
+        width: 100%;
+        height: auto;
+        overflow: visible;
+      }
+      .axis {
+        stroke: var(--line);
+        stroke-width: 1;
+      }
+      .grid {
+        stroke: var(--grid);
+        stroke-width: 1;
+      }
+      .tick {
+        fill: var(--muted);
+        font-size: 10px;
+      }
+      .albl {
+        fill: var(--ink2);
+        font-size: 11px;
+      }
+      .tt {
+        position: fixed;
+        pointer-events: none;
+        background: var(--ink);
+        color: var(--bg);
+        font-size: 12px;
+        padding: 5px 8px;
+        border-radius: 7px;
+        opacity: 0;
+        transition: opacity 0.08s;
+        white-space: nowrap;
+        z-index: 50;
+        box-shadow: 0 4px 14px rgba(0, 0, 0, 0.18);
+      }
+      .controls {
+        display: flex;
+        gap: 10px;
+        align-items: center;
+        margin-bottom: 10px;
+        flex-wrap: wrap;
+      }
+      input[type="search"] {
+        flex: 1;
+        min-width: 160px;
+        background: var(--bg);
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        padding: 7px 11px;
+        color: var(--ink);
+        font-size: 13px;
+      }
+      table {
+        width: 100%;
+        border-collapse: collapse;
+        font-variant-numeric: tabular-nums;
+      }
+      .tblwrap {
+        max-height: 420px;
+        overflow: auto;
+        border: 1px solid var(--line);
+        border-radius: 10px;
+      }
+      th,
+      td {
+        padding: 7px 12px;
+        text-align: right;
+        border-bottom: 1px solid var(--line);
+        white-space: nowrap;
+      }
+      th:first-child,
+      td:first-child {
+        text-align: left;
+      }
+      thead th {
+        position: sticky;
+        top: 0;
+        background: var(--panel);
+        cursor: pointer;
+        user-select: none;
+        font-size: 12px;
+        color: var(--ink2);
+        font-weight: 600;
+        z-index: 1;
+      }
+      thead th:hover {
+        color: var(--ink);
+      }
+      th .ar {
+        color: var(--muted);
+        font-size: 10px;
+      }
+      td.pos {
+        color: var(--pos);
+      }
+      td.neg {
+        color: var(--neg);
+      }
+      tbody tr:hover {
+        background: var(--bg);
+      }
+      .foot {
+        color: var(--muted);
+        font-size: 11.5px;
+        margin-top: 26px;
+        text-align: center;
+      }
+      .sub2 {
+        font-size: 12.5px;
+        font-weight: 600;
+        color: var(--ink2);
+        margin: 0 0 8px;
+      }
+      .legend {
+        display: flex;
+        flex-wrap: wrap;
+        gap: 6px;
+        margin: 2px 0 12px;
+      }
+      .row {
+        display: flex;
+        flex-wrap: wrap;
+        gap: 6px;
+        align-items: center;
+      }
+      .sw {
+        display: inline-block;
+        width: 10px;
+        height: 10px;
+        border-radius: 3px;
+        flex: none;
+        vertical-align: middle;
+      }
+      code {
+        font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+        font-size: 12px;
+      }
+      .chip {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        padding: 3px 9px 3px 7px;
+        border: 1px solid var(--line);
+        border-radius: 999px;
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 12px;
+        font-variant-numeric: tabular-nums;
+        cursor: pointer;
+        user-select: none;
+      }
+      .chip:hover {
+        color: var(--ink);
+      }
+      .chip .sw {
+        width: 10px;
+        height: 10px;
+        border-radius: 3px;
+        flex: none;
+      }
+      .chip.off {
+        opacity: 0.38;
+      }
+      .chip.off .sw {
+        background: var(--muted) !important;
+      }
+      /* single-file selector: a segmented control, exactly one active */
+      .seg {
+        display: inline-flex;
+        gap: 4px;
+        flex-wrap: wrap;
+        margin-bottom: 12px;
+      }
+      .seg .opt {
+        padding: 4px 11px;
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 12.5px;
+        cursor: pointer;
+        user-select: none;
+      }
+      .seg .opt.on {
+        background: var(--accent-soft);
+        color: var(--accent);
+        border-color: transparent;
+        font-weight: 600;
+      }
+      .clbl {
+        font-size: 11px;
+        color: var(--ink2);
+        font-weight: 600;
+      }
+      /* info button + popover */
+      .ibtn {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 15px;
+        height: 15px;
+        border-radius: 50%;
+        border: 1px solid var(--line);
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 10px;
+        font-weight: 700;
+        line-height: 1;
+        cursor: pointer;
+        margin-left: 6px;
+        padding: 0;
+        vertical-align: 1px;
+      }
+      .ibtn:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .ipop {
+        position: fixed;
+        z-index: 20;
+        max-width: 320px;
+        background: var(--panel);
+        color: var(--ink);
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        padding: 9px 11px;
+        font-size: 12px;
+        line-height: 1.45;
+        box-shadow: 0 8px 26px rgba(0, 0, 0, 0.18);
+      }
+      .empty {
+        color: var(--muted);
+        font-size: 12.5px;
+        padding: 22px 0;
+        text-align: center;
+      }
+      /* the correlation matrix is roughly square; left to fill the column it would tower over the page,
+         so it is capped and centred */
+      #c-corr {
+        max-width: 560px;
+        margin: 0 auto;
+      }
+      /* section-3 heading + file selector pin to the top while its panels scroll past, so the reader can
+         always see (and change) which file is driving them */
+      .stick {
+        position: sticky;
+        top: 0;
+        z-index: 6;
+        background: var(--bg);
+        padding-bottom: 8px;
+        margin-bottom: 4px;
+        box-shadow: 0 6px 10px -8px rgba(0, 0, 0, 0.35);
+      }
+      .stick .sechead {
+        margin-top: 22px;
+      }
+      .stick .secsub {
+        margin-bottom: 8px;
+      }
+      /* lower-triangle scatter cells: clickable, and outlined on hover / while open to signal it */
+      #c-corr rect.scell {
+        cursor: pointer;
+      }
+      #c-corr rect.scell:hover,
+      #c-corr rect.scell.hot {
+        stroke: var(--accent);
+        stroke-width: 1.5;
+      }
+      /* floating card holding the enlarged pairwise scatter */
+      #corr-zoom {
+        position: fixed;
+        left: 50%;
+        top: 50%;
+        transform: translate(-50%, -50%);
+        z-index: 40;
+        display: none;
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 12px;
+        box-shadow: 0 12px 40px rgba(0, 0, 0, 0.35);
+        padding: 12px 14px;
+      }
+      #corr-zoom.on {
+        display: block;
+      }
+      #corr-zoom .cz-head {
+        display: flex;
+        align-items: center;
+        gap: 10px;
+        font-size: 12px;
+        color: var(--ink2);
+        margin-bottom: 6px;
+      }
+      #corr-zoom .cz-x {
+        margin-left: auto;
+        border: none;
+        background: transparent;
+        color: var(--ink2);
+        font-size: 18px;
+        line-height: 1;
+        cursor: pointer;
+      }
+    </style>
+  </head>
+  <body>
+    <div class="wrap">
+      <header>
+        <h1>Sequencing-error correction</h1>
+        <span class="sub">sample <b id="h-sample"></b></span>
+        <span class="badge" id="h-method"></span>
+      </header>
+      <p class="lead" id="h-lead"></p>
+
+      <div class="tiles" id="tiles"></div>
+
+      <!-- ============ SECTION 1 · comparison across files ============ -->
+      <div class="sechead">
+        <span class="n">1 · COMPARE</span>
+        <h2>
+          Reproducibility of the error signature
+          <button
+            class="ibtn"
+            type="button"
+            data-info="Upper triangle: Pearson r between two files of the per-variant removed error frequency (raw − corrected counts-per-coverage) over shared SNVs. Lower triangle: the same pair as a scatter — click to enlarge, hover a point for its variant."
+          >
+            i
+          </button>
+        </h2>
+      </div>
+      <p class="secsub">
+        Where errors recur. The upper triangle scores how strongly two files agree on which variants are error-prone;
+        the lower triangle shows each pair as a scatter (click to enlarge). Higher agreement means the correction
+        reflects sequencing chemistry rather than chance.
+      </p>
+      <div class="panel">
+        <div id="c-corr"></div>
+      </div>
+
+      <!-- ============ SECTION 2 · raw vs corrected, input / output ============ -->
+      <div class="sechead">
+        <span class="n">2 · SELECTION</span>
+        <h2>
+          Raw versus corrected frequency
+          <button
+            class="ibtn"
+            type="button"
+            data-info="Per-SNV raw vs corrected frequency (counts ÷ coverage); dashed y = x marks no change. The 'removed' lane holds variants floored to zero, which the log axis cannot place. Error bars span the spread across selected files."
+          >
+            i
+          </button>
+        </h2>
+      </div>
+      <p class="secsub">
+        Effect of correction on measured variants, split by input and output libraries. Toggle files; with two or more,
+        the bars span their spread.
+      </p>
+      <div class="grid2" id="scatter-groups"></div>
+
+      <!-- ============ SECTION 3 · one file at a time ============ -->
+      <!-- header + file selector stay pinned while scrolling this section, so it is always clear which
+           file the panels below are showing -->
+      <div class="stick">
+        <div class="sechead">
+          <span class="n">3 · DETAIL</span>
+          <h2>
+            One sequencing file in detail
+            <button
+              class="ibtn"
+              type="button"
+              data-info="Every panel in this section is computed from the file chosen in the selector."
+            >
+              i
+            </button>
+          </h2>
+        </div>
+        <p class="secsub">Choose one file; every panel below recomputes for it alone.</p>
+        <div class="seg" id="file-seg"></div>
+      </div>
+
+      <div class="panel" id="snv-panel">
+        <div class="legend" id="snv-legend"></div>
+        <div class="grid2">
+          <div>
+            <div class="sub2">
+              Sequencing-error frequency by substitution class
+              <button
+                class="ibtn"
+                type="button"
+                data-info="Per-SNV removed error frequency (raw − corrected counts-per-coverage ≈ per-nt error rate) in the selected file, by strand-symmetric substitution class; tick = class median."
+              >
+                i
+              </button>
+            </div>
+            <div id="c-bee"></div>
+          </div>
+          <div>
+            <div class="sub2">
+              Correction magnitude
+              <button
+                class="ibtn"
+                type="button"
+                data-info="Percentage of each SNV's counts removed in the selected file; untouched variants are omitted."
+              >
+                i
+              </button>
+            </div>
+            <div id="c-hist"></div>
+          </div>
+        </div>
+      </div>
+
+      <div class="panel">
+        <h3>
+          Where errors concentrate along the ORF
+          <button
+            class="ibtn"
+            type="button"
+            data-info="Fraction of counts removed per residue along the reading frame, selected file."
+          >
+            i
+          </button>
+        </h3>
+        <p class="desc">Fraction of counts removed at each residue position.</p>
+        <div id="c-bias"></div>
+      </div>
+
+      <div class="panel">
+        <h3>
+          Single-nucleotide substitutions by position
+          <button
+            class="ibtn"
+            type="button"
+            data-info="One row per SNV of the selected file, in reference (base_mut) coordinates; frequencies are counts ÷ coverage."
+          >
+            i
+          </button>
+        </h3>
+        <div class="controls">
+          <input
+            type="search"
+            id="snvq"
+            placeholder="Filter (e.g. K181M, 893, T&gt;A)…"
+            aria-label="Filter single-nucleotide substitutions"
+          />
+          <span class="sub" id="snvcount"></span>
+        </div>
+        <div class="tblwrap">
+          <table id="snvtbl">
+            <thead>
+              <tr>
+                <th data-k="ntpos">Position</th>
+                <th data-k="nt">Substitution</th>
+                <th data-k="pos_mut">Residue change</th>
+                <th data-k="cls">Class</th>
+                <th data-k="f_raw">Raw freq</th>
+                <th data-k="f_cor">Corrected freq</th>
+                <th data-k="pct">% change</th>
+              </tr>
+            </thead>
+            <tbody></tbody>
+          </table>
+        </div>
+      </div>
+
+      <div class="foot">nf-core/deepmutscan · sequencing-error correction report</div>
+    </div>
+
+    <div class="tt" id="tt"></div>
+    <div id="corr-zoom"></div>
+
+    <script id="ec-data" type="application/json">
+      __EC_DATA__
+    </script>
+    <script>
+      const DATA = JSON.parse(document.getElementById("ec-data").textContent);
+      const FILES = (DATA.files || []).map((f) => (typeof f === "string" ? { id: f } : f));
+      const V = DATA.variants || [];
+      const CLASSES = DATA.classes || [];
+      const V_BY_KEY = {}; // variant lookup for the correlation-matrix scatter point annotations
+      for (const v of V) V_BY_KEY[v.key] = v;
+      let R_LAST = null; // last correlation matrix (with its keyed pairs), for openScatter
+      const tt = document.getElementById("tt");
+      const fmt = (x, d = 0) =>
+        x == null || isNaN(x) ? "–" : Number(x).toLocaleString(undefined, { maximumFractionDigits: d });
+      const sci = (x) => (x == null || isNaN(x) ? "–" : Number(x).toExponential(2));
+      const esc = (s) => String(s).replace(/&/g, "&amp;").replace(/</g, "&lt;").replace(/"/g, "&quot;");
+      const showTT = (e, html) => {
+        tt.innerHTML = html;
+        tt.style.opacity = 1;
+        tt.style.left = Math.min(e.clientX + 12, innerWidth - tt.offsetWidth - 8) + "px";
+        tt.style.top = e.clientY - tt.offsetHeight - 10 + "px";
+      };
+      const hideTT = () => (tt.style.opacity = 0);
+      const SVGNS = "http://www.w3.org/2000/svg";
+      const el = (n, a = {}) => {
+        const e = document.createElementNS(SVGNS, n);
+        for (const k in a) e.setAttribute(k, a[k]);
+        return e;
+      };
+      const clsColor = (k) => `var(--c${CLASSES.findIndex((c) => c.cls === k) + 1})`;
+      const clsLabel = (k) => (CLASSES.find((c) => c.cls === k) || {}).label || k;
+
+      // One tooltip handler per chart instead of two listeners on each of the thousands of points.
+      // The tip text rides on the element as data-tip, so a single delegated mousemove serves the
+      // whole SVG — far lighter to build and to repaint than per-marker listeners.
+      function tipDelegate(svg) {
+        svg.addEventListener("mousemove", (e) => {
+          const t = e.target.getAttribute && e.target.getAttribute("data-tip");
+          if (t) showTT(e, t);
+          else hideTT();
+        });
+        svg.addEventListener("mouseleave", hideTT);
+      }
+
+      // Short, distinct file labels: strip the prefix and suffix every id shares, so a 6×6 matrix and
+      // the selectors stay readable ("input_1" rather than "GID1_input_1_pe").
+      function shortLabels(ids) {
+        if (ids.length < 2) return ids.slice();
+        let pre = ids[0],
+          suf = ids[0];
+        for (const s of ids) {
+          while (!s.startsWith(pre)) pre = pre.slice(0, -1);
+          while (!s.endsWith(suf)) suf = suf.slice(1);
+        }
+        const a = pre.length,
+          b = suf.length;
+        return ids.map((s) => {
+          const t = s.slice(a, s.length - b).replace(/^[_.-]+|[_.-]+$/g, "");
+          return t || s;
+        });
+      }
+      const SHORT = shortLabels(FILES.map((f) => f.id));
+
+      document.getElementById("h-sample").textContent = DATA.sample;
+      const estimatorLabel = { mle: "maximum-likelihood estimation", eb: "empirical-Bayes estimation" }[DATA.estimator];
+      const methodLabel =
+        DATA.method === "false_doubles"
+          ? `False-doubles correction${estimatorLabel ? ` (${estimatorLabel})` : ""}`
+          : DATA.method === "wildtype"
+            ? "Wildtype-based correction"
+            : DATA.method;
+      document.getElementById("h-method").textContent = methodLabel;
+      document.getElementById("h-lead").textContent =
+        `How ${methodLabel.toLowerCase()} estimated the sequencing-error background in single-nucleotide counts and ` +
+        `what it removed, across ${FILES.length} file${FILES.length === 1 ? "" : "s"} of sample ${DATA.sample}.`;
+
+      /* ---------- per-file, per-variant quantities ---------- */
+      // The error frequency the correction removed from a variant in one file. Equal to the estimated
+      // per-nucleotide error rate at that site (coverage cancels): counts and coverage are what the
+      // report already carries, so nothing extra has to be threaded through the pipeline for this.
+      const removed = (v, fi) => (v.f_raw[fi] != null && v.f_cor[fi] != null ? v.f_raw[fi] - v.f_cor[fi] : null);
+
+      /* ---------- axis helpers ---------- */
+      function textW(svg, s, cls) {
+        const t = el("text", { class: cls, x: -9999, y: -9999 });
+        t.textContent = s;
+        svg.appendChild(t);
+        const w = t.getComputedTextLength();
+        t.remove();
+        return w;
+      }
+      const TICK_PX = 10,
+        LBL_PX = 11;
+
+      /* =========================================================
+         SECTION 1 — cross-file correlation of the error signature
+         ========================================================= */
+      function pearson(pairs) {
+        const n = pairs.length;
+        if (n < 3) return null;
+        let sx = 0,
+          sy = 0,
+          sxx = 0,
+          syy = 0,
+          sxy = 0;
+        for (const [a, b] of pairs) {
+          sx += a;
+          sy += b;
+          sxx += a * a;
+          syy += b * b;
+          sxy += a * b;
+        }
+        const cov = sxy - (sx * sy) / n;
+        const vx = sxx - (sx * sx) / n,
+          vy = syy - (sy * sy) / n;
+        if (vx <= 0 || vy <= 0) return null;
+        return cov / Math.sqrt(vx * vy);
+      }
+      // r -> colour on a sequential ramp anchored at the panel; the matrix is about agreement, so only
+      // the 0..1 range is coloured and anything negative (essentially never seen for a real signature)
+      // shares the low end.
+      function rColor(r) {
+        if (r == null) return "var(--grid)";
+        const t = Math.max(0, Math.min(1, r));
+        return `color-mix(in srgb, var(--accent) ${Math.round(t * 100)}%, var(--bg))`;
+      }
+      function drawCorr() {
+        const host = document.getElementById("c-corr");
+        host.innerHTML = "";
+        const N = FILES.length;
+        if (N < 2) {
+          host.innerHTML =
+            '<div class="empty">A single sequencing file — cross-file reproducibility needs at least two.</div>';
+          return;
+        }
+        // per file: map variant key -> removed error frequency (SNVs only)
+        const maps = FILES.map((_, fi) => {
+          const m = new Map();
+          for (const v of V) {
+            if (!v.cls) continue;
+            const e = removed(v, fi);
+            if (e != null) m.set(v.key, e);
+          }
+          return m;
+        });
+        const R = [];
+        for (let i = 0; i < N; i++) {
+          R.push([]);
+          for (let j = 0; j < N; j++) {
+            if (i === j) {
+              R[i].push({ r: 1, n: maps[i].size });
+              continue;
+            }
+            const pairs = [];
+            const a = maps[i],
+              b = maps[j];
+            const [small, other] = a.size < b.size ? [a, b] : [b, a];
+            // Keyed points are kept only for the lower triangle (i>j), which is where the scatterplots are
+            // drawn; x = column file (j), y = row file (i), so the thumbnail and its enlargement share a
+            // frame with the matrix.
+            const keyed = i > j ? [] : null;
+            for (const [k, va] of small) {
+              const vb = other.get(k);
+              if (vb == null) continue; // r is symmetric, so lane order is irrelevant
+              pairs.push([va, vb]);
+              if (keyed) keyed.push({ key: k, x: maps[j].get(k), y: maps[i].get(k) });
+            }
+            R[i].push({ r: pearson(pairs), n: pairs.length, pairs: keyed });
+          }
+        }
+        R_LAST = R;
+        closeScatter(); // a rebuild (e.g. theme change) invalidates any open cell
+        const off = Math.max(...SHORT.map((s) => s.length)) * 6 + 14;
+        const cell = Math.max(30, Math.min(64, Math.floor((980 - off) / N)));
+        const W = off + N * cell + 12,
+          H = off + N * cell + 34;
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        for (let i = 0; i < N; i++) {
+          const rl = el("text", { class: "tick", x: off - 6, y: off + i * cell + cell / 2 + 3, "text-anchor": "end" });
+          rl.textContent = SHORT[i];
+          svg.appendChild(rl);
+          const cl = el("text", {
+            class: "tick",
+            x: off + i * cell + cell / 2,
+            y: off - 6,
+            "text-anchor": "start",
+            transform: `rotate(-45 ${off + i * cell + cell / 2} ${off - 6})`,
+          });
+          cl.textContent = SHORT[i];
+          svg.appendChild(cl);
+        }
+        for (let i = 0; i < N; i++)
+          for (let j = 0; j < N; j++) {
+            const c = R[i][j];
+            const x = off + j * cell,
+              y = off + i * cell;
+            if (i > j) {
+              // lower triangle: a scatter thumbnail of the two files' removed-error frequencies. Clickable
+              // (hover outline hints at it); click enlarges it (openScatter), where points annotate on hover.
+              svg.appendChild(
+                el("rect", {
+                  x: x + 1,
+                  y: y + 1,
+                  width: cell - 2,
+                  height: cell - 2,
+                  rx: 4,
+                  fill: "var(--bg)",
+                  stroke: "var(--line)",
+                  "stroke-width": 0.5,
+                }),
+              );
+              if (c.pairs && c.pairs.length >= 3) miniScatter(svg, c.pairs, x + 3, y + 3, cell - 6, rColor(c.r));
+              const hit = el("rect", {
+                x,
+                y,
+                width: cell,
+                height: cell,
+                fill: "transparent",
+                class: "scell",
+                "data-ij": i + "," + j,
+              });
+              hit.addEventListener("mousemove", (e) =>
+                showTT(
+                  e,
+                  `<b>${esc(SHORT[i])} × ${esc(SHORT[j])}</b><br>` +
+                    (c.r == null ? "too few shared variants" : `r = ${c.r.toFixed(3)} · ${fmt(c.n)} shared variants`) +
+                    (c.pairs && c.pairs.length >= 3 ? "<br><span style='opacity:.7'>click to enlarge</span>" : ""),
+                ),
+              );
+              hit.addEventListener("mouseleave", hideTT);
+              if (c.pairs && c.pairs.length >= 3) hit.addEventListener("click", () => openScatter(i, j));
+              svg.appendChild(hit);
+              continue;
+            }
+            const rect = el("rect", {
+              x: x + 1,
+              y: y + 1,
+              width: cell - 2,
+              height: cell - 2,
+              rx: 4,
+              fill: rColor(c.r),
+              stroke: "var(--line)",
+              "stroke-width": 0.5,
+            });
+            svg.appendChild(rect);
+            if (c.r != null && cell >= 34) {
+              const t = el("text", {
+                x: x + cell / 2,
+                y: y + cell / 2 + 3,
+                "text-anchor": "middle",
+                "font-size": 10,
+                "font-variant-numeric": "tabular-nums",
+                fill: c.r > 0.55 ? "var(--bg)" : "var(--ink2)",
+              });
+              t.textContent = i === j ? "—" : c.r.toFixed(2);
+              svg.appendChild(t);
+            }
+            const hit = el("rect", { x, y, width: cell, height: cell, fill: "transparent" });
+            hit.addEventListener("mousemove", (e) =>
+              showTT(
+                e,
+                `<b>${esc(SHORT[i])} × ${esc(SHORT[j])}</b><br>` +
+                  (i === j
+                    ? `${fmt(c.n)} variants`
+                    : c.r == null
+                      ? "too few shared variants"
+                      : `r = ${c.r.toFixed(3)} · ${fmt(c.n)} shared variants`),
+              ),
+            );
+            hit.addEventListener("mouseleave", hideTT);
+            svg.appendChild(hit);
+          }
+        // colour key
+        const kx = off,
+          ky = off + N * cell + 18,
+          kw = Math.min(180, N * cell);
+        for (let s = 0; s < kw; s++) {
+          svg.appendChild(el("rect", { x: kx + s, y: ky, width: 1, height: 8, fill: rColor(s / kw) }));
+        }
+        ["0", "0.5", "1"].forEach((lab, i) => {
+          const t = el("text", {
+            class: "tick",
+            x: kx + (kw * i) / 2,
+            y: ky + 20,
+            "text-anchor": i === 0 ? "start" : i === 2 ? "end" : "middle",
+          });
+          t.textContent = lab;
+          svg.appendChild(t);
+        });
+        const kl = el("text", { class: "tick", x: kx + kw + 10, y: ky + 8, "text-anchor": "start" });
+        kl.textContent = "correlation (r)";
+        svg.appendChild(kl);
+      }
+
+      // A shared log scale for a set of {x,y} error-frequency pairs (both axes use it, so y=x reads true).
+      function pairScale(pairs) {
+        const vals = pairs.flatMap((p) => [p.x, p.y]).filter((v) => v > 0);
+        const lo = vals.length ? Math.min(...vals) : 1e-6;
+        const hi = vals.length ? Math.max(...vals) : 1;
+        const l10 = (v) => Math.log10(Math.max(v, lo / 2));
+        const span = l10(hi) - l10(lo) || 1;
+        // Normalised 0..1 position, CLAMPED: a variant the correction left alone removes 0, which on a log
+        // axis falls below the floor and would otherwise plot at a negative coordinate - outside the cell.
+        // Clamping pins those to the bottom-left edge instead of letting the cloud spill past its box.
+        const nrm = (v) => Math.max(0, Math.min(1, (l10(v) - l10(lo)) / span));
+        return { lo, hi, l10, span, nrm };
+      }
+
+      // Thumbnail scatter inside a matrix cell: just the dot cloud, no axes.
+      function miniScatter(svg, pairs, x0, y0, size, col) {
+        const s = pairScale(pairs);
+        const px = (v) => x0 + s.nrm(v) * size;
+        const py = (v) => y0 + size - s.nrm(v) * size;
+        for (const p of pairs) {
+          svg.appendChild(
+            el("circle", { cx: px(p.x), cy: py(p.y), r: 0.7, fill: col || "var(--accent)", "fill-opacity": 0.5 }),
+          );
+        }
+      }
+
+      // The enlarged, hoverable scatter shown on click. Lives in a floating card (#corr-zoom).
+      let corrOpen = null; // "i,j" of the open cell, or null
+      function openScatter(i, j) {
+        const zoom = document.getElementById("corr-zoom");
+        const cell = R_LAST[i][j];
+        if (!cell || !cell.pairs) return;
+        corrOpen = i + "," + j;
+        const S = 360,
+          m = { l: 60, r: 12, t: 12, b: 46 };
+        const iw = S - m.l - m.r,
+          ih = S - m.t - m.b;
+        const sc = pairScale(cell.pairs);
+        const px = (v) => m.l + sc.nrm(v) * iw;
+        const py = (v) => m.t + ih - sc.nrm(v) * ih;
+        const svg = el("svg", { viewBox: `0 0 ${S} ${S}`, width: S, height: S });
+        tipDelegate(svg);
+        // y = x reference
+        svg.appendChild(
+          el("line", {
+            x1: px(sc.lo),
+            y1: py(sc.lo),
+            x2: px(sc.hi),
+            y2: py(sc.hi),
+            stroke: "var(--ink2)",
+            "stroke-dasharray": "4 4",
+            "stroke-width": 1,
+          }),
+        );
+        [0, 1, 2, 3].forEach((k) => {
+          const gv = Math.pow(10, sc.l10(sc.lo) + (sc.span * k) / 3);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: py(gv), y2: py(gv) }));
+          const ty = el("text", { class: "tick", x: m.l - 6, y: py(gv) + 3, "text-anchor": "end" });
+          ty.textContent = gv.toExponential(0);
+          svg.appendChild(ty);
+          const tx = el("text", { class: "tick", x: px(gv), y: m.t + ih + TICK_PX + 4, "text-anchor": "middle" });
+          tx.textContent = gv.toExponential(0);
+          svg.appendChild(tx);
+        });
+        for (const p of cell.pairs) {
+          const vv = V_BY_KEY[p.key];
+          svg.appendChild(
+            el("circle", {
+              cx: px(p.x),
+              cy: py(p.y),
+              r: 2.2,
+              fill: "var(--accent)", // stay blue like the thumbnail; class colour is not the point here
+              "fill-opacity": 0.7,
+              "data-tip": `<b>${esc(vv ? vv.pos_mut : p.key)}</b>${vv ? ` · <code>${esc(vv.nt)}</code>` : ""}<br>${esc(SHORT[j])} ${sci(p.x)}<br>${esc(SHORT[i])} ${sci(p.y)}`,
+            }),
+          );
+        }
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: S - 6, "text-anchor": "middle" });
+        xl.textContent = SHORT[j] + " — removed error frequency";
+        svg.appendChild(xl);
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: 14,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = SHORT[i] + " — removed error frequency";
+        svg.appendChild(yl);
+        zoom.innerHTML = `<div class="cz-head">${esc(SHORT[i])} × ${esc(SHORT[j])} · r = ${cell.r == null ? "–" : cell.r.toFixed(3)} · ${fmt(cell.pairs.length)} shared variants<button class="cz-x" type="button" aria-label="close">×</button></div>`;
+        zoom.appendChild(svg);
+        zoom.classList.add("on");
+        zoom.querySelector(".cz-x").addEventListener("click", closeScatter);
+        // reflect the open cell in the matrix
+        document
+          .querySelectorAll("#c-corr .scell")
+          .forEach((r) => r.classList.toggle("hot", r.dataset.ij === corrOpen));
+      }
+      function closeScatter() {
+        const zoom = document.getElementById("corr-zoom");
+        if (zoom) {
+          zoom.classList.remove("on");
+          zoom.innerHTML = "";
+        }
+        corrOpen = null;
+        document.querySelectorAll("#c-corr .scell.hot").forEach((r) => r.classList.remove("hot"));
+      }
+      // Dismiss: a click that is neither inside the card nor on a scatter cell (clicking another cell just
+      // re-opens via its own handler), and whenever the matrix scrolls out of view.
+      document.addEventListener("click", (e) => {
+        if (!corrOpen) return;
+        if (e.target.closest("#corr-zoom") || e.target.closest(".scell")) return;
+        closeScatter();
+      });
+
+      /* =========================================================
+         SECTION 2 — raw vs corrected, split by library type
+         ========================================================= */
+      // one independent selection set per type group
+      const groupSel = {};
+      function typeGroups() {
+        const order = ["input", "output", "quality"];
+        const present = [];
+        for (const t of order) {
+          const idx = FILES.map((f, i) => i).filter((i) => (FILES[i].type || "") === t);
+          if (idx.length) present.push({ type: t, idx });
+        }
+        // any unforeseen type still gets a panel rather than vanishing
+        const known = new Set(order);
+        const rest = FILES.map((f, i) => i).filter((i) => !known.has(FILES[i].type || ""));
+        if (rest.length) present.push({ type: FILES[rest[0]].type || "other", idx: rest });
+        return present;
+      }
+      const GROUPS = typeGroups();
+      GROUPS.forEach((g) => (groupSel[g.type] = new Set(g.idx)));
+
+      function groupTitle(t) {
+        return (
+          { input: "Input libraries", output: "Output libraries", quality: "Quality controls" }[t] || t + " libraries"
+        );
+      }
+
+      function buildGroups() {
+        const host = document.getElementById("scatter-groups");
+        host.innerHTML = "";
+        GROUPS.forEach((g) => {
+          const box = document.createElement("div");
+          box.innerHTML =
+            `<div class="sub2">${esc(groupTitle(g.type))}</div>` +
+            `<div class="row" data-grp="${esc(g.type)}">` +
+            g.idx
+              .map(
+                (i) =>
+                  `<span class="chip${groupSel[g.type].has(i) ? "" : " off"}" data-f="${i}">${esc(SHORT[i])}</span>`,
+              )
+              .join("") +
+            `</div><div id="scat-${esc(g.type)}"></div>`;
+          host.appendChild(box);
+        });
+      }
+      document.getElementById("scatter-groups").addEventListener("click", (e) => {
+        const c = e.target.closest("[data-f]");
+        if (!c) return;
+        const grp = c.closest("[data-grp]").dataset.grp;
+        const i = +c.dataset.f;
+        const set = groupSel[grp];
+        if (set.has(i)) set.delete(i);
+        else set.add(i);
+        if (!set.size) GROUPS.find((g) => g.type === grp).idx.forEach((k) => set.add(k));
+        buildGroups();
+        GROUPS.forEach((g) => drawScatter(g));
+      });
+
+      // mean and sample-SD of a per-file quantity over the selected files of a group
+      function aggSel(v, field, set) {
+        const vals = [];
+        for (const i of set) {
+          const x = v[field][i];
+          if (x != null && !isNaN(x)) vals.push(x);
+        }
+        if (!vals.length) return null;
+        const mean = vals.reduce((a, b) => a + b, 0) / vals.length;
+        let sd = null;
+        if (vals.length > 1) {
+          let ss = 0;
+          for (const x of vals) ss += (x - mean) * (x - mean);
+          sd = Math.sqrt(ss / (vals.length - 1));
+        }
+        return { mean, sd, n: vals.length };
+      }
+
+      function drawScatter(g) {
+        const host = document.getElementById("scat-" + g.type);
+        host.innerHTML = "";
+        const set = groupSel[g.type];
+        const pts = [];
+        for (const v of V) {
+          if (!v.cls) continue;
+          const fr = aggSel(v, "f_raw", set);
+          if (!fr || !(fr.mean > 0)) continue;
+          const fc = aggSel(v, "f_cor", set);
+          pts.push({ v, fr: fr.mean, frsd: fr.sd, fc: fc ? fc.mean : 0, fcsd: fc ? fc.sd : null, n: fr.n });
+        }
+        if (!pts.length) {
+          host.innerHTML = '<div class="empty">No variants for this selection.</div>';
+          return;
+        }
+        const W = 500,
+          H = 260,
+          m = { l: 0, r: 10, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        tipDelegate(svg);
+        const xs = pts.flatMap((p) => [p.fr, p.frsd ? p.fr + p.frsd : p.fr]);
+        const ys = pts.filter((p) => p.fc > 0).flatMap((p) => [p.fc, p.fcsd ? p.fc + p.fcsd : p.fc]);
+        const lo = Math.min(...xs, ...(ys.length ? ys : xs)),
+          hi = Math.max(...xs, ...(ys.length ? ys : xs));
+        const l10 = (v) => Math.log10(Math.max(v, lo / 2));
+        const span = l10(hi) - l10(lo) || 1;
+        const labels = [0, 1, 2, 3].map((i) => Math.pow(10, l10(lo) + (span * i) / 3).toExponential(0));
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...labels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 14;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const lane = 16;
+        const x = (v) => m.l + ((l10(v) - l10(lo)) / span) * iw;
+        const y = (v) => m.t + (ih - lane) - ((l10(v) - l10(lo)) / span) * (ih - lane);
+        const yZero = m.t + ih - lane / 2;
+        [0, 1, 2, 3].forEach((i) => {
+          const gv = Math.pow(10, l10(lo) + (span * i) / 3);
+          const gy = y(gv);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = gv.toExponential(0);
+          svg.appendChild(t);
+          const tx = el("text", { class: "tick", x: x(gv), y: m.t + ih + TICK_PX + 4, "text-anchor": "middle" });
+          tx.textContent = gv.toExponential(0);
+          svg.appendChild(tx);
+        });
+        svg.appendChild(
+          el("line", { x1: m.l, x2: m.l + iw, y1: yZero, y2: yZero, stroke: "var(--line)", "stroke-dasharray": "3 3" }),
+        );
+        const lz = el("text", { class: "tick", x: m.l - 8, y: yZero + 3, "text-anchor": "end" });
+        lz.textContent = "removed";
+        svg.appendChild(lz);
+        svg.appendChild(
+          el("line", {
+            x1: x(lo),
+            y1: y(lo),
+            x2: x(hi),
+            y2: y(hi),
+            stroke: "var(--ink2)",
+            "stroke-dasharray": "4 4",
+            "stroke-width": 1,
+          }),
+        );
+        pts.forEach((p) => {
+          const px = x(p.fr),
+            py = p.fc > 0 ? y(p.fc) : yZero;
+          const col = clsColor(p.v.cls);
+          if (p.frsd != null) {
+            const x0 = Math.max(p.fr - p.frsd, lo / 2);
+            svg.appendChild(
+              el("line", {
+                x1: x(x0),
+                x2: x(p.fr + p.frsd),
+                y1: py,
+                y2: py,
+                stroke: col,
+                "stroke-width": 1,
+                "stroke-opacity": 0.35,
+              }),
+            );
+          }
+          // vertical (corrected-frequency) spread bar, mirroring the horizontal raw one
+          if (p.fc > 0 && p.fcsd != null) {
+            svg.appendChild(
+              el("line", {
+                x1: px,
+                x2: px,
+                y1: y(Math.max(p.fc - p.fcsd, lo / 2)),
+                y2: y(p.fc + p.fcsd),
+                stroke: col,
+                "stroke-width": 1,
+                "stroke-opacity": 0.35,
+              }),
+            );
+          }
+          const pct = p.fr > 0 ? ((p.fc - p.fr) / p.fr) * 100 : null;
+          const c = el("circle", {
+            cx: px,
+            cy: py,
+            r: 2,
+            fill: col,
+            "fill-opacity": 0.7,
+            "data-tip":
+              `<b>${esc(p.v.pos_mut)}</b> · <code>${esc(p.v.nt)}</code><br>raw ${sci(p.fr)}` +
+              (p.frsd != null ? ` ± ${sci(p.frsd)} (n=${p.n})` : ` (n=${p.n})`) +
+              `<br>corrected ${sci(p.fc)}` +
+              (p.fcsd != null ? ` ± ${sci(p.fcsd)}` : "") +
+              (pct == null ? "" : `<br>${fmt(pct, 1)} % change`),
+          });
+          svg.appendChild(c);
+        });
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Corrected frequency";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "Raw frequency";
+        svg.appendChild(xl);
+      }
+
+      /* =========================================================
+         SECTION 3 — single file: bias, beeswarm, magnitude, table
+         ========================================================= */
+      let curFile = 0;
+      let clsFilter = null;
+
+      function buildSeg() {
+        document.getElementById("file-seg").innerHTML = FILES.map(
+          (f, i) => `<span class="opt${i === curFile ? " on" : ""}" data-i="${i}">${esc(SHORT[i])}</span>`,
+        ).join("");
+      }
+      document.getElementById("file-seg").addEventListener("click", (e) => {
+        const o = e.target.closest("[data-i]");
+        if (!o) return;
+        curFile = +o.dataset.i;
+        buildSeg();
+        drawDetail();
+      });
+
+      function drawLegend() {
+        document.getElementById("snv-legend").innerHTML = CLASSES.map(
+          (c, i) =>
+            `<span class="chip${clsFilter && clsFilter !== c.cls ? " off" : ""}" data-c="${esc(c.cls)}">` +
+            `<span class="sw" style="background:var(--c${i + 1})"></span>${esc(c.label)}</span>`,
+        ).join("");
+      }
+      document.getElementById("snv-legend").addEventListener("click", (e) => {
+        const c = e.target.closest("[data-c]");
+        if (!c) return;
+        clsFilter = clsFilter === c.dataset.c ? null : c.dataset.c;
+        drawLegend();
+        drawBee();
+        drawTable();
+      });
+
+      /* --- positional error bias, single file --- */
+      function drawBias() {
+        const host = document.getElementById("c-bias");
+        host.innerHTML = "";
+        const by = new Map();
+        for (const v of V) {
+          if (v.pos == null) continue;
+          const r = v.raw[curFile],
+            c = v.cor[curFile];
+          if (r == null) continue;
+          const p = by.get(v.pos) || { raw: 0, cor: 0 };
+          p.raw += r;
+          p.cor += c == null ? 0 : c;
+          by.set(v.pos, p);
+        }
+        const P = [...by.entries()]
+          .map(([pos, d]) => ({ pos, frac: d.raw > 0 ? 1 - d.cor / d.raw : null }))
+          .filter((d) => d.frac != null)
+          .sort((a, b) => a.pos - b.pos);
+        if (!P.length) return (host.innerHTML = '<div class="empty">No corrected variants in this file.</div>');
+        const W = 1000,
+          H = 230,
+          m = { l: 0, r: 12, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        const maxP = Math.max(...P.map((d) => d.pos));
+        const maxY = Math.max(0.001, ...P.map((d) => d.frac));
+        const labels = [0, 1, 2, 3, 4].map((i) => (((maxY * i) / 4) * 100).toFixed(1) + "%");
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...labels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 8;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const x = (p) => m.l + ((p - 1) / Math.max(1, maxP - 1)) * iw;
+        const y = (v) => m.t + ih - (v / maxY) * ih;
+        labels.forEach((lab, i) => {
+          const gy = y((maxY * i) / 4);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = lab;
+          svg.appendChild(t);
+        });
+        const step = Math.max(1, Math.round(maxP / 10));
+        for (let p = 1; p <= maxP; p += step) {
+          const t = el("text", { class: "tick", x: x(p), y: m.t + ih + TICK_PX + 5, "text-anchor": "middle" });
+          t.textContent = p;
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Counts removed";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "ORF residue position";
+        svg.appendChild(xl);
+        let d = "";
+        P.forEach((p, i) => (d += (i ? " L " : "M ") + x(p.pos) + " " + y(p.frac)));
+        svg.appendChild(el("path", { d, fill: "none", stroke: "var(--c1)", "stroke-width": 1.5 }));
+        const ov = el("rect", { x: m.l, y: m.t, width: iw, height: ih, fill: "transparent" });
+        svg.appendChild(ov);
+        ov.addEventListener("mousemove", (e) => {
+          const r = svg.getBoundingClientRect(),
+            px = ((e.clientX - r.left) / r.width) * W;
+          const pp = Math.round(((px - m.l) / iw) * (maxP - 1)) + 1;
+          const d0 = P.find((z) => z.pos === pp);
+          if (!d0) return hideTT();
+          showTT(e, `<b>position ${pp}</b><br>${(d0.frac * 100).toFixed(2)} % of counts removed`);
+        });
+        ov.addEventListener("mouseleave", hideTT);
+      }
+
+      /* --- beeswarm: raw & corrected frequency per class, single file --- */
+      // A tidy beeswarm without a physics library. Points are sorted by y, then each is offset in x to
+      // the position nearest the lane centre where its marker does not overlap any already-placed one
+      // (true circle-to-circle distance, so no two dots touch). Nothing is clamped during placement, so
+      // there is never a forced overlap; if the resulting swarm is wider than the lane it is scaled back
+      // to fit, which only nudges points in the very densest classes. Items carry .yPix in, get .dx out.
+      // stable 0..1 hash of a string, for deterministic per-variant jitter
+      function jhash(s) {
+        let h = 0;
+        for (let i = 0; i < s.length; i++) h = (h * 31 + s.charCodeAt(i)) | 0;
+        return ((h >>> 0) % 1000) / 1000;
+      }
+      // Places every point at the nearest free x to its own lane centre (`p.base`), writing an ABSOLUTE
+      // x to `p.absX`. Both lanes of a class must go through one call: placing them separately lets a
+      // dense lane drift sideways into its neighbour's space and overlap it, since the natural swarm
+      // width is deliberately not clamped (compressing to fit a lane is what reintroduces overlaps).
+      function beeswarm(pts, r) {
+        const use = pts.filter((p) => p.yPix != null).sort((a, b) => a.yPix - b.yPix);
+        const placed = [];
+        const min2 = (2 * r + 0.5) * (2 * r + 0.5);
+        for (const p of use) {
+          let best = p.base;
+          for (let s = 0; s < 800; s++) {
+            const cand = p.base + (s === 0 ? 0 : (s % 2 ? 1 : -1) * Math.ceil(s / 2) * (r * 0.85));
+            const clash = placed.some((q) => {
+              const dx = q.absX - cand,
+                dy = q.yPix - p.yPix;
+              return dx * dx + dy * dy < min2;
+            });
+            if (!clash) {
+              best = cand;
+              break;
+            }
+          }
+          p.absX = best;
+          placed.push({ absX: best, yPix: p.yPix });
+        }
+        return pts;
+      }
+
+      function drawBee() {
+        const host = document.getElementById("c-bee");
+        host.innerHTML = "";
+        // One point per SNV per class = the error frequency the correction removed from that variant in
+        // the selected file (raw − corrected counts-per-coverage), i.e. the estimated per-nt error rate at
+        // that site. Untouched variants remove 0 and have no rate to place on a log axis, so only variants
+        // with a positive removed frequency are shown.
+        const perClass = CLASSES.map((c) => {
+          const pts = [];
+          for (const v of V) {
+            if (v.cls !== c.cls) continue;
+            const rmv = removed(v, curFile);
+            if (rmv != null && rmv > 0) pts.push({ v, y: rmv });
+          }
+          return { cls: c.cls, label: c.label, pts };
+        }).filter((s) => s.pts.length);
+        if (!perClass.length)
+          return (host.innerHTML = '<div class="empty">No single-nucleotide error was removed in this file.</div>');
+        const all = perClass.flatMap((s) => s.pts.map((p) => p.y));
+        const lo = Math.min(...all),
+          hi = Math.max(...all);
+        // taller than the other panels: more vertical room spreads the swarm in y, which is what keeps
+        // dense classes from having to overlap in x
+        const W = 500,
+          H = 360,
+          m = { l: 0, r: 10, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        tipDelegate(svg);
+        const l10 = (v) => Math.log10(Math.max(v, lo / 2));
+        const span = l10(hi) - l10(lo) || 1;
+        const labels = [0, 1, 2, 3].map((i) => Math.pow(10, l10(lo) + (span * i) / 3).toExponential(0));
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...labels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 16;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const y = (v) => m.t + ih - ((l10(v) - l10(lo)) / span) * ih;
+        [0, 1, 2, 3].forEach((i) => {
+          const gv = Math.pow(10, l10(lo) + (span * i) / 3);
+          const gy = y(gv);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = gv.toExponential(0);
+          svg.appendChild(t);
+        });
+        const bw = iw / perClass.length;
+        const med = (xs) => {
+          if (!xs.length) return null;
+          const s = [...xs].sort((a, b) => a - b);
+          const h = (s.length - 1) / 2;
+          return (s[Math.floor(h)] + s[Math.ceil(h)]) / 2;
+        };
+        // Frequencies are counts/coverage, so many variants land on nearly identical values; a small
+        // deterministic vertical jitter (stable per variant, recoverable on hover) breaks those ties so
+        // the swarm spreads into neighbouring y-space rather than stacking dots in x. Every point of every
+        // class is placed in ONE pass (per-class placement would let a dense swarm bleed into a neighbour).
+        const half = bw * 0.22;
+        const rr = 1.1;
+        const items = [];
+        perClass.forEach((s, i) => {
+          const cx = m.l + bw * (i + 0.5);
+          const dim = clsFilter && clsFilter !== s.cls;
+          s.cx = cx;
+          s.op = dim ? 0.14 : 1;
+          s.col = clsColor(s.cls);
+          s.pts.forEach((p) =>
+            items.push({
+              v: p.v,
+              y: p.y,
+              base: cx,
+              col: s.col,
+              op: s.op,
+              yPix: y(p.y) + (jhash(p.v.key) - 0.5) * 16,
+            }),
+          );
+        });
+        beeswarm(items, rr);
+        items.forEach((p) => {
+          svg.appendChild(
+            el("circle", {
+              cx: p.absX,
+              cy: p.yPix,
+              r: rr,
+              fill: p.col,
+              "fill-opacity": 0.65 * p.op,
+              "data-tip": `<b>${esc(p.v.pos_mut)}</b> · <code>${esc(p.v.nt)}</code><br>error frequency ${sci(p.y)}`,
+            }),
+          );
+        });
+        perClass.forEach((s) => {
+          const mm = med(s.pts.map((p) => p.y)); // class median error frequency
+          if (mm != null)
+            svg.appendChild(
+              el("line", {
+                x1: s.cx - half,
+                x2: s.cx + half,
+                y1: y(mm),
+                y2: y(mm),
+                stroke: s.col,
+                "stroke-width": 1.5,
+                "stroke-opacity": s.op,
+              }),
+            );
+          const t = el("text", {
+            class: "tick",
+            x: s.cx,
+            y: m.t + ih + TICK_PX + 4,
+            "text-anchor": "middle",
+            "fill-opacity": s.op,
+          });
+          t.textContent = s.cls;
+          svg.appendChild(t);
+        });
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Removed error frequency";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "substitution class";
+        svg.appendChild(xl);
+      }
+
+      /* --- correction magnitude, single file --- */
+      // Percentage of each single-nucleotide variant's counts removed by correction, 0–100 %. Correction
+      // only ever subtracts and floors at zero, so this is bounded. Untouched variants sit in the 0 % bar,
+      // so the panel shows at a glance how few variants were adjusted versus left alone.
+      function drawHist() {
+        const host = document.getElementById("c-hist");
+        host.innerHTML = "";
+        const pcts = [];
+        for (const v of V) {
+          if (!v.cls) continue;
+          const r = v.raw[curFile],
+            c = v.cor[curFile];
+          if (r == null || !(r > 0) || c == null) continue;
+          const pct = ((r - c) / r) * 100; // fraction of the raw counts removed, 0..100
+          if (pct > 0) pcts.push(Math.min(100, pct)); // adjusted variants only - untouched ones are excluded
+        }
+        if (!pcts.length) return (host.innerHTML = '<div class="empty">No variants were adjusted in this file.</div>');
+        const NB = 40,
+          bw = 100 / NB;
+        const bins = new Array(NB).fill(0);
+        pcts.forEach((p) => {
+          let k = Math.floor(p / bw);
+          if (k >= NB) k = NB - 1;
+          bins[k]++;
+        });
+        const W = 500,
+          H = 300,
+          m = { l: 0, r: 12, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        tipDelegate(svg);
+        const maxC = Math.max(...bins);
+        const ylabels = [0, 1, 2, 3, 4].map((i) => fmt(Math.round((maxC * i) / 4)));
+        m.l = Math.ceil(LBL_PX + 18 + Math.max(...ylabels.map((s) => textW(svg, s, "tick"))) + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 8;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const x = (p) => m.l + (p / 100) * iw;
+        const y = (c) => m.t + ih - (c / maxC) * ih;
+        ylabels.forEach((lab, i) => {
+          const gy = y((maxC * i) / 4);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = lab;
+          svg.appendChild(t);
+        });
+        bins.forEach((c, k) => {
+          if (!c) return;
+          const x0 = x(k * bw),
+            x1 = x((k + 1) * bw);
+          svg.appendChild(
+            el("rect", {
+              x: x0 + 0.4,
+              y: y(c),
+              width: Math.max(0.8, x1 - x0 - 0.8),
+              height: m.t + ih - y(c),
+              fill: "var(--c1)",
+              "fill-opacity": 0.85,
+              "data-tip": `<b>${fmt(k * bw, 0)} … ${fmt((k + 1) * bw, 0)} %</b><br>${fmt(c)} variants`,
+            }),
+          );
+        });
+        for (let i = 0; i <= 4; i++) {
+          const p = (100 * i) / 4;
+          const t = el("text", { class: "tick", x: x(p), y: m.t + ih + TICK_PX + 5, "text-anchor": "middle" });
+          t.textContent = p + " %";
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Variants";
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "% removed";
+        svg.appendChild(xl);
+      }
+
+      /* --- SNV table, single file --- */
+      let sortK = "ntpos",
+        sortAsc = true;
+      function drawTable() {
+        const q = document.getElementById("snvq").value.trim().toLowerCase();
+        let rows = [];
+        for (const v of V) {
+          if (!v.cls) continue;
+          if (clsFilter && v.cls !== clsFilter) continue;
+          const fr = v.f_raw[curFile],
+            fc = v.f_cor[curFile];
+          if (fr == null) continue;
+          const pct = fr > 0 && fc != null ? ((fc - fr) / fr) * 100 : null;
+          rows.push({ v, f_raw: fr, f_cor: fc, pct });
+        }
+        if (q)
+          rows = rows.filter(
+            (a) =>
+              a.v.pos_mut.toLowerCase().includes(q) ||
+              a.v.nt.toLowerCase().includes(q) ||
+              String(a.v.ntpos).includes(q) ||
+              (a.v.cls || "").toLowerCase().includes(q),
+          );
+        const get = (a) =>
+          ({
+            ntpos: a.v.ntpos,
+            nt: a.v.nt,
+            pos_mut: a.v.pos_mut,
+            cls: a.v.cls,
+            f_raw: a.f_raw,
+            f_cor: a.f_cor,
+            pct: a.pct,
+          })[sortK];
+        rows.sort((p, q2) => {
+          const A = get(p),
+            B = get(q2);
+          const c =
+            typeof A === "number" && typeof B === "number" ? A - B : String(A ?? "").localeCompare(String(B ?? ""));
+          return sortAsc ? c : -c;
+        });
+        document.getElementById("snvcount").textContent = `${fmt(rows.length)} substitutions`;
+        const tb = document.querySelector("#snvtbl tbody");
+        tb.innerHTML = rows
+          .slice(0, 3000)
+          .map(
+            (a) =>
+              `<tr><td>${a.v.ntpos ?? "–"}</td><td><code>${esc(a.v.nt)}</code></td><td>${esc(a.v.pos_mut)}</td>` +
+              `<td><span class="sw" style="background:${clsColor(a.v.cls)}"></span> ${esc(clsLabel(a.v.cls))}</td>` +
+              `<td>${sci(a.f_raw)}</td><td>${sci(a.f_cor)}</td><td>${a.pct == null ? "–" : fmt(a.pct, 1) + " %"}</td></tr>`,
+          )
+          .join("");
+      }
+      document.querySelectorAll("#snvtbl thead th").forEach((th) =>
+        th.addEventListener("click", () => {
+          const k = th.dataset.k;
+          if (sortK === k) sortAsc = !sortAsc;
+          else {
+            sortK = k;
+            sortAsc = true;
+          }
+          drawTable();
+        }),
+      );
+      document.getElementById("snvq").addEventListener("input", drawTable);
+
+      function drawDetail() {
+        drawBias();
+        drawBee();
+        drawHist();
+        drawTable();
+      }
+
+      /* ---------- run-level tiles (over all files) ---------- */
+      function tiles() {
+        let rawTot = 0,
+          corTot = 0,
+          adjusted = 0,
+          nSnv = 0;
+        for (const v of V) {
+          let anyR = false,
+            anyAdj = false;
+          for (let i = 0; i < FILES.length; i++) {
+            const r = v.raw[i],
+              c = v.cor[i];
+            if (r != null) {
+              rawTot += r;
+              anyR = true;
+              corTot += c == null ? 0 : c;
+              if (c != null && c !== r) anyAdj = true;
+            }
+          }
+          if (anyAdj) adjusted++;
+          if (v.cls && anyR) nSnv++;
+        }
+        const pct = rawTot > 0 ? ((rawTot - corTot) / rawTot) * 100 : 0;
+        const t = [
+          ["Sequencing files", fmt(FILES.length)],
+          ["Single-nucleotide variants", fmt(nSnv)],
+          [
+            `Variants adjusted (≥1 file) <button class="ibtn" type="button" data-info="Counted if adjusted in ≥1 file (a union), so larger than any single file; the DETAIL section shows per-file counts.">i</button>`,
+            fmt(adjusted),
+          ],
+          ["Counts removed", (pct >= 0 ? "" : "+") + fmt(pct, 1) + " %"],
+        ];
+        document.getElementById("tiles").innerHTML = t
+          .map(([k, v]) => `<div class="tile"><div class="k">${k}</div><div class="v">${v}</div></div>`)
+          .join("");
+      }
+
+      /* ---------- info buttons ---------- */
+      document.addEventListener("click", (e) => {
+        const prev = document.querySelector(".ipop");
+        if (prev) prev.remove();
+        const b = e.target.closest(".ibtn");
+        if (!b) return;
+        e.preventDefault();
+        e.stopPropagation();
+        const pop = document.createElement("div");
+        pop.className = "ipop";
+        pop.textContent = b.dataset.info;
+        document.body.appendChild(pop);
+        const r = b.getBoundingClientRect();
+        pop.style.left = Math.max(8, Math.min(r.left, innerWidth - pop.offsetWidth - 8)) + "px";
+        pop.style.top =
+          (r.bottom + 6 + pop.offsetHeight > innerHeight ? r.top - pop.offsetHeight - 6 : r.bottom + 6) + "px";
+      });
+
+      /* ---------- go ---------- */
+      // The comparison + selection panels (tiles, correlation matrix, the two scatters) render
+      // synchronously so the page is usable at once. The single-file detail section (positional bias,
+      // beeswarm, magnitude, table) is the heaviest work, so it is deferred to the next task tick - the
+      // page paints first, then the detail fills in - which noticeably cuts the perceived load time.
+      tiles();
+      drawCorr();
+      buildGroups();
+      GROUPS.forEach((g) => drawScatter(g));
+      buildSeg();
+      drawLegend();
+      setTimeout(drawDetail, 0);
+      addEventListener("resize", () => {
+        drawCorr();
+        GROUPS.forEach((g) => drawScatter(g));
+        drawDetail();
+      });
+      // Close the enlarged scatter once the matrix it belongs to has scrolled out of sight.
+      const _corrHost = document.getElementById("c-corr");
+      if (_corrHost && "IntersectionObserver" in window) {
+        new IntersectionObserver(
+          (entries) => {
+            if (corrOpen && entries.some((e) => !e.isIntersecting)) closeScatter();
+          },
+          { threshold: 0 },
+        ).observe(_corrHost);
+      }
+    </script>
+  </body>
+</html>
diff --git a/assets/multiqc_config.yml b/assets/multiqc_config.yml
index 5845dd0..bcfed63 100644
--- a/assets/multiqc_config.yml
+++ b/assets/multiqc_config.yml
@@ -1,7 +1,5 @@
 report_comment: >
-  This report has been generated by the <a href="https://github.com/nf-core/deepmutscan/releases/tag/1.0.0" target="_blank">nf-core/deepmutscan</a>
-  analysis pipeline. For information about how to interpret these results, please see the
-  <a href="https://nf-co.re/deepmutscan/1.0.0/docs/output" target="_blank">documentation</a>.
+  This report has been generated by the <a href="https://github.com/nf-core/deepmutscan/releases/tag/1.0.0" target="_blank">nf-core/deepmutscan</a> analysis pipeline. For information about how to interpret these results, please see the <a href="https://nf-co.re/deepmutscan/1.0.0/docs/output" target="_blank">documentation</a>.
 report_section_order:
   "nf-core-deepmutscan-methods-description":
     order: -1000
diff --git a/assets/samplesheet.csv b/assets/samplesheet.csv
index 5f653ab..5b5d503 100644
--- a/assets/samplesheet.csv
+++ b/assets/samplesheet.csv
@@ -1,3 +1,5 @@
-sample,fastq_1,fastq_2
-SAMPLE_PAIRED_END,/path/to/fastq/files/AEG588A1_S1_L002_R1_001.fastq.gz,/path/to/fastq/files/AEG588A1_S1_L002_R2_001.fastq.gz
-SAMPLE_SINGLE_END,/path/to/fastq/files/AEG588A4_S4_L003_R1_001.fastq.gz,
+sample,type,replicate,file1,file2
+ORF1,input,1,/reads/forward1.fastq.gz,/reads/reverse1.fastq.gz
+ORF1,input,2,/reads/forward2.fastq.gz,/reads/reverse2.fastq.gz
+ORF1,output,1,/reads/forward3.fastq.gz,/reads/reverse3.fastq.gz
+ORF1,output,2,/reads/forward4.fastq.gz,/reads/reverse4.fastq.gz
diff --git a/assets/schema_input.json b/assets/schema_input.json
index 656b88f..88afb3a 100644
--- a/assets/schema_input.json
+++ b/assets/schema_input.json
@@ -9,25 +9,46 @@
         "properties": {
             "sample": {
                 "type": "string",
-                "pattern": "^\\S+$",
-                "errorMessage": "Sample name must be provided and cannot contain spaces",
+                "pattern": "^[^\\s/]+$",
+                "errorMessage": "Sample name must be provided, cannot contain spaces, and must not include special characters",
                 "meta": ["id"]
             },
-            "fastq_1": {
+            "file1": {
                 "type": "string",
                 "format": "file-path",
                 "exists": true,
-                "pattern": "^([\\S\\s]*\\/)?[^\\s\\/]+\\.f(ast)?q\\.gz$",
-                "errorMessage": "FastQ file for reads 1 must be provided, cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'"
+                "pattern": "^\\S+\\.(bam|fastq|fq|fastq\\.gz|fq\\.gz)$",
+                "allOf": [
+                    {
+                        "if": {
+                            "pattern": "^\\S+\\.bam$"
+                        },
+                        "then": {
+                            "pattern": "^\\S+_(pe|se)\\.bam$",
+                            "errorMessage": "If file1 ends with .bam, it must contain '_pe.bam' or '_se.bam', defining paired-end or single-end"
+                        }
+                    }
+                ],
+                "errorMessage": "File 1 must be provided, cannot contain spaces, and must have an allowed extension (.bam, .fastq, .fq, .fastq.gz, .fq.gz)"
             },
-            "fastq_2": {
-                "type": "string",
+            "file2": {
+                "type": ["string", "null"],
                 "format": "file-path",
                 "exists": true,
-                "pattern": "^([\\S\\s]*\\/)?[^\\s\\/]+\\.f(ast)?q\\.gz$",
-                "errorMessage": "FastQ file for reads 2 cannot contain spaces and must have extension '.fq.gz' or '.fastq.gz'"
+                "pattern": "^\\S+\\.(fastq|fq|fastq\\.gz|fq\\.gz)$",
+                "errorMessage": "File 2 must have an allowed extension (.fastq, .fq, .fastq.gz, .fq.gz) or be null for single-end reads"
+            },
+            "type": {
+                "type": "string",
+                "enum": ["input", "output", "quality", "wildtype"],
+                "errorMessage": "Type must be one of: input, output, quality, or wildtype"
+            },
+            "replicate": {
+                "type": "integer",
+                "minimum": 1,
+                "errorMessage": "Replicate must be a positive integer"
             }
         },
-        "required": ["sample", "fastq_1"]
+        "required": ["sample", "file1", "type", "replicate"]
     }
 }
diff --git a/assets/summary_report/summary_report_template.html b/assets/summary_report/summary_report_template.html
new file mode 100644
index 0000000..16dbfb1
--- /dev/null
+++ b/assets/summary_report/summary_report_template.html
@@ -0,0 +1,1948 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>nf-core/deepmutscan · report · __SAMPLE__</title>
+    <style>
+      :root {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --na: #dddddd;
+        --ring: #ffffff;
+        --s1: #2a78d6;
+        --s2: #1baf7a;
+        --s3: #eda100;
+        --s4: #008300;
+        --s5: #4a3aa7;
+        --s6: #e34948;
+        --s7: #e87ba4;
+        --s8: #eb6834;
+      }
+      /* Light is the report's default: it is what gets printed, screenshotted and put in a paper, so
+         it must not depend on the reader's OS setting. Dark is opt-in through the header toggle only,
+         which is why there is deliberately no prefers-color-scheme rule here. */
+      :root[data-theme="dark"] {
+        --bg: #0f1219;
+        --panel: #161b24;
+        --ink: #e8ecf3;
+        --ink2: #a8b2c1;
+        --muted: #6b7688;
+        --line: #232a36;
+        --grid: #1c222d;
+        --accent: #5b90ee;
+        --accent-soft: #22304a;
+        --na: #2b323f;
+        --ring: #0f1219;
+        --s1: #3987e5;
+        --s2: #199e70;
+        --s3: #c98500;
+        --s4: #008300;
+        --s5: #9085e9;
+        --s6: #e66767;
+        --s7: #d55181;
+        --s8: #d95926;
+      }
+      :root[data-theme="light"] {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --ink: #1a1f2b;
+        --ink2: #5b6470;
+        --muted: #98a2b3;
+        --line: #e6e9ef;
+        --grid: #eef1f5;
+        --accent: #2f6bd8;
+        --accent-soft: #d7e2f7;
+        --na: #dddddd;
+        --ring: #ffffff;
+        --s1: #2a78d6;
+        --s2: #1baf7a;
+        --s3: #eda100;
+        --s4: #008300;
+        --s5: #4a3aa7;
+        --s6: #e34948;
+        --s7: #e87ba4;
+        --s8: #eb6834;
+      }
+      * {
+        box-sizing: border-box;
+      }
+      html,
+      body {
+        height: 100%;
+      }
+      body {
+        margin: 0;
+        background: var(--bg);
+        color: var(--ink);
+        font:
+          14px/1.5 -apple-system,
+          BlinkMacSystemFont,
+          "Segoe UI",
+          Roboto,
+          Helvetica,
+          Arial,
+          sans-serif;
+        -webkit-font-smoothing: antialiased;
+        display: flex;
+        flex-direction: column;
+      }
+      header {
+        display: flex;
+        align-items: center;
+        gap: 14px;
+        padding: 14px 22px;
+        border-bottom: 1px solid var(--line);
+        flex: none;
+      }
+      header .logo {
+        height: 50px;
+        width: auto;
+        display: block;
+      }
+      /* Two logo files ship with the pipeline: the wordmark is near-black in one and white in the
+         other. Swap them with the theme, or "deepmutscan" becomes unreadable on the dark background. */
+      header .logo-dark {
+        display: none;
+      }
+      :root[data-theme="dark"] header .logo-light {
+        display: none;
+      }
+      :root[data-theme="dark"] header .logo-dark {
+        display: block;
+      }
+      header .theme {
+        background: none;
+        border: 1px solid var(--line);
+        color: var(--ink2);
+        border-radius: 7px;
+        padding: 5px 9px;
+        cursor: pointer;
+        font: inherit;
+        font-size: 13px;
+        line-height: 1;
+      }
+      header .theme:hover {
+        border-color: var(--accent);
+        color: var(--accent);
+      }
+      header .title {
+        color: var(--muted);
+        font-size: 18px;
+        font-weight: 500;
+        border-left: 1px solid var(--line);
+        padding-left: 14px;
+      }
+      header .spacer {
+        flex: 1;
+      }
+      header .sub {
+        color: var(--ink2);
+        font-size: 13px;
+      }
+      .shell {
+        display: flex;
+        flex: 1;
+        min-height: 0;
+      }
+      nav {
+        width: 210px;
+        flex: none;
+        border-right: 1px solid var(--line);
+        padding: 14px 10px;
+        overflow-y: auto;
+      }
+      nav button {
+        display: block;
+        width: 100%;
+        text-align: left;
+        background: none;
+        border: 0;
+        border-radius: 8px;
+        padding: 8px 11px;
+        margin-bottom: 2px;
+        color: var(--ink2);
+        font-size: 13.5px;
+        cursor: pointer;
+        font-family: inherit;
+      }
+      nav button:hover {
+        background: var(--panel);
+        color: var(--ink);
+      }
+      nav button.on {
+        background: var(--accent-soft);
+        color: var(--accent);
+        font-weight: 600;
+      }
+      main {
+        flex: 1;
+        min-width: 0;
+        overflow-y: auto;
+        padding: 22px 26px 60px;
+      }
+      /* a page heading + selector that pins to the top while the panels below scroll (Library QC),
+         mirroring the error-correction report's per-file section */
+      .stick {
+        position: sticky;
+        top: -22px;
+        z-index: 6;
+        background: var(--bg);
+        padding: 22px 0 8px;
+        margin: -22px 0 10px;
+        box-shadow: 0 8px 10px -8px rgba(0, 0, 0, 0.3);
+      }
+      .stick h1 {
+        margin: 0 0 8px;
+      }
+      /* the Variant-effects structure spins on its own and is not meant to be grabbed - interaction
+         lives in the full inspection tool - so mouse events on its canvas are ignored */
+      #f-struct canvas {
+        pointer-events: none;
+      }
+      .sect {
+        display: none;
+        max-width: 1180px;
+      }
+      .sect.on {
+        display: block;
+      }
+      h1 {
+        font-size: 19px;
+        font-weight: 650;
+        margin: 0 0 4px;
+        letter-spacing: -0.01em;
+      }
+      .lead {
+        color: var(--ink2);
+        font-size: 13px;
+        margin: 0 0 18px;
+      }
+      .tiles {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+        gap: 12px;
+        margin-bottom: 22px;
+      }
+      .tile {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 12px;
+        padding: 14px 16px;
+      }
+      .tile .k {
+        color: var(--ink2);
+        font-size: 12px;
+      }
+      .tile .v {
+        font-size: 21px;
+        font-weight: 650;
+        letter-spacing: -0.02em;
+        margin-top: 3px;
+      }
+      .panel {
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 14px;
+        padding: 16px 18px;
+        margin-bottom: 18px;
+      }
+      .panel h2 {
+        font-size: 14px;
+        font-weight: 600;
+        margin: 0 0 2px;
+        display: flex;
+        align-items: center;
+        gap: 2px;
+      }
+      .panel .desc {
+        color: var(--ink2);
+        font-size: 12.5px;
+        margin: 0 0 12px;
+      }
+      .row {
+        display: flex;
+        align-items: center;
+        gap: 8px;
+        flex-wrap: wrap;
+        margin-bottom: 12px;
+      }
+      .chip {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        padding: 3px 10px 3px 8px;
+        border: 1px solid var(--line);
+        border-radius: 999px;
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 12px;
+        cursor: pointer;
+        user-select: none;
+      }
+      .chip:hover {
+        color: var(--ink);
+      }
+      .chip .sw {
+        width: 10px;
+        height: 10px;
+        border-radius: 3px;
+        flex: none;
+      }
+      .chip.off {
+        opacity: 0.38;
+      }
+      .chip.off .sw {
+        background: var(--muted) !important;
+      }
+      .act {
+        display: inline-block;
+        background: var(--bg);
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        padding: 5px 11px;
+        color: var(--ink2);
+        font-size: 12px;
+        cursor: pointer;
+        font-family: inherit;
+        text-decoration: none;
+        line-height: 1.4;
+      }
+      .act:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .act::before {
+        content: "↓ ";
+        color: var(--muted);
+      }
+      button.act::before {
+        content: none;
+      }
+      svg {
+        display: block;
+        width: 100%;
+        height: auto;
+        overflow: visible;
+      }
+      .grid {
+        stroke: var(--grid);
+        stroke-width: 1;
+      }
+      .axis {
+        stroke: var(--line);
+        stroke-width: 1;
+      }
+      .tick {
+        fill: var(--muted);
+        font-size: 10px;
+      }
+      .albl {
+        fill: var(--ink2);
+        font-size: 11px;
+      }
+      .opts {
+        align-items: center;
+        gap: 10px;
+        margin-bottom: 8px;
+      }
+      .seg {
+        display: inline-flex;
+        border: 1px solid var(--line);
+        border-radius: 7px;
+        overflow: hidden;
+      }
+      .seg button {
+        background: none;
+        border: 0;
+        border-right: 1px solid var(--line);
+        color: var(--ink2);
+        font: inherit;
+        font-size: 12px;
+        padding: 5px 10px;
+        cursor: pointer;
+      }
+      .seg button:last-child {
+        border-right: 0;
+      }
+      .seg button.on {
+        background: var(--accent-soft);
+        color: var(--accent);
+        font-weight: 600;
+      }
+      .sw-lbl {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        font-size: 12px;
+        color: var(--ink2);
+        cursor: pointer;
+      }
+      .cellbox {
+        border-top: 1px solid var(--line);
+        margin-top: 14px;
+        padding-top: 14px;
+      }
+      .cellbox h3 {
+        margin: 0 0 10px;
+        font-size: 16px;
+      }
+      .cellbox h4 {
+        margin: 16px 0 8px;
+        font-size: 13px;
+        color: var(--ink2);
+        font-weight: 600;
+      }
+      .cellbox code {
+        font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+        font-size: 12px;
+      }
+      .tiles.small .tile .v {
+        font-size: 16px;
+      }
+      .tile .v.small {
+        font-size: 13px !important;
+      }
+      .tag {
+        font-size: 11px;
+        font-weight: 500;
+        color: var(--ink2);
+        background: var(--panel);
+        border: 1px solid var(--line);
+        border-radius: 20px;
+        padding: 2px 8px;
+        vertical-align: middle;
+      }
+      .cites {
+        margin: 4px 0 14px;
+        padding-left: 22px;
+      }
+      .cites li {
+        margin-bottom: 10px;
+        line-height: 1.5;
+        color: var(--ink);
+      }
+      .tt {
+        position: fixed;
+        pointer-events: none;
+        background: var(--ink);
+        color: var(--bg);
+        font-size: 12px;
+        padding: 5px 8px;
+        border-radius: 7px;
+        opacity: 0;
+        transition: opacity 0.08s;
+        white-space: nowrap;
+        z-index: 30;
+        box-shadow: 0 4px 14px rgba(0, 0, 0, 0.18);
+      }
+      table {
+        width: 100%;
+        border-collapse: collapse;
+        font-variant-numeric: tabular-nums;
+      }
+      .tblwrap {
+        max-height: 380px;
+        overflow: auto;
+        border: 1px solid var(--line);
+        border-radius: 10px;
+      }
+      th,
+      td {
+        padding: 7px 12px;
+        text-align: right;
+        border-bottom: 1px solid var(--line);
+        white-space: nowrap;
+        font-size: 12.5px;
+      }
+      th:first-child,
+      td:first-child {
+        text-align: left;
+      }
+      thead th {
+        position: sticky;
+        top: 0;
+        background: var(--panel);
+        font-size: 12px;
+        color: var(--ink2);
+        font-weight: 600;
+        z-index: 1;
+      }
+      .ibtn {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 15px;
+        height: 15px;
+        border-radius: 50%;
+        border: 1px solid var(--line);
+        background: var(--bg);
+        color: var(--ink2);
+        font-size: 10px;
+        font-weight: 700;
+        line-height: 1;
+        cursor: pointer;
+        margin-left: 6px;
+        padding: 0;
+        font-family: inherit;
+      }
+      .ibtn:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .ipop {
+        position: fixed;
+        z-index: 40;
+        max-width: 320px;
+        background: var(--panel);
+        color: var(--ink);
+        border: 1px solid var(--line);
+        border-radius: 9px;
+        padding: 9px 11px;
+        font-size: 12px;
+        line-height: 1.45;
+        box-shadow: 0 8px 26px rgba(0, 0, 0, 0.18);
+      }
+      .frame {
+        width: 100%;
+        height: 78vh;
+        border: 1px solid var(--line);
+        border-radius: 10px;
+        background: var(--bg);
+      }
+      .note {
+        border-left: 3px solid var(--accent);
+        background: var(--panel);
+        border-radius: 0 8px 8px 0;
+        padding: 10px 14px;
+        font-size: 12.5px;
+        color: var(--ink2);
+        margin-bottom: 16px;
+      }
+      .cite {
+        color: var(--muted);
+        font-size: 11.5px;
+        line-height: 1.6;
+      }
+      .cite a {
+        color: var(--ink2);
+      }
+      .empty {
+        color: var(--muted);
+        font-size: 12.5px;
+        padding: 10px 0;
+      }
+      .sm {
+        display: grid;
+        grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
+        gap: 14px;
+      }
+    </style>
+  </head>
+  <body>
+    <header>
+      <a
+        href="https://nf-co.re/deepmutscan"
+        target="_blank"
+        rel="noopener noreferrer"
+        aria-label="nf-core/deepmutscan"
+        style="display: inline-block; line-height: 0"
+        ><img
+          class="logo logo-light"
+          alt="nf-core/deepmutscan"
+          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LsujuNxdQHtnTTpGKmj+Y0mdSYieOc8E323w6ENi0jsR0qAm0ywUmZERG6/rpLZoxxTRCQeTVJ3T39F9XtW0SgLLA9OV3NtSRK84+1GtdMgGGTclAg/4Ptn6mdniQUe9kUgPgQeBwC0AETTKMrOoMyAHzT7OqP2j41v/Cj7B2WdyIUACODEWgi6j0A/g1Q/WXzkd55pJtZtrz1tR3L4K0jEL7jrJDCAEqFYvSoGUgBV8DCfhyRIpiMXHX3+n5qJeSJh5sha+xoiPhVMh0qlpg/6Ldj7DcPY6/+m7GRMRLX/phdeap01/QA9QkL8QTh3ObS+h4hG9Wzj3HPPFZ/61KeOIOLTwHiVVLp3Q5TegZkbDrcfrB4zieczBWftAAm6hRkXP/TQQ9fuueee6WhizYOZJ1lr/00IHM6MV4B5d6l0adASG977oV7X869DoH5q8c46gG5MjXlfuVxeVDSmdevWTe/q6jqZCO8kYK8N7xF7+EHHQSNEBCHEhuPBWVshol877/83iqI/F42n1ZhZOZveKlX0kkYJllQaNk3uUFF8SLtuEJox9+rTfyQidTKnxUJiAA7Ay7vL5uxZU/1eXSUZEUkikGO4hyqJ+Z+lq/DnvkopLliw2QEwtZtWRAQnicAAp+yFYxKSANWghYxiBZ/Yzy055sJzC+00CBoY0xasBb87c3La518iQK9dKfwr4f2uFMkyiGqJlKtNEMpJwXPKoMSrVo8qu44SKRLiAEg6gI35wNyrT//qzBXi2/cUbNHyjoQknkySNp2qgYdI+EZAhAhEwydlguBTP35bHFvEmOrR3rn/VEoeVMuKPRpdeAbbOOGqnWqfJ4SYRELuB2A/590HyLk7mfldRHRfM3Eyp//mnfgMCXo1kcgdZ6PYmRkY1MFZCNFFQh4B4Ijddtv1NmPMeVrrG5vawUj7T5L9vBJv9s4dq6TctT41E3tX6DUN9TqISAop36iduwLAogLbojRNT1FS/peQckcA8M7CF3yPmRnOOdSPByFEmYQ8hsFvdMZc2F+p/Fdvb++qQhttIWvtywXJA4cvzQCQpO+Np+QKAIh5u6KFRVkQIgBnTJ+y9MxZU70imiFLSiWpg3MeBMjdumL53QVz7GefWlG5YvX6cilHkmWYwQzeLlbmoElld0B3meZFmrukYA/wSmPiRyqG/tJfoQerCSqCAOuHmitxq0IvKAiGMSYX7HlXn3EwS5xk+vhIIcX2pERtlnNmcNrG7kjMYOsAC5AU21Ik/2/lLPvaeb8487Qnjzt/WbFtwcNza6aFYOTaDkmxOYzXGVKlUpkfaf3/hBDHgQje2VytP3l57zdc+IlICSkPds7tAqBQgsV3363tPnt90jn6mJSy5J2Fb0MXunq8RICQ+mBmd71z5jtCqE8S0brRbp+ZBYBXe+fe55hfI4WKALT8fR+0rdythcw8w1v7jSiKTgLQdOI6lPr7KoRQJOXZkyZ1/VuS9J0Sx91NJdqjRezPESoSDVuvpII16SKl+6/ucGjD2vby48sgbL0hQREEkqLWiYq5NgXNRslL7XEir5smxXs/tvO836PPDjxjk210Yo8h4L1RpGamqUXiGVqQOnfbmVhlbeXm9ZVy3OBxoQNgPfO+XXHy9plT/CE9XXqKlCUikAcgpIBWEiaxT75uK/rNGan/88efXLHq2rXrXi8FvZ0i2b3hmsNADJrWvnct2NJ0NMHa7op37seR+hLArxJKSsCDnW/6Gf5osPP1aRKO8In95bxfnHlk4SQraAljzOuFEN8TQsxt9QV+KINauwplRswre6ydfL5S+qSirTvNYq4lGEIIQUK9xzm7F3P/W4kmNV3ag9P0UO/cp0E4TEhZuKWqnarV6k7eucuFUvu281ioJ1pS6b2ULN2YpgPHR1HXbW3ZWQPVanU3EL2eG4wcBFCboxR8CdFW46rQcCSn9ViYyUzZo7WqXQ9n72CiJ4kxA+CXUqyms3EAA6Ql2PunhXUn33n0BTcNSpfWAHhg5RPnXWksfh5Hau8ktbDMiCSp/547y7/l0afSp62LNr5YJcyYLqV5z5yp5titJkddUqiUGUn2FCGOFVLjVqXWf6lqqpdus+tnB7dU3rjNtaedL1P/GVLiGGYGg9FNKD5IKQga6GiZBlLitaJLvxbMkhM7JonVxrhqIWK1P4T7we6XHx/m+uswa5PThRBXCiHmOmvanlw1i5ljZyZfqpQ+yVlTu0B3kPe1R5BSqkOdi65j5lnNbMfa9Cwv5W+FlIeBGWPxWhqpVqs7aSWvE1Lu26ljwVkDIeUcQernzNVd2r7DQaSk06TSXY3ef1ErLLpW6viSTsaVh+e0DMZ0MK/zqf0fB7zoyWN/8Jolx1x42pPHXniUUvIANu58EIFKGuz8P13ij1j85otuGmp7M7b/5EMMc4KxbpWUtcuS8YytIxV9aPY0B35+8jEGUPWMF3WVqj/ecRt3yowpXUqQqnoPnx0zpVjBGnd7Yv2h03b4+Fc3Sq4AAEvf9IN/PHnMBcc6w6cRsJoiCYyT+R2DzUNHEywG2/odzXjCVQuK9RHrVe9pYx3LlsQmyTuI5PcEoTxeWlAasWn6Zan1MSP1lWm3LMna3zvzQ2YuF12fGS8VQkTjLZll5qlKyp8KqXbt9LHgrIFUelub4ofM3JFCk7xu3QwC/Ttz4+SWSICBq4hocSdiKsKx7QbRL7yjQ5YcfeHHnj72wkcG//7xo85/8smjLzyLvP8wp/ZmRfS6pcf/4B/DbXPGgk8/7Jz/v0g/P9gjYcbrpnTHr+mdlKTMYACGvX/b9N6BC3eYIxeUo1LF84bECqi1XCWp++tAlY+cs/MnRyxr8dSxF1zEhl7DVXf/eudLIy0fBHmFQqN1tda0jy743ZmTxzqULQGzeQ1J+V0ikrVOyOOXMdU3CCnf650bF0lJrdVFv9aZ5KNNrD4uM1lrkq9LpV40Vom2swYqig/01n6sI/srlU6USs/2DY59EgLeWaMUvteJeIrq4cqjTx5z4QlPHXP+/cMtt/iYH3z1yb898prHjzr/yTzbFZ4vHaiYNfVWLGZACBJnzZpCJSJnmP0Ht55W+a9tZ5ZiIXS6UT8vKQWMcas5Naduu/snck8/9uRx5/9Npf2vTgSFuQiDlgkJVoath4jVfNvvw3xUbcbMs73D94SUpUYXmPFi+fLl3QTxP0JKMVxrw3CEEJBKQSo96EfVygc0ib0DCflRTpJ9m97IOGFM9Tgh9SnNtA5u+r4+/0MF6yix92Dwf1Sr1d0KB1JkP8wxCGcMt4wQEuz5FuC8u9sZS7MePOGK/GVDPntL7g92+i6fWgrgRj2oFSv1jN27ytGxW/VUPzZ7WnLOrK3KBizcEDc7USThHX9nxm6ffjh3fJnHj/vxiqeOvuCyousFQSMTa9h/faQK0aYVvDzXSjKMql8XgRnHAvjJsItZT6RIkxSbjP7jZkYWChr5YiAI7Ny4qVs2Gtaa85TSC0bTWiGEeEHdqCHlrJU0nGnTpr1FSrl7M7FKVSuCak2awLnlIFpLoHXM3A3CZDDPUjoqA8VHyvlaB+0u683HAZxYOLgCcr3XjYyQlPKqVb0O4gu1qWDyJ9tSSoAEnDVPgfmvTPgnET0Hj0lM2I6IDiRgH6l07vfWewep9CTPyUcAvDN3MEVZ+1oh5N6NEkoCaiPxyH+X6LPjo4NcBwmiK733/z747yxYfGz29JIWJFLmITtKKSVQrZinY+ZvdCjUIBjW+E+wiECRBFsHdrycrX2cCI8y6Bki7mMiDcZUAu8MEruTktvWens1cV6q1d160S7XvLPn4aMvWt9wMYhUgh+C9dELOpQxwOCZJERv7iSLCHC8msGrhy376kFEmPBzFqZp+hJB9NZGc66NpJ60OGsG4P0iMD9IJJZ779cSUcLMUyFoKwJvB9CuRDSrvk7RUWm1gqfm3S8oFZ4DEUFIBefsP+D5Is/4fRRFS26++eaBV77ylZaZJYAykmQb59wrCHinVPpA9q5Qh3P2DkR0dJIk+8ZxfG+hIPO/GDjn1pP3SwFeAcZKJuoH+xREVQFhPbyChwJRBEI3MfeCxGSAZwDYSiqxFeTQreWmd9IpWumdiySYtaTJrgXx56XSlxLRJh2Ya59d8mq2+IJUep+822fvIUgcz8z/TUSP5w4qJ2YmZ8w5Ugj4BgmlUArO2geUin/V6v1PBF6525PUr1ZSbuWyG2ZmQEmSm5atep7WEsa4n/Tu/MmVHQo1CIY1vhMsIjB4DVL7c/b+Ks3RvY8f972Gc4rNueqUaZrLh7Pgj1Ak9y1aU4s9A4TZhv18AA37Fiw//oJFs68/68VZkC/4nXL+WxTJU/MWR6VIAhX7rVSLL4+07LLqc0mujY5jAviIkCoq2mLzfIuFvQfEF0mlf4/a1DcN3xNmnmGM2RVkX8WMN4J5H6m0zD3KwpiDSNBeRR5d1arIE1trv6yUOo8U9Q2xjAPQB+BhAA8z8yXOmPeToM8LIaK8SVbWihVxmr4VwL25gyxASgXL6del0ucRUaFpmpi5CxiYYgy21zrapA8OM5etSd9dNB7n7HLP/OZI64aV2IkoBXDjunXr7uoql6+RSh+c55jLWrG6nTMnAvhikdhySdP9IMWrhr/BIIBx4Xidl7LdZs7/1LIVj553r1LiMDfoicRw90ZCEKpVOyCUvLgDIQZBLuM6wSItwam74cljLjg7z/JPH/ujZwH8dLur334jm+h7FKkTC03jwAwhpXZWbIthEiwQ8TIM3Zo07+rTiz9LIkqXHXX+hG+dGgkz7+Ksed2wdX+GIJWGc3Y1wJ+USl08XFI1GBGtBLASwK3MfJ4xZn/y9gQwvVlIOY/88JXxLfGxSuqGRSCHIqSCtenntY5zT7eRvZ7/SZLKKqX0+UQk87e0MUA4etmyZZ+ZPXt2f+5AC2DmStHkCgCIaAC178nTQ/3eWvsyIdVueRNYIoJnb9i6s6JSKdc0N729vSur1erpAG4TQkzJ3ULIOJ6Zv9Lq6ule4Ew5zA2GkBLWpCuUTn/Wyv1ONES4Vwo6LO/yWktUE3vHzO0//s92xhUERYzrBAsAaOM5T3JYfMyla2Zc/q7Ty5zuSFocwKbA40IpAOtmF91nHYMLT1HK4Jb1rdr28uPLUvXOH3ahCGDrqfLbhRF7JhJZlwatAQ1o6NpsijorC6Z17T+z3wMRWJMw4FXLzroi1+ggAHAmOUHquKvo4yDv3CLv+fgo0k13+CUiA+AOAHcw8xcAd6wneqLR8rU54szLiuwje3T1F6Wi/24mxjguX2TS9CCl9Zm5+w05ByJaMG1a756ovb52aE/fP+YTi/S9ElLBmOTqqFS6tshuSqXSP41JfkQiei9yJFjZDcAeaZruCmBhkX0Np7+/f1sGHT9SaQZB4qdEPctbtd+JyDPf54d7HrgRIQhg/LKNIQVBYeM+wWJuLvlYecJ3+ra76rQvwONKFLlAEMDSdzWzz/GAop49GP4WBiQwRF9QIpADqncvpnRFv0Q2zxcJAukU0ApeG0BLkLYQWkNZD+8khAXISZA24FJMAuJiYPjRUHXMLJxNX1fktQgh4J1bJ7w/SUZRy0ZTEdGzAC4YbplqtbqdkmK3Iq1ttWrQ7qtEuune+0rrLzpnTxBCTsnTT42ZIZUW3uDlaF+C1XJLly7tAvCyvI9rqTZ9kgXE/zWzP6XoUmftOUSkRmodrD96hTGHooUJltbyFKn0Vg1br4SAs7bivL+wVfucqMjTE6lxIBp5zBARoVK1FkxDFjENgrGyeZdpoO4/svXLSBZ8mV5M2GJz7EkA1EVCxERUesGPqP1U/764lDy4LAag4FkRoCCEghSKhFAspYKUiqRUUomN/icVK6l8pCRkoaFl8wHap0jCQkLCWftViqKOJw5KqZ2Vblxle2OyVnV7mVLxzaPZLxEtYubfUMESDpJwwGj222lz5kzflQRtn7dMh5AKzLxQa31Pc3vUCwF+RMj895RMeElz+xpiW8zdgsSpwyWUJCTA/tdxHD/Qqv1OVCKSq5z3CeW4N5aSatPwoPqvDoQWBLlt1gnW4mO+voYJDyPHbOwv0GSr2fjBHpyVi6j/ZCf26l2LkNz3FDa8JwRACpCSICkBJUBSQEgBqWr/FkqApAQpCS8FvJZgLSEilbsN31q7l1R6Ut6EJZsm5Bkdx98t+upbgYh3K/RkjAQAvpWInhv9vsUNRddhYBdmHvct0nVpin2kVCO2Jg3G4JuyR72F1UaY+r8VWgfYi5l1M/vbmHPujVLpnZxtUJqBCM5ZliS+04r9TXQliOcADFCOc3dWlPSx6Tt/dtSToAdBK02YE3KzCFiFwr2iNjNU+0f17sWo3v8UBrfo1ZKqWmIFJUCqllAJLSC0hNQSQsnagAMt4CMJrxWEVkCBPhKA369QyCTA7H6TdVTvOHa8U8HqDGCgydaVF3LO3c/eOSFEvs7uzGDmbQBMRa1T/7gnBPYqug55/J2ZxRW4ovAX+ngcz86Y/C1D7MHM2w0MDMwE0PTE2gDAzNKl6TmQjQ8oIRWcTe+Cim4Zzb42F1bKlKzNVcxU1M7vufuCBkGnbPYJFoCBNnXRnRgIANWSq2ST5Or5pIqUAFStlUpoCalqP6QESNcSMK8kvFKAlpCRKjZZN9OuRUP3zH8suk6rEFHhyZSJ8Ggr9p0kydJyKX5WCDnT5Rhhl3Wa7gGSCZNgEWPnIst7Z8GET1lr3n8sji68PwsDImyVd8Si9x4g9Gqtt8YoEyxjKgdLGR084r49vt9sC91mh1MG4POcuokIRKP7jIKgHbaEBGvLtVFyhcHJlaANCVX9B/WWKyU3/NR/53Wt5QpaQmpVa/EqMs0N85zcYdcel7goohEnam0b4hm5FyWCNcYpHbdkUt6enp7VzpqnQTQzz/LMDCFEyRie1or9txszk7M212sbtA6U1juPbkAjo9EjuiH3pyJhrZ2LUbZMEsTZQkrZqHO7lArWpItVFP1iNPvZnPRXtIxUmrsPAnue8DUCg81PSLA2V9lj0cpQLVdUT64Gt17Vkqtay5WoPRrU9UeDEk6rer+rWnHUAte5J554okSCpuddXggBa22lkrqxeTzILJ01k/IuX5vmyBskySZFRZtBRM6YpFB/EiElPLveVuy/7Z55pgvTpxVOBvMmR61EBW4MhsJc2dFacdSwgztqU39dSkRrR7OvzYmITTc7lPL10WOQkPnnRgyCDtmsO7lviVh4IikFCUL1nsVIFy6tJVC1CuO1zu1Z4lT7UUBUS5xk9iNiBYpriRTHCj7W4FhDlCPIkq4lZVKAwbl6Kc2fPz9i5u7cFdQBEFG1XC6PVfFViVolsCIsYl+4GGcjxPmnRapdhAgA4lbtv6227irVqrw3P0dkp3j4rUazvk3FO5XS3Y0Gd2SlGdZa6y8ZzX42N9JgKoG7cnVBBAD2nc++g2AEoQVrc2NEhYkfqi5cWq7e/xRBPZ8DEQCxod9VrQULkkBS1jq2CwIJAZIEkIAXAp4EPBEgBEgQPHvA1vpvCVDDaYs2UjhhYQb6+vrGqvecZM4fbzZRdwqUW3cXLahwVXYiPzESLPQqIFWFJ0UfA0TUdIK1Zs2arUjQycO9ThIS7M3V5XK5YdHbLREJv5VSUrocc8rWSjnQxGi9DbYoIcHazCxduO3Cbfd4cH9762Nazpu8aYIytfaPKS/482C9L/g7AQA9FkAP4FH7AQDjkWJdroSir69Pl+Io9wWVmUGEWEpZzrVC69VrW+RGRDQwMNC6FmHPsvAoRjsBMpYahQlz7qGeZtfs6up6s1R6bqO+V1lfQ8uM7zcd3uaKaY6SAjZHgsVgMFB4UEoQtNsEOckFuX32s/4poILaT0OjLtZUQHd3t7HGmCLlMpg5EkLk7gfVYj7vfIdAVsGdORZCtKwFyQPdBfMrQMqWPaJsM4uJM1quqXPkwoULI0E4c7hlhFQwafonHUV3NhfaZox4F5GzfiEzQAK5B6UEQaeEBCvohBTggbw945kZJEQkwFPaG1ZDFkDRx32qVCq1bAYAQSjYyZ6hRkiqx5EqAdWi9emkaknNz0KIuakkf5dddjiMSOzfqDQDZXPAEPN3iahAvZMtBNNuecvssWeAOfcgmiDolJBgBZ1giKhQp22pNAFuJwC3tTGuIRGRt2myJu/yWZkEbUxlBoB/jnb/zNxljZlZpBO4NcZ7xurR7rtDktpP/gRLCAln0qs86G8dG5kjhGDi+5tZlVicI5SkhvMOSglrzD9XPPvsjaOKcTP0yCNfj0H9u7qcdfaySaHn8JNfLdO8D02Um4xgCxASrKATDGoTLBdC7F4K4NI2xDMiJlqee9ksIWTQjgD+1ILdzwZ4DuedVkgIMHOfs3YCJVi8CsCOeVcgIcAej0U6Oq+NcbUEJ8leTojXDj/vJoHBF26zzTZjNVJ23JpM/XMBzM+bYDnvAcbWz/SnswAsamdsQVBEKNMQtB0RMbMv2LLD8IzDli5d2tWeqEbc/1NF1yDm3VuxZ5sku0ily3nnbQQRmPm5crlcOIkdC7XjgR4pvCLT4a2aG7CdrMAZUqpSw9IMtXk2V6Sp/WmHQ5sQJNFeUaS6fM5nhMwMIUW3FJjX5tCCoJCQYAUdQUSFHrV456Ck2nHmzJmvbldMwyGS+eety3jvD23JhMuS3kCF+icRCHiMiFpS6LQTGMUSbu8slJR7Wlt9RZtCaglm3ppAJ2XTFw2JSIDBl3V3dz/TwdAmDCb/ciWLDIgB4kgBKn+LaBB0Qkiwgo7wHvd75zhv4sDMgBBE8B9buHBh0aKfo+a9f8g56/PG650FCdo7TdPdRrNfZu4G4/VFa0Qxoam+QmOFiP/G3qPg8SCJ5Yf48nwFbseCM+bfpdIzfINppGqFRU1VeVzQ4dAmBObLJXs6xBaZ5xTZxBXeF57vNAjaKSRYQUdore/z3j0lZP5ro7MGUkUH77rzjh9pR0zM3HDUn9b6Yfb+6bzxZv2wIgG8bTQxuTQ9QSk9P88kz4P2DkC0ou9XxyhVutN5t1SIgseD1oe7Y8272hha05iXdjHh9OEGJ5CQAOPXFMcLOxjahLH8kX9tR8S75al/NZhnAAUnEA+CdgsJVtARRLQO4N8VnazXOweS6lybpue0KhbmZH9nzGXOpW9ptAwRrSXQXUXiZe9AQpyRJMkezcXFsyHo00XWkbXRaM8ppW5vZp9jhYjWAPhz0VIN7B0I4kvW2pNaGc+5554r0jQ9xNrk5Ga3Ye3MI5RUuw1XmsE7x0zuu00HupmLFL80jvWkvP2v6rzzIKLt7777zHHfRy/YcoQEK+gckj/y3uV+7AYAzB4EaBLi286Y71cqlfnN7JqZu5jN4c65nzgr/iKUOhGgYTvQO+YbiuzDew+p1GQpxEXM6woVPlzFq3q9MxdLpbcr1HpFAgS6hYiWFdnf+ECXMed/TAjU3mMhqIuAS61N/4uZm660DgCVSmW+telZn/70p2/WWt8CT6c0sx1mFsT+nNqAg6GXEVKB2d2tVOmm0cS8ObPMh4uCSTcAOM9g8Jxtpuy0ydwUI9n5ujNDDa2gLUKZhqBjlFJ/djb9i1TRyxrVBxqKr/XVIaHUmZpwjDPmKg9cq5LkXkyatIKINunwwsxiYGBgdlcU7emBV3hn3ySE2E0IUZtYI9v0cPs1xtwgBa2SUk53DfrUbKz2WFMf6F35Bub0XUTR3SOtw0myt3Pim0Kqlxd5X+qJCRONSSmL0VJK/coac5/Sep8ir9s5ByFEJIT+nHP2zdamF0ipf0lEi0Zal5kjJMkCp8TLCeJNDD5YSjVl0CLNVcM35sUgcWij1qsNCN+niVPFvqOW3fuVSeD0kKKPB4GsFhbRVOntNgDyzZF6+fFyrpr8hYpz2wBouuUyCBoJCVbQMURkmc153rlfUW2sfu51mbmWvEg5AyTOEuCzLOtnyZonbZo+TYTVHkjAKBEwzdl0RhzpBVByK5E95vPOgrmWKMkcfX+6u7ufccZcBRJnAvkSLGBDkvViZ+0tzpkrvccvlLUPolR6DrULuEClspXVYg8BeawDTpBS9RRJMoBai4iz5n6l9G8LrThOEFFik+RbQPEO3957wHtIpfcG8E1nzeetNQ8QYyEz/wsCa+G4D4CElN1EvI33vJOzdjdIsaOUujbPJXvU3/fRVIq38GcrFatGn6GUCtaYJ5WOftH0TjZzsmwPUFJuZ3PezAzGzChFSlYqZnsAfx9p+TlXnTJNyfhbohSd5AaSh3a88b3xY6//Zu7psYIgj5BgBR2mfuu9+bHS+pSiCQVQa70AHIgISqlpIDENwH7AEM+72cM7hyKJ3MaMc19XwMlCiK7cdalQS7KEEF0k5NuEwNscuB/W9DHQR4BiJacqpXsBAnuHou9FvfVKkvhSkSr5442Mop84m54mVfSSpo6HenIk5RSQ+DcA//b8xl+4rMgOkGbe7+FUKpX5IHHMsIVFiSCILs36nm1Wtrv67VMg1GGL3/SDq0azHaHwliiSolJ9/nsmieCZc81pICRBCOwNYNg45v/i1H2clBeRVvv7qgGY5gwk62cCWDKa+INgY6EPVtBRRMRK6w87ax8bTYsBM8O52oWy4c8okysAKJVKD7L3F1CB0W513vsNsQghJkkpZymld5BKbaeU6vVZ/EUStzohFYxJ/wApryy88jhCRBXJ9EHvbCJE86ejEY+FQT/NvN/D0ZLerpSe3LCwqJBw1qxLrb2kpTseL6qlKezpx3OvPfPrs354clNzN6559As7EnBSkloQAZEgRET8nLHGMzudY+Jn5xhMdDjzuQ0PpHlXn36aV+oPQsn9ObG1+SAFehT03GbiDoLhhAQr6DgiWsHA2d65ASnHfyOqiqLPOWseGU1C6L0flATYUSV/Uil4Z1d7z+8joqKTUo87FEW3M/jzJGShDu/jAa9ePZlB7xiubhkJAWZcUy6XH+9gaJ1TNlMBjoUW74snl/6w3dWnvaLI6sznCgf8T3dZT4kYYIa9t69SPXfJ8uoxjy7h9z3xjFlcSZOyEBiuA7wxFkqKg1Y+Ep2w8e/mXnXmDvOuPeMyUuJCEE3jNGttZIC0JIKfX+g1B0EO4//qFmyWtNZ/sElymgculVJFxeo+dRYRrU7T9HTy7gYpVc9YxiqlBLw37PzZpVLpwTELpMWk1F+01myvlD6t1ldudC2PneJ6e49WUs5v9MixVprBWga+1+HQOsaz6CYiwakFKXkQs//d3GtOvw6Ov2e1/Muyo85v+AhbAEieKH3ESrzprmf7qres7eOb1/eLh6tGV5mlJsJNZgALH1tqzpk1deBNW/XoyUpqy4Db6NEhM0AE0pH8Bi86bwnN/+RfZl935nTFfAYxf5C0nMGp3bRMGRE8i0KjfoMgj5BgBWNGxfFlSZKUJfBtqXS5lf1iWi2KolttkpzjJS6SSkXOdj7JklLCMzt25j9UXL6i4wG0ERF5Zn6vtaZHKX3CREiymFk5m569SWevQYRUcCa9Vevojg6G1lEEH5FQYMdg4wAiJbQ8lskfq5x/aN41p9/Cjv/OTItIiH5y7L2EJvJbz9LqxWc/sezsR6rGPZUaXfUsJQGKCKUN/QwJz3mvP7d0pfrJqrXpW6b3DrxqcrfcOlJKZ3VK6rF469kKmn5nv/n5y24457rHk/QIEev57Bw4Ge4768vtfp+CLU9IsIIxFcfxxcaYxd65C6TSCzp8YS30PErF8U9skjgHXCCV7u5kQiiVhveun9m9R0XlSzq24w4iogozn2xt+rRS0QcAxlgksnlZa18uSB44XGFRMAOM7xJRazt+jSeCqux97dvEAJjBqQMIEErsCil2JQbgfDblEUMQgaTCCiL8et0ABGppaqlBXysJQApBi4yN/3vps/zt5WvsrqXI7NoVYRutjSZCn/Pi8SQVj1UT8Xhi5xgtzpFKgtORjyFBGL93d8GEFRKsYMxprf/IAwOHIY6/AOAkqbRoZ6I1qC9V4au3iuPL0jRdQsB3pNJ7gz3y1shqhpQSIAHn7D+k53dTFP+5bTsbB7IaUR+01t5PwOel0nPYu5Z3TB9G7nkvif05QkWiUaItpISz9iEZRYUK1k44JCvw3oFIvuD5GwNsPTC4rlU9f/KM+qjLIhONKgBKEPV5r2/vr+jb+ioA8PyUV1RLxhQRKM35vWQGHCZgod5gvOt8J3cq+MMF51bZGDMV3udE2t9mgrq6FpOUb/WM13nnbmGApdKQSrWk47OUErXtaTjnFjpjPiClbmoEXhRFf1nf1/dKa+153vvV9e22qoM2EW2I1Xte5b3/rJTqFRRFrUiuqL6PPD9jRSl1UZKmB3trL2VGRSpdSzZbqPY+qw3vNeCXeCBXlXVm3hVErx+2NAMIIFw4kcto5MEWa8Gcr4YUY7ipGnMTACIilMRGP0TQRPlPqwSwccwsFo0+qiB4oc63YBX5gjEAGuXXkYgL73Mi7W8zo7X+HTP/wRjzMmbzDgK9Tiq19fNLMNj7Da1bg1u56gkBEYGEwODs1Vmzgoj/zMDlUspfklL9o4lzypQpqwF8ipkv8d6/HcwnCSl3fH6fDO9c7XHJCNsiIgghAKoXamJ45x4B8c9SY3/Y4tFnXNvF+D/wurq6FgN4R5IkX1PMZzLhOKn0zA0LsIcfdCw0siFh3PSYYGfdUhL4s/fuSjWQ3BxNnvxsntisTU9XKuoavvXKrJRK/yTP9iayLmFXDDCtEoLmsRv/x9VgJAS8c0uFMpvNgJFg/OhoguWJf0bMtwOU61vIlsl7mW/agwacs+dJlhfk3iczeZsuanqHLL/MzJcW2Z9jLG56f5uhrL/KLQBuYeYZ1toXCYFXMePFYJ7HzDOFEF0vSEoA1JMa730F3q8G+EkS4u9C4A9S6b8Q0fI2xPoYgP9atWrVV6ZNm3aQ9/ZwZryUvd+BiKZLpUdsdrEm9cy8kogfA9Ffvfe/WbNmzR0zZsxY3+p4ldJfAswlPl/tRkgY0tqN6fEZx/G9AN7V19f3+VLJH0IkXseMlzDzNlKIyWLEUh8Ma0xC3j9HRM+A8TAT3SYV7sTa/odpypTnisTDzDOcNf/O3PixJZEAe/tzInqmyLYnooePvmj93KtPewyC5hWY8GB8UAKw9k+Lj710zViHEmx+wgOqYEJh5m4AWxtjZmiiSRboJu9jFmwAVJSKVyNJnkMcrxjLqtnMPB3AtoCdnaZ+jiSa7OHLRBQTUwqgAsFrmcUypdTTwPqniHpXjlW8Ew0zx5VKZZZSajshxDwAPYDvZeZJxJRAoA+OByDlADOvUEo9BaxbCfSuJaJR9ZxP0/Q9WutvNmy9EgKeuSqlOpCI7h/NviaKuVed9nFR0l8YfqTeOEMABDkmfuWSN15461iHE2x+QoIVBEGQEzOXrDV3KKX3bjjvoNIwJr0uiuI3dTi8MTPv6tMPAOF2AGqidHugkoKv2h8vOebCU8Y6lmDzFCq5B0EQ5GXta6SUew9XmsE7x0TiOx2ObEw9adbeyx5/ppGfiI8LFElw1T6sBH94rGMJNl8hwQqCIMiBmckRn0MkGnasF1LBe/s3pdQfOxze2DrhCseEL7Hzfrw/F6FYwTv/hIc4/ok3/aDl/TKDoC4kWEEQBHmk6X5gepUftjQDQEzfz+p5bVGeOubC37D1F1Pc/JydL0AAWlkqRBCopMDG3epTc/hTx5y/RfSPC8ZOSLCCIAhysAJnSqUiblD0VEoFa9IlMoqaqq+2ORBkP+wTcxuVRjFAnQgUSYDZw3tLWtb+3GyuJQgUKwC8lhP7mUqiX7/0zZc82nyAQZDPOG/MDYIgGHsDAwNzI63uk0pN9Q0q9wupYG16ntbxpzoc3rgy95p3ziGSl1GkXsaJrVVKz6s2VY6D89/0wM+0RNV77OlJvIUIR5ASko0buX6gIJAUgCCwcSsB/imR+N7io85/aBQvLQgKCQlWEATBCGyaflJq/d+NOrdn0+KsldYdQOXyvzoc3riz441v7U3S8nlEdDZpqTh1IydaUgCAIWvPXnzsRRdt/Ovtrj7tFUz0UQCHU6QErAf7bJuCQIJqCZpnsHHrmXCnIPGLNMENy044/8mWv8ggGEFIsIIgCEaQJMmeURRNNmborlVaa6Rp2hfH8X0dDm1c2+bq014hhXw/mF9DkZwEz2Dn65Ng1xCBtARbP8DWn7PkuAt/ONI2lRCnM/PLAMwAADCeg8BjzHQfEd1Knu5afMz3F7X31QXB8EKCFQRBELTVvGvO2h1wx4LxahB2A/N0UrUmK3Y+AeF279y5Tx178S15t7ng8jMn+0hsDaTwKl7x5JHfLVSRPwjaLSRYQRAEQccs+MXZM43088j7rYnYO89PPvWPuQ/is59tPPdQEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBEARBkMsWU2iUmTUA8eij+SZR32mnnZiI0vZGFQRBEATB5miLSLCYWaZpepVSakfn3IhTuwshyHu/Qmt9NBGt7USMQRAEQRBsPtRYB9AhBGA3IcROQohcK3jvZwKQbY0qCIIgCILN0paSYAGAAQBjzIgLKqU2LB8EQRAEQVBUvuacIAiCIAiCILeQYAVBEARBELTYlvSIMAiCYLPAzAqAXrJkSa7l586dCwAMICGiEQf6BEEweiHBCoIgmGCMMedorT+w9dZb51reOQfvfZ/W+rUAlrc3uiAIgJBgBUEQTDjMPA3AAq117nWstRWEkdFB0DEhwQqCIJhgmNkD+UZFA4CUEkQURkYHQQeFTu5BEARBEAQtFhKsIAiCIAiCFgsJVhAEQRAEQYuFBCsIgiAIgqDFQoIVBEEQBEHQYiHBCoIgCIIgaLGQYAVBEARBELTYhKqDxcw9AKYB2MpaO9k5V5JSwlprpJTrnHOrS6XSSiJaO9ax5sXMMYCZxpitvfczlFLdAGLnHABUhRBrvPfLoyhaRkQrxzbaoTHzlCRJZgkhZhHRVO/9JABKSmmZuZ+Zn9VaL1uxYsXyWbNm9Y11vENh5l4AU1E7tqY453qVUrFzLgYAKWWVmfuY+Vnn3Io1a9asnD17dv/YRp0fM0dr166dpLXuEkLERCTjOEa1WnWlUikB0A+gj4hsq/a3bt26nt7e3knVajUiIsHMjpnTcrlc31fain1toZqd7sa3NIotADMLAN0AuqvVaqlUKslqtcqlUsn09fVVuru7+4hoYIxjjAH0VqvVbiFEFxGVjDFaa+211gmAPgDrADzXqu94JzCzBFDu7+/vllKWSqWSSpJEMLPPziUDqJ1LqmMd61BorAMYDjP3WmtfAuAwZn4xM+9ARNOYuUtKKaSsFSX23sNay0RUAfAsgH8R0W3M/But9V+JyFSr1YVxHO+RpzCfUgrW2mVa6z2JaHWrX1e1Wt0ljuPDrLWvALAXM89h5t4oijb5PJgZxhgrhHiWiP5FRLcJIX4N4C9jdVAxc1f2ubyamV8KYEdmniGljOufyWDWWjDzAIBnhBD3A/ijtfY3pVLp4Q6HDgBYvXr15KlTp+7hnHsRM+8PYGdm3hbAFABdQgg51OsAgDRNmYj6AKwA8IgQ4nYp5U0A7hnrkywAMPPUNE3nSyl39t7vCmBHANsCmAWgl5m7AMR4vqK3I6KEiPqZeQ2AJUT0TyK6S0p5JxE9nmOf3QB2ds7ty8z7MfPOAOYCmMLMk4hIZydKByDN3qe1AJ4iooeJ6G5r7Z1xHD9ERC1LALL5+qKCq5nRFORk5hKKPRlIiMgV3B6nafqJKIo+VaTQqLV2fRRF+z399NPL5syZM9zitpXJ75o1a6Z2dXXtJoTYm5l3yb5rvQA0MzOAFMAAEa0moqeJ6Anv/cNa60eI6NlWxZEXM29lrd2PmV8ihNiHmRcw84zsBr/+3WHUjpUKgD5mXk5EiwE8SET/sNYuLJVKi4b7bEcR32xr7e5EtDcz78HMOzDzHCKazMzdRBRl37f69cSj9r1bR0TLmflRIcRdUso/A7iXiMb8RpGZywC2sdbuyMy7EtFO3vvtiWhrAFOzc0wJtUYhQUSOmQ2ASva6lgF4lIj+LqW8E8DC7LMZU+MywapWq7tIKU8jomOFEDsQ1cJkZnjvUftOYsO/678nIgghNvzZWgsiuts5930i+rBSahdrR07e25FgMbO21r6RiM5g5pcppSbVf+e9f8HrGqz+WoQQEEJsWJ6Z7wfwAynlJZ1qsRsYGJgbRdE7mPktQojd6vEM/lwavQYiwuCkxVrbR0S/Z+Zvaq3/2O7YV65c2TNlypRXATgWwMsBbKfU8w24jY6tjTV4LciSkl8IIX5IRI+28aUMjlkAmG+t3Q/AS5j5ACLamZlnaa03aZ2ufz4bv7b6a6ofa3XW2nVEdDuAH0oprxp8wurv758Tx/FhzPw6AC9l5u201i/ISuvvaZ59GWOqQoh7vfeXJUlyWU9Pz6jny0uS5K1RFJ2bNwnRWiNJku+USqWvNbM/ZpZpml4fRdGOefaptYYx5vQoiv7UYHulNE1viKJo7kbbY2aeJoSY1ug4bbA9D2AxaglUw5jSNP11HMfvy73hIWTft9cCOA7AIQDmDv6+jSR7vcsA3CWEuFZKeQMRtW0ORWZW1trDhBD/7r1/lRBi2/r5Lft9w+/O4OUGxd8nhPgnM9/CzNcvXbr0zu23376pG+K7775b77333vsDOFwI8Upm3lMIMX3j/eY5Bw9eJ7sWPiqEuME599Moiu5qJr5mMPN0a+2ezHwQgAMB7MnM20ZR1DXEsg3fewCbvP/GGE9Ejwghfmmt/VEcx/9o2wsZwbhKsNauXTutq6vrY0R0hpRyCrCh9aOp7RER6l/qrOUh1+ttdYJljDmMiD4rpTwE2DDxatPbG/y6vPcPeu8/pbW+erRxNsLMk4wx7yOi9yulZgGj+1yA2pciu6v2AC6z1n6yXC4valHIGzDzdGPM6UT0DqXULtnfwTk3qvgHG/x5OOfWOOcujaLoy9ldVUsx82Rr7UEAXgvg5cy8m9a6e9DvW/ba6p8RADjn7mHmT1lrnVLqVACvVkrNqC/bquMh29YSZv6K1vq7o3mcUalU3lsqlb5RZJ00Tb8Yx/EnmtkfMytjzANa653zrmOtfb3W+lcNtlc2xjyktZ638e+898i6ERSilNokud1YmqZXx3F8bOGNo5YUOufeCeA9Usrdsr8rfEzWb2LqsTrnljHz5c6577ay5ZuZyVp7DBF9iIgOFkK05Du00fEMIrrPOff1OI4vLhDbNOfcW5n5FAD7KaUkMPwNeRGDz1vWWkNEv2Xm/23HDW9287GHlPIw7/2rAewvhJg9+Ea1/p63+HVVAFymlPo8ET0xqg03E0und9jIwMDAy7TW31VK7dHsyaNVWpVgMXPJWvs5AB9USqnRXoSGorWGc46dc/8XRdHHW92vJUmSfYUQ31VKvYSZkacFsIj6l8E596Qx5sxyufybVm3bGPNmIvqilHLH7M+t2nRDUkoIIeCcW8TMH9FaXznabWaPul5mjHkLEb1GSjl/0IVnVMl6XlnLBgshSCnV9AU+j3oS4Jy7Xkp5OhGtaGY7lUrl3aVS6VtFWrDSNP3vOI7/q5n9ZQnWfVrr3fO2YFlrj9Ba/7rB9srGmIVa6wWdOHbrMaVpemUcx8cXXTdN0wOEEN+QUh4MtPb7Vj8mrLVrAVyolPoSEa0azTar1eouSqn/EUIclW275edn4PlzXJqmf47j+GV51knT9FAiukgptQAY/Q1M3hizG97LlVIfI6InR7tdZt7ROXccMx/LzPtqraPs71t6k9tIPdF1zj1trX1/qVQa9fm40P47ubNGkiQ5KYqiG5RSexhjxjS5apV169bNMMZcpZT6CBEpY0xbDqbsJEZRFP2HMebS7Fl2S1Sr1TcqpX6rlHqJMablyRWwoY8ZpJTztNZXJUlyYiu2m6bpuUR0uZRyR2NMR5IroJbwZK9nPhH93Bjz+exRXlOq1eqxzrlbnXN/1FqfIaWcX9+HMaYjyRVQO86EEFT/73Z+R621sNZCSvlGa+3VzDytbTsLWsIY82YhxO+klAe34/tmra0fg5OVUh9K0/SI0WwvSZITtda3SCmPqn+f2nWxH7TdpMBqBymlFtTfy3YnIvXzMAChlDrJOXdTmqYvaXZ7lUplB2PMpc65u6WUX1JKHSiEiOqvp90JY533vn4+nqO1/mmlUjm97TsdZMwTrCRJjpdSXiKE6OnURbDdmHlauVy+Smt9RCcugvWDSGt9kjHm26O5oNdVq9WjpZSXCSFmdOJzyU6eXUKIi40xrxvNttI0/YDW+jMAaKyOqUEnq09Za7/WzGfCzCSE+JSU8iX1E2CnTkwN4unYvuuvVyl1sDHmG8w8blrbgxdKkuQkIvqxEGJqu79vgxL7po+HarX6USnlT4hoVieSlyaNyUi/QTe8C4jo6iRJ9m5mO1LKI5VSbyOiyZ2+GRxKdlxqpdS3jDGv6tR+xzTBStP0QCnlBUKIuB2tI2PhiSeeKKVpeqlS6pBOX9yttdBan5okyQdHs500TV8qpbxEStnVyddgrYVSqgzgokqlskMz20iSZA8i+ny9n8JYyka3Qin1XmNMs/160uzfrQ1ugshasv49SZKjxjqWYFNpmh4shJgw5/BqtfrxOI6/DEBOhHjHSnZzs7UQ4gfLly/vHnmNTTgA4+pplHMOSqmYmb+5evXqyZ3Y55glWMw8GcCFUsrJm9OBPmfOnP+KougNY9FyUh+1JaX8TJIk+za5jRlEdLFSakw+l+yLPVsI8bVsqHFR5yilusfLF7ve10AI8elO3jltLpi5PjL4o1lftGCc4FrtuO8rpbonwjk8SZK3KaXO61S/xYkuOxe/aOrUqWeNdSytkj3p2a27u/sdndjfmCVYaZp+TGu9VysSkfrw08GlDMZCmqaHSCk/3OzoHq31Jj9FhjYDG7L0bgBfaOaxlDHmPKXULkU/FyIaMn6tdeHPJGuJOzJJkmOKrMfMMRGNuyQmS3o1M3+VmSeNvEYwmLUWQoiDjDEHjHUswfOSJDlDa71nM+fwRue7+jkv54DvIrHuKYT4uhCCQnKVX9Yt4F1cqwG2OTmNa8VZ22pM7gir1epOQoh3N3ugDx4CCwBpmnrUitV5ADqKIl3/XafuVrI6V1+UUkZFTjj1UWfGmKcA3MHMjzFzHxH1ENEOAF6ktd6uyAi+7LHK4dbawwD8Pm8saZr+mxDiHUUTxKyejzfG/B3A35j5aSLyzDwTwJ5EdIDWujtv/yFmrifNn2Dm64koV+fQgYGBGVrrrZv5vAcP7R1Os8dTdue0jzHmZADfL7yBJmz8PdlYK78bGw+r39hoRh0yM7TWMuvYfMcowpyIFIBNjs1mH4HnvGEbseWYmbvSND2jmf0TEYwxTzHzP5n5KaoV7hVE1MvMc7Pah3NbNVo1G935f0qpKaO5oa+fq4fTqevN4EaFkYwmpuxmd0GSJIcC+GVTGylopHMJMPrXRER7DAwM7Afg9ibDzGVMEiwieo9SqreZVpJsKGlqrb3de38TM98rhFiqtV6XTfvRZa3dlplfRESvBnCg1jpud+dga+0bpZSHFGkqz4Zpr3LOfa5arf6st7d3k2HHzDw1G+b6ea311nneM2aGUkqkafo+5EywmJnSNP2ElFIX+Vyy5OomAJ/RWt82VN0iZt7NWvthIcQ789Zvyfou7WetPRzAdXli6erqitM01UU/5+w1WGPMwwD+ycxPMvOzQogKahWbu5h5LmrJ4r5a6+5RfMHfzcyXUhur8Gtdu7+w1q6z1j7MzI8R0TLvfVUIETHzLCLaiZn30Fr3jPYiprVGdgOwGMBCZl4khFgLQDLzdGZeQES7a61nAc0P3yeig5sOcmJiZl6CjQZrEBEDmEy1yt1FNuaNMc8g6x8zlCiKAGDEgp7GmBdLKXcucr7LyrEs8d5/KoqiXzYqgcPMU6y1+1trTwRwnNZ62mhKxDjnjlVKvbqZ9QffeBljnrXWPsbMTxDRciHEAADhvZ9CRNsA2AHAdlrrLmB0NxUjxZJ1PVjlvf9XdlO+hJlXCyFS1CrkTyWi7Yhod67Vyis1G09WpPQ1aHOCNajEDay1zxDRY8y8iJlXoDZtXEvOXdkNm3DOvQybW4LFzDOMMScWvTjVs3Xv/S+UUl8mouGqzv4DwI0APpckyX7W2tOJ6FQA5XYkWcws0jR9r1IqdxKXJYpPeu+PieP4b42WI6LnAFzY19d3F4BfSim3zXNAOedARK+uVqu75CnMZ4x5kRDiNUUTRGPMxVrrdw2XMBDRPwGclqbpY0qpL+Spf1JvxfLevxM5E6xmZCf9awF8SWv995Fay5h5J2vtOwC8T2vdXSRZyFoW97LWvgzA70YVeANZ/ag7AFxkrf19Nl3HJl+27L3dwRhzEhF9QGs9vZkbHgDWGPN7IvqW1vrP1GBWAWae6Zw7AsBHtNa5pqwaYhs7LV++vHu8zmfZBkkURa8DILLEp47TNP1PrfUnCk6V0x9F0WGoVUhvaNWqVSOeBLz3h2itc4/SzS6ca7z3Rw93vgMAIloD4I8A/lipVL4E4HQAp2mtZxW9bjBzlKbpR6SUhW+wsxtga4y5kYh+pLX+C4BnsgR3qH3FSZLMd8691Ht/JIBDtdbTC+10CPWW6GzQzD8B/BrAb5VS9wJY3iieLCaVpumu1tq3MvPZWutmW/H2Z2Yabl+jkZ2PlnvvfwLgeq31wkZ1zrIRxQuyc9cHtdbTmnlNQoj9Rhn2yPto9w42lqbp4VrrWUWyzvqJ3Fr7ISnlm0dIrl4gjuO/a63f7Zx7omh/przSNN1HCPFveV9TlihWrbXvGOlkU9fd3X2fc+59zOzz9E/w3kNrXRZCvCnP9pn5FKVU7tafrCDhrSMlV4NFUfRFY8x1eT8H7z2I6BVZ61HLZcnV7Q899NAJURTdnudRJBE9qrX+JDMf7pxbUuSYqnfY9t4fN6rAG8iqUBsp5duVUueXy+XHh0qu6rEQ0b+iKDrPe3+YtfahestXXtkNxUNa6yO11jc0Sq4AgIhWKKUu7e/vP9QYc23RfWXfrWk9PT1bTE0sImIi6qPaXGuDf9YzczMtoAxg7RDbe8HPNttsM+KcmkKIPYrsOEtwfpX3fFdXLpef0Fp/0hjzYu/9F5m5ftHNde2y1r5cSnlA0darrIDz3wAcHkXRm7TWVxLRsuESDCJKSqXSw0qpS6IoerMxZl/n3JnW2lt8LTMsFQoCG1qHq865K7z3RyilXqS1/g+t9a+JqGGyNygmG8fxQq31x5n5Fdba+4t+97JrwrYA2tZ/NHscfKHW+kNa65uHKyKbfS8Gn7seLvqaAICZ57e7/EvHEywiemPRdbLWns9GUfS/zeyzA6OPjlZK6bx3V1ll2UvL5fJNRXYSx/E1zHxrwYv660c6iLKO12/Im1wJIeqPaT9a9FEXEZ2XTcsw4rLOOWitJ6dp+ooi+ygQC7z39+y5556Fq99HUXSb9/6t3vuBIp34s8TmFdzCgrAbbx+1C2lucRzfz8wnW2v7mhgkEiFHn526yZMnP6u1frsx5oGiySmAcn0KraDpOlCtOufPLroCMy9tdmddXV1LpJSfkFIe5L2/AECuCYqdc2+VUlKR1qusZf46KeWrRzNtzKRJk5YqpS5QSr3SOXcoEf2owAVdAYBz7kpmPkQpdUKWVDU9oXwcx/c5546z1j4zXN/MjWXnrF4AzZRrKKLwM9w4jv/R7LmLiKah+GTwhXQ0wcpGIhxU9GBP0/SuOI7/p32RNS8bqffavMtnHdqrAL5VdF9ExN77H+VdPmsB2gfAnOGWM8bsK4TYPm8LXNZc/YdJkyYVfn6ttb7be39XkYvreBwZCABRFN3qnDu/yMkqe48XpGm6Y9sCa+LiG0XRPcz806In3ixRLJQsEtFaZv5S0RiFEIqI2pKYBvkxM3nvN5mUdyRENOpjnogel1KeGUXRNSMty8y9QojDil5vrLV3aq1PybpnjBoRcRRFf46i6Pt5H7Ex89Npmp6qlDo+iqJ7WhEHAJRKpUeZ+X+L3hQC0GiiBa6gpm4aoii6m5kvK3ruQu28tfkkWGma7kRE2xR5PMjMkFJ+jVo8x16rVCqVbYlo9yKtV8x8VxRFDzSzP+/9zcaY/ryjR5RSU6y1I1XjPUQpVeguD8DPiyxcl40u/G3B1V7EzG39IjTLe/9tY0zuu6esg6UG0FSF5HYioiuzpLzIOhIFWrDqoij6nTHm2SInxew9bqY2WtBiUspCnaGyWnCvTtO0JQMViGjETjfGmL2JaG6RrhvW2sQY8wEiWjfqIEchiqKfxHF8STu27Zz7RZFzVoYwDmZ+aaSZcxczy7Vr17b16Van37DdlVIq74VcSgljzHIpZcsmAG41rfXuSqnegn3K/tBsZ8Hrr79+EYB/5b0wERGYeaTOfC/Ou38hBNI0HYii6Na862yMmW/Lm5BmHeK3wwitcGOlXC4/xsx/KZIoZPZsRzyjYYx5xFrbV+QkNQorADw+lnXrguZkLemFHlV57yGE6CaiK621p3Jn6sEdWOTGMWuZ/3VXV9df2xzXiNrVmRwASqXSEgCLi7b4VKttG/g8atbah621/R06d+XW0VGEQoidCi4PIrqbiJ5tV0yjxcx7FMyaQUT3c/NFztgY8y8UawFpeDFnZm2M2SXvhrKT0BIAq0bxGp5K07QipSyPlGhlJSe6rbULACxqcn9txcx/AHB4kXVa8bik1crl8npjzHohRHe7a/kQEadpOm6/18HwiOjpoutkpVdmE9FFzrkPpWl6udb6WgALiagdUy/sW3QFKeXP2hBHSzGzXL9+/dSenp6tjDE9qD26IwCJ977PGLO6u7t7daNWPiIyxpghS2Q0kiV843a+LmNMXxzH64UQk8ZTIdmOJlhZS0TRde5tQygtw8yFksZsNMv/M8Z8Hs09c2Zmnl1kVAwRbT/MENupAHIPf87qic01xvwVTcSfJZiSiHINCqiPvLPW7oDa0O1xR0r5t3rzdN67ZWae085hz01yGKZGUqsx8/ifXyVoZGEzK9XPW1rrPaSUnzXGfJKI/pHV0vtjtVr9W09Pz4rRBse1un655zPNWub7vfd3jnbf7dDf379tHMevZObDjDF7l0qlbdI07QVQklISsOHcmsRxvN5a+7Qx5p/MfAeAW7OyBxuaoIp+95oZPNNJPT09Lk3T8TE/2iCdroM1o4l1Hm15FC1ERNsWXUcptf1omjKLFLnMOiJvvWLFikkAhqofNI2ZcxcszBKeLqXU7vkj3lTRuiVCiMLJeacopZ6st8jlfR+JaOqiRYtiAOO33T0IGvuLtbZ2h9BEbcH6918IEUkpX4RaP8uPlEql5Wma3imEuFFK+VsierzJ+MpEtHXehbOR3U+XSqVha4R1WpIkewgh3g/gGCnlhppa9YLN9Z86KWVMRLEQYjpqTzlONMY459yDzrnrrLWXxXG8EE3cSJVKpXGbYI1XnW7B6s27bL01QAjxTDtjGg1mJmvtVkXX6+TEqNkXcHJ3d/dkDJFgGWO2yirkFtpmpyezZuZx2Qcr8xyAvrwj3LL3umf+/PkhwQomJK31PcaYB5stGls3eMofqk3/MksI8UYAbzTGrEvT9E/M/MMoiq4vUhKmr6+vJ4qi3iKPi4QQy4uWnWkXZpbGmA8LIT4upZzsvc91zq2fxwf3CSYiKaXcC8BeQogPVqvVqwDMzvveZI0BXKlUir+QLVzHephm5QwKDfPMHketb1NIo/bAAw9oZm53bZBRyb5wk6SUQ8ZJRD1NdNDuOGYedUXkNkoA5BrZCWxIekuoDX0OggmHiCoAvtPKbXJt+hcYY2CMgRCiV2t9ZBRFlzvnbjHGvCLvtpRSXQBKBW8cx8UMAcxcMsZcpLX+EhFNNsaMasqd+g2xMQZE1BXH8clCiD2a2GZowSqoYwnWFVdcQdmQ7ty49u0Yl+UZAKC3t1cCaPuM3KORXcy1EKJRIlgq0ndoDI3n2dwdgKK38bK/vz8kWMGEpbW+OE3TvzRTRTuPequNMQZSygOFEDemaXp2nnWJKCaiQk9oeBycBJmZkiT5ptb6bdbals5lCDz/njbREXxcd3IfrzqWYB1//PHNfkDjdhx31rlw3MZXl8U55OMr59z4b74CIIQotXtag1Hgop3ViQiTJk0ar68nCEZERBVmfqdz7ol2JVl12eOxshDiO2manpVjFZE9NcmNiMb8Ztk59444jk/Pnt6MdTgvUC6Xx1dAE0AnkwNm5kKtUVli0O7qsU2bM2eORwdHXTUrK3fRqFDnhBjJlRUaHZfJ7KpVqxQKPu7L7pbH/bETBMMplUqPOOfeaK39h9a6UKHHorIJ7EkI8bU0TV8+0uIAijbTTC2alLUSM0/33n92447rzWrlZzHeyzSMVx3r5E5EnCRJ7uJ0Wf0jMPPUdsY1ShZNdFJWSrX1RDQU7/2QnzUzV7LhvYW+1O2+Yx1CjObnX2urOI5jZs5dfyX77FOM48ffQZBXHMcPrFu37tXlcvlLAN6htRbtaoHJ5ictWWv/j5kPyfqCbYKZk6wOVK4b9CzW6c8991wPgIaTlreTMeYtWuu5zQwaGOqaUj+vb8x7X/jRIzNjYKDpaRC3WJ0u01D4wGXm+W2IoyWIyCZJUug1KaXgnPs6gD9T57Isqlar9w31Cynlc8YYFkLkiiUbzrzSOfch51y1Qx3kKZsXbFy2+GitpzBzT4ESDSCi9ah1jg+CCa+3t3clgNOMMZc55z5CRK9U2YSjRcrK5GGMQRzH+1trDwdwzVDLlEqldWmaDgghevLsO1tmZldX1zYYgwSLmUWapicWXU9KWZ/fdjGAPwP4OzMvEUKsQa1fqHLOTRZCzCainb33+xLRnkQ0pUgCHFqwmtPpBGt5E+vs0/IoWqhoRWMigve+L47jK9sVUxFa65VpmvYTUXeeL1y9xICU8i9KqWZr1GxWlFLbCiFGrEo/GDOvBhDGPY8/TT8ieuCBB8TOO+88Lh9jd4rW+ncAfsfMB1lr30xEr2LmnbXWG6bGaVXCxcxvRoMEC0CfEGIVEc3Ksy3vPbTWcZqmLwLw4KiDK6hSqWyjlNqryPuSzaqxjpn/S2v9o7yTUzPzPGvtNUqp/fKWDGJmdHV1hQSroI6eDJh5ccHlAeCgUUzJ0nbM/FjB5UFEb+TxM3nxSiJambfEQHYiKhlj3tTmuCYMZt5HCFHokQgRLRlnVdw3O0UnJM40PVp11qxZMTPnLjab2SyPASK6Q2v9ESnlgdbafZ1zJ1hrv2Wt/bv3PtFaQ2uNvOedBg5odG0gorTo9SZb76jRBNQsrfVOUsrcdbuyLh0GwKlKqW/kTa6ydZ9k5tUFJ3UPLVhN6PTdVqGq7Fmnxl0qlUruyYg7jZmHfPTWSDar/J7VavWV7YqpCCLqB/BI0fkUvfdvf+SRR8Zt4ttJzHxYE+v8sx2xBM8TQhTuH0lETc8YMH369KkAtspzkRzU53Gz7odHRLZcLj+mlLpCa/1epdRBzHyAc+79zrk/eu/TZvpzZpXMZ2OY2UGaOTcT0WuLTn/WCt77bYvcpGVdTf6otb6qyV0WuvYzM/r7+5vc1ZarowkWET2cpqkpUpBRKSWVUue0ObSmxXF8rzFmoMhrklIKIcQnmXlc1EEiotuLLG+tRRzH+8ydO/dt7YppohgYGJhHRC8v+siDmf/WppCCjLV2TZHls9FbezHzpJGXHnJ/eyqlcvX5AQBjjB8vxS07hYhMHMcPKKW+oZR6FYCDnXNXFU2ysvd4UpqmDQdBCSFuK9Ka6L2HUqrHGPPJQsG0ABFNbmKdm9sQSkPjoU7YRNPRBCuKoieIaEmRZuGsxefNxphXjWLX3MaD4wlmfqBIZ29jDKIoelmSJB9vU0yFGGNuyuYVy71O9qjwv5Mk2bNdcY3julcbSCnP0VpPyTsqJ+uQuj6O47+3ObQtnvf+GWNM7uPaOQcp5fxqtfqyZvbHzMfnPbdlMVWcc6ua2VdR2emvZXX7BgYGtmvF9zOKonuklCcYY37RREuWHG56qmq1eqe1dmWRc7O1FkKIU5IkafnN48DAwDxmbhRMM5/LmlGEUxR3d3eHBKugTrdg9QO4s+iFXAgREdH51Wp1lyZ3HRFR7lFeRRCRI6Lri65nrYVS6tNpmn6wlfEkSbK3c+7/GWNel3edcrl8l/f+sSInouxiNFMIcWWSJHs1FewQmLmbmY9JkuS76Ez19qYPijRND5FSvqfIkGcpJZj5XgBLmt1vkE+5XH4SwNqCrcuklPrEwoULC/WR7O/vfzERHV8k0QawYt26dSuL7KdZWd/PUpqmhedOHYrW+oPW2p/19/dvM9ptEZFzzn03e0RXZD2f9UMaUk9Pzwrv/R+K3NBn75OQUn7HWnty7hWHkSTJvsaYS6SUP0Xja27hAS/MPOr3vuguO7y/Ca/jI16Y+Yai61hrIaVcoJS60Rjz+qLrJ0nyASnl3CIXwiwZy3VAaa0vt9bmfkw4aPtSKfW/xphLKpXKDrlX3sjAwMA8a+0ZaZr+TghxhxDiQ8aYmXnXJ6IBAJcXrRphjIFSahchxO+TJDkjm1+vsIULF0Zpmr7EGPNl59zdAK4iomOa2VZRzVZvTtP05UR0mRCiu+jjQSK6hohaN249aGQ5gMeLfC+zY/plu+yyy9fzHs/VanX3KIouVUoVrYX28KxZs5p6RCiEKFQgmJmhtdZEdGiOZaeMtEz2OO3EOI7/Yq09fbQDkaSU6733uS/g2ftXjaJozXDLMfMlzrlCrfNZojeJmX9ojLkoSZI9cq/8/H4nG2OOstb+Qghxm1Lq7UQ0CQ2KnxJRoUSbmSGEOOLuu+9utptJ0WSJ168ft9MCj1udLtOAKIp+a4xZIaWcWSThMcZAa73AWnt9mqa/EkL82Bhze6lUepqIXtBRNGuGnW6tfTERvRPAMUWq42Z1iirIWUQ061t2vVLqxCIX26yjJpRSbwdwpDHmCgBXK6XuBbBqqItw1iw/xRizgxDipQCO8N6/REo5NatRBQCQUhaqGRVF0cXW2vdKKXuLfi5KqZlKqfOdc2enafpTZv59FEWPZS2Wm8j6ns2y1u4J4FUAXsPMeyqlBjehtX1W++xxwInGmNR7f1EURf8gomEvXJVKZXul1OkA3q+UmlSkKKCUEmmarvPeN9sxNSggq1P3JwD7F1kvO9ecba3d2xjzf0qpP6E22nbDCSQbBby9MeY41I6FmUULRDLzrYVWeKHCV7vsacCHkyS5NY7je4eIZ4G19ixjzDQAp4+wudodopTbAbjAOXeGtfZrUsrrGn3vR4jtkDiOKe97KISAc27VqlWrhn3EWiqV/pim6V+jKDq4yOdTrxqvlDrVGHNCmqY3AfgNgHu01ktQezyXovY+SAC9AOakaboPEb3CWnuoUmr7+rbqL7PR/rz3i9M09UIIkec6lT0BOWDPPff8IID/yf3CMszczNOB0IJVUMcTLCJakSTJlUKIdxWtJpvNBi601m8A8Abv/TpjzFNJkiwnoj5m9kRUylpvtlVKzSAiNFlVeDGKXeS/Yq09WggRF6yHVJ/MdJoQ4mzv/dlpmq4EsChJkqeJaA1qX2TNzFOstVsz81xmni2lFEAtIRx88mim+CcR/atarV6qlHpv0c+lXktFa72/lHL/NE2NMWZJmqaLAawA0AfAe+8nEdE0Y8w2qH0+U+p3ltbaDa8hq0/YdtnjgF6l1LuNMWdYaxcmSXIXET3gvV8qpVzvnFNEtBUR7QDgIAAHK6WmOOdQ9IKaTVl0ZblcXtSO1xNsioiutta+n4ioyDkgS7IOBnBw9n18Ik3TlcxsAXQZY+YA2F5r3VX/DuclhIC11hDRr4q+nrqi9feA2oVeKTXPe39TmqY3APgHAMPMs4lovzRND4yiqDdN06vzbrP+urXWBwL4qXPun865y51zv9RaLySiEc+haZoeQkQfLXLeyW6CH5wxY8awiSYRGWPMZ51zvyKiXMlLXf1zFUJMklIeidpNMNI0XUNEa1F7rOcAxMw8GcBWURTJ+rqDrzsjnZOjKHo8TdNlUspt8tamyloRz0vTNNZa/79GVe03Zox5LRHtWfD95p6enpBgFdTxBAsAmPmbxpi3NfN4ZfDJTAjRK6XcHcDuGy9Xnw5gFP2ubi5SpyiKonuSJLkwiqJ3N1NEzzlXv2uCUmqGEGK44cdo5gI/kjiOv2StPU4pNSfvl3ywejxSSi2lXABgQaNlW/D5tIT3fkM/Pynl/tiotWPjZK8+G31R2UW1n5n/d1QBB4Vorf+apuldURQdWPRzG5TwD/l9bPY7mLVk/jWKon8UXjmjlHrMGGOJSBX5DmXdLaYIId4K4K2DfzfovFV4xoRBidZuAM611n7KWvtwmqb3ALiXiP7FzMuZeQC1ydHLQogFzPwaZj5RSjmpiXPOb/IspLX+bZqmP9Jav72Zz6t+jgBqiZ2UcgoRbbg5HLxMs+dkIlqTJMntRHRckbiEEEpr/Tlr7dFpml7EzLcmSbLMWpsKIcTkyZN1tVrtEkJMYeYFUspXAjiFiMp5E6z641jUKsMHBYxJglUqlR5KkuRbUsr/HE1F38EHfqvUL4Te+58XXTeKos8YY16jtd652S9aPXkq2orUCkT0dJIkH5VS/pgKzk04WDs+l3Zrd8zZRfXbcRw/0LadBJsgIlOtVr/CzFc0e0y38vtYj4GZv0ZEo9nov5h5iVJq+6KJyXCvZ5SFPwff/EqlVP3m9xQA9RYdh9qjJlVv1XHOochrkFLCGLPWWntN3nWSJPkYgJeO5twMPH9+bgchxE8B5E6wgOfPW1rr/QHsn6apKZVKzwGwzCyMMVIIUQLQFcexBIofz9kx+zRqTyKCAsZsWocoir5ojLlnDCYNHlY2/cD3S6XSw0XXJaJVAE631vZ36jFXq8Vx/JM0Tb8zUeMfj7TWSJLk/iiKvjDWsWyJ4ji+2lp73Xg4ppVSMMb8+q9//WvhkceDEVEfEf2h6MCUTqk/aRj8A9QSLyGEqndrMMYUvrHJCnJe3NXVlbtSe09Pz3IAb7fWrhlv15w6pdSvkiS5r5n46u9l9vRgplJqjtZ6aynlDClljxBCNvt+A4AQ4pYwMKe4MUuwiGgdM7/dWrt0vBzwWmtYa2+LouizzW4jiqJbvfdne+9thyZCbrk4jj9sjLl2vHwuE5lSCtbatUKI07J+G0GHEZHTWr/XWvvEWB7T2fllOTO//5WvfGXxZ/CbusRa68ZrkrWx+kCjIgOONqa1hjHmsTRNv1h03SiKbnfOneKcWz8ez21ZH6pPOud8sy2J9a4X1lpYazfM+9js+5090al473/U1Aa2cGM6MWkcxw8w87HOucVjecATUf3kd5dS6kQiWjea7cVx/GNjzJnMXB2PX+SREFFFa32KMeZqrXWh2jTB85RS8N4nzrnToyi6a6zj2ZIR0ZNpmp5krX1mLL6TWms45/qste8olUqPtGibt3nvrx0PLXOdkL2HzwF4e09Pz4pmtlEqlX7JzMc758bkOBhJqVS6wTn3FSnlmJ93s/5m8N7/XxzHC8c0mAlqzGd+j6LoTinla5xzN2utmxoBNxpa6/q8TldUKpU3ENFTrdhuuVy+2Fp7nHNuyURMUohovdb63621XxNC8Hg8GY1nWmswc59z7tRSqXTlWMcTAJMmTbrTe/8G59wDnfxOZonBCmvtieVy+det2i4Rsff+o8aYsUgWOnbtqN8AO+eeYubjoii6bTTb01r/xjn3WufcXzp8zcmVCUdR9Kk0Tb+nlOr49bBOSll/nP2TKIo+NyZBbAbGPMECACJ69Omnn369c+4TAFZ14qCvz+bunFtijDlbSnlSb29vS6sql0qlG40xhzrnrhRCoN0n9fqJqP7HFmyvqrX+oDHmBOfcY/VktJ2yUgYYbeHCovtr5fay4+pR7/1RcRz/rGUbD0YtjuO/SSlfZa29hIh8O7+Tg84xNzvnXlUqlW5s9T7K5fK/nHNvs9au62SSJYToA2qvcbQd4xupn8+ymlfXGmNeqbW+qRXbjuP4finla40xn2LmVe06t9XPB5mHkKOWFBHZKIrenSTJfzJzfydvBqSU9ZvDAWvt57TWpxJR0pGdb4bGTdvyvHnzKgC+yMyXW2vfR0Qnaq1nAc8/Vx6NenPnoLpLTzDzJVrr85VSz4z6BTRQLpefAHB8tVp9k1LqI0KIf6vPmj7aMgVEBCHEhhOcMYattQ8IIX4RRdHvW/QSUCqVrmTmm7337/Hen6m1ng1gw/P90cjKUmz4s7V2nXPuz8x8MYC2Tt9ORPDeP8fMPVEUqdF8JlLK+oWgaoz5odb6XCIa9XFFRBoA8l48syKuozkbRxsl6iMuP4p9FXptAOCcG/XVnIiWAzjVGHOZMeY/iehQrTW14jtZPw6yWB/x3n9Na/0DpVQ6wqpNK5fLvzPGHO2cu1BrvaAF58sR7277+vq+0tPT8w8AJ3vvD9Va9wCjP1cPPh9ko91utdb+b6lUuqbpjTbe1wCA8yqVyk+Z+d1E9Bat9Rzg+dF5zRwLQogNDQTW2sQ5dzMzf19rfWPejuLZcl9O0/QPAP6Lmd+gtZbZNltW3maI6+I6ANd77/8viqJ7RrFpCRT7biPHcTeCTp67chk3CVYdEf0LwPuZ+cvOuaO898cQ0QFa62kbL9voC1BPPAbLOvw9CeDPRHSNUup3WRHPjiiVStcy8w3W2sO896cAeJWUcvbgO5NGHUCzonqb3MVkJ4FVzLyQmf8E4DdKqb/lKe5XVDZC8jN9fX3fz2q1nOi9P0Br/YLJVof6TBrFDwCm5ikiupOIfmetvalcLj/e6viHopRCmqaXEdGPrLVnAnitUmrO4GWG+kyGej3W2hXe++uZ+XtRFN3dwjD/AcCn6cjXZyEEvPcWTcxrlnHMfLf3fmaeYfPZhXAlgGY7bD8IYFre15a1bI6qf+RgWuvfMPPvrLUv996/1Xv/GiLabqjaZ3mPaWvtGufcHcz8syiKrlFKdWRgg9b6pv7+/peXSqVPMPPJWuveeuwjJQobX2QBjNhNYurUqWsA/BzAzyuVyo4AjhBCvNF7/yKt9dTByw53XhvqPG2tfUII8XtmvkwpdYtSqq01a7Kb4A8z8/84544EcLT3/qBsrtVNls8KFDfcnjGmD8D93vtfM/N1Q1XNzys7l7wpTdOXZnMjHs7MC7TWLwhguMEDw51/s/f7GQD3CiF+Za29sVwuP9ZsvIPiWQbg7jzfbQCIogje+9HMz+oA3FX03GWtbeuxNSE6BjHzNtbafZh5fyLaA8B8Zp4JoJuZy6jdCQvUpiIw2Z3Juqza8WNEdJ/3/h6t9QPjZSQXM88AcIC19mBm3oeI5jPzdGaehFpmLQB4qk3dUgUwAOA5AM8Q0eNE9E9mXpgkycPd3d3LxiB+SpJkFyJ6iRDiQAC7c23y0ckAyllLCgA4qk1lVAHQT0QrmPlpInqEiB6UUt4P4HEiGnWNFWbeIU3T+6SUueaE01ojTdNvxXH83mz96dbaFwM4mJn3BjAfwDQAk5g5plolaE9EVWZeJ4RYIoS4j5n/JKW8lYg6/jkErcO1+eP2EUIcyMz7AtiBmWcxcw8RlQa1DrrssUk/at/JJ5n5QSHEHUqpu4god/mAdqhWq7sopY5n5tcz825ZYdGGyxtjmIiWAvgLgJ9kN59N3aQx81xr7UHe+5dm5+q5AKYC6AIQcW0aM4daUl4hovXZ9+ZfRPR37/3dWut/ENGYTnzHzLOstXsx875EtDsRzfXez2DmbiKSWZJlUasNtRrA0977x6SU91lr7y+VSo9TgULVBeLqNsbsBeDFRLQPgB2ZeTaAHmbuQu1aWG8J8qi9zykRVbIir88CeEYp9TgzPySlfADAI9kNdNBiEyLBGkp2MHVXKpUyEen6xY+ZjbV2oKenZ307WnLahZkVgB4A3cgSrGq16kul0uAEa4BGV5ywrZi53NfX19vd3V1OkkRnf+dKpVI9wRqgnNM5NLn/ZhKsb8dx/J4G26t/JpOq1WpMRDKOYwegumrVqnUjTdMRTHzMPKm/v79n0qRJpeyYpuyYridY62mE+SvHEjNvC2DHNE3nM/PWRNQrhFDee0tEa5h5KRE9NjAw8OiUKVNWt2H/EWrntPp3SGTfIdvX11dZt25d3zbbbDPQ6v22Q5YcatSe/PCiRYvc/PnzzVifk7k2KXl3pVLpKpfLOkmS+nQ9HoD13qddXV2VRYsWVbbffvsJc03cHIy7R4R5Za1UE+KLmUd2kn4u+5mQsuSpbQlUp20On0kwOlSbuLitfQHbKRsV3ZKR0U3uP0WthaflyVunZYnUuLvBzRoSQuI0Do2LUYRBEARBEASbk5BgBUEQBEEQtFhIsIIgCIIgCFosJFhBEARBEAQtFhKsIAiCIAiCFgsJVhAEQRAEQYuFBCsIgiAIgqDFQoIVBEEQBEHQYiHBCoIgCIIgaLGQYAVBEARBELRYSLCCIAiCIAhabMLORRgEQyBm7pJSQko58tIAiChqc0xBEATBFigkWMHmZJUQ4oPGmAgAj7Sw1pqI6G8diCsIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAIgiAINkv/HxTXlzDOm/JfAAAAAElFTkSuQmCC"
+      /></a>
+      <span class="title">report</span>
+      <span class="spacer"></span>
+      <span class="sub" id="run-tag"></span>
+      <button class="theme" id="theme" type="button" title="Switch between light and dark"></button>
+    </header>
+
+    <div class="shell">
+      <nav id="nav"></nav>
+      <main id="main"></main>
+    </div>
+
+    <div class="tt" id="tt"></div>
+
+    <!-- 3Dmol.js, inlined by the build only when this run has a structure; empty otherwise -->
+    <script>
+      /*__THREEDMOL_JS__*/
+    </script>
+    <script id="report-data" type="application/json">
+      __REPORT_DATA__
+    </script>
+    <script>
+      const DATA = JSON.parse(document.getElementById("report-data").textContent);
+      const SVGNS = "http://www.w3.org/2000/svg";
+      const el = (n, a = {}) => {
+        const e = document.createElementNS(SVGNS, n);
+        for (const k in a) e.setAttribute(k, a[k]);
+        return e;
+      };
+      const tt = document.getElementById("tt");
+      const showTT = (e, html) => {
+        tt.innerHTML = html;
+        tt.style.opacity = 1;
+        tt.style.left = Math.min(e.clientX + 12, innerWidth - tt.offsetWidth - 8) + "px";
+        tt.style.top = e.clientY - tt.offsetHeight - 10 + "px";
+      };
+      const hideTT = () => (tt.style.opacity = 0);
+      const fmt = (x, d = 0) =>
+        x == null || isNaN(x) ? "–" : Number(x).toLocaleString(undefined, { maximumFractionDigits: d });
+      const esc = (s) => String(s).replace(/&/g, "&amp;").replace(/</g, "&lt;").replace(/"/g, "&quot;");
+
+      /* ---------- theme: light unless the reader opts into dark ---------- */
+      // localStorage throws outright when this file is opened from an opaque origin (a sandboxed or
+      // data: URI frame), so every access is guarded -- an unguarded read is exactly what breaks the
+      // embedded MultiQC report. Losing the preference is fine; throwing here would kill the script.
+      const store = {
+        get(k) {
+          try {
+            return localStorage.getItem(k);
+          } catch (e) {
+            return null;
+          }
+        },
+        set(k, v) {
+          try {
+            localStorage.setItem(k, v);
+          } catch (e) {}
+        },
+      };
+      const themeBtn = document.getElementById("theme");
+      let THEME = "light";
+      // Push the theme into every embedded report too. The frames share this document's origin
+      // (they are srcdoc, not data: URIs), so their theme controls are reachable directly: the EC
+      // report keys off data-theme like this page, MultiQC off Bootstrap's data-bs-theme. A frame
+      // mounted later is themed on mount via applyTheme(frame).
+      function applyTheme(root) {
+        (root ? [root] : document.querySelectorAll(".frame")).forEach((f) => {
+          const doc = f.contentDocument;
+          if (!doc || !doc.documentElement) return;
+          doc.documentElement.setAttribute("data-theme", THEME);
+          doc.documentElement.setAttribute("data-bs-theme", THEME);
+        });
+      }
+      function setTheme(t) {
+        THEME = t;
+        document.documentElement.setAttribute("data-theme", t);
+        themeBtn.textContent = t === "dark" ? "☀ Light" : "☾ Dark";
+        store.set("dms-theme", t);
+        applyTheme();
+      }
+      setTheme(store.get("dms-theme") === "dark" ? "dark" : "light");
+      themeBtn.addEventListener("click", () =>
+        setTheme(document.documentElement.getAttribute("data-theme") === "dark" ? "light" : "dark"),
+      );
+
+      const SAMPLES = DATA.samples || [];
+      const SIDS = SAMPLES.map((s) => s.id);
+      // Default matches the window the pipeline's R plots use, so the smoothed curves here and the
+      // published PDFs describe the same thing; the Library-QC control lets the reader change it live.
+      let WINDOW = Number((DATA.run || {}).sliding_window_size) || 10;
+      // Reads must span every varying base (plus --min_flank), so counts and counts-per-coverage are
+      // only comparable within a class; pooling them makes a curve that tracks class composition
+      // rather than the library. The R plots draw one line per class for the same reason.
+      const VB = [
+        ["1", "1 varying base"],
+        ["2", "2 varying bases"],
+        ["3", "3 varying bases"],
+      ];
+      // Colour groups the files by role: inputs share a blue family, outputs a red/orange one, so a plot
+      // reads "before vs after selection" at a glance. Replicates within a role are separated by
+      // lightness (kept in a band that stays legible on both the light and dark panel). Wildtype/quality
+      // helper samples get their own hues; anything with no parseable type falls back to the accent.
+      // A sample keeps its colour no matter what is filtered. Beyond 8 samples an overlay would have to
+      // crowd the lightness band, so the comparison falls back to small multiples instead.
+      const MAXHUE = 8;
+      const COLOR = {};
+      (function buildPalette() {
+        const FAM = { input: 212, output: 14, wildtype: 265, quality: 150 };
+        const groups = {};
+        SAMPLES.forEach((s) => (groups[s.type || "other"] = groups[s.type || "other"] || []).push(s));
+        for (const [type, arr] of Object.entries(groups)) {
+          arr.sort((a, b) => (a.replicate ?? 0) - (b.replicate ?? 0) || (a.id < b.id ? -1 : 1));
+          const hue = FAM[type];
+          arr.forEach((s, i) => {
+            if (hue == null) {
+              COLOR[s.id] = "var(--accent)";
+              return;
+            }
+            const n = arr.length;
+            const L = n <= 1 ? 55 : 66 - (20 * i) / (n - 1); // 66%..46%
+            COLOR[s.id] = `hsl(${hue} 68% ${L}%)`;
+          });
+        }
+      })();
+      const OVERLAY_OK = SIDS.length <= MAXHUE;
+      const sel = new Set(SIDS);
+
+      /* ---------- info buttons ---------- */
+      document.addEventListener("click", (e) => {
+        const prev = document.querySelector(".ipop");
+        if (prev) prev.remove();
+        const b = e.target.closest(".ibtn");
+        if (!b) return;
+        e.preventDefault();
+        e.stopPropagation();
+        const pop = document.createElement("div");
+        pop.className = "ipop";
+        pop.textContent = b.dataset.info;
+        document.body.appendChild(pop);
+        const r = b.getBoundingClientRect();
+        pop.style.left = Math.max(8, Math.min(r.left, innerWidth - pop.offsetWidth - 8)) + "px";
+        pop.style.top =
+          (r.bottom + 6 + pop.offsetHeight > innerHeight ? r.top - pop.offsetHeight - 6 : r.bottom + 6) + "px";
+      });
+      const info = (t) => `<button class="ibtn" type="button" data-info="${esc(t)}">i</button>`;
+
+      /* ---------- generic charts ---------- */
+      // Width a string actually occupies once rendered, so margins can be derived instead of guessed.
+      // The svg must already be in the document for this to return anything but 0.
+      function textW(svg, s, cls) {
+        const t = el("text", { class: cls, x: -9999, y: -9999 });
+        t.textContent = s;
+        svg.appendChild(t);
+        const w = t.getComputedTextLength();
+        t.remove();
+        return w;
+      }
+
+      const TICK_PX = 10, // must track .tick font-size
+        LBL_PX = 11; //     must track .albl font-size
+
+      // Rolling mean over ORF positions, matching rollapply(width=w, mean(na.rm=TRUE), fill="extend")
+      // in the pipeline's R plots: centre-aligned, gaps ignored rather than treated as zero, and the
+      // partial windows at each end filled by extending the first/last complete value.
+      function rollmean(vals, w) {
+        const L = vals.length;
+        if (!(w > 1) || L < w) return vals.slice();
+        const out = new Array(L).fill(null);
+        const lo = Math.ceil(w / 2) - 1,
+          hi = L - Math.floor(w / 2) - 1;
+        for (let i = lo; i <= hi; i++) {
+          let sum = 0,
+            n = 0;
+          for (let j = i - lo; j < i - lo + w; j++) {
+            if (vals[j] != null && !isNaN(vals[j])) {
+              sum += vals[j];
+              n++;
+            }
+          }
+          out[i] = n ? sum / n : null;
+        }
+        for (let i = 0; i < lo; i++) out[i] = out[lo];
+        for (let i = hi + 1; i < L; i++) out[i] = out[hi];
+        return out;
+      }
+
+      // Series for one sample: pull `key` off the chosen varying-bases class (or the position list for
+      // coverage, which is a property of the position and has no class), then optionally smooth.
+      function series(s, key, cls, smooth) {
+        const src = cls == null ? s.positions : (s.classes || {})[cls] || [];
+        if (!src.length) return [];
+        const pos = src.map((d) => d.pos);
+        let v = src.map((d) => (d[key] == null || isNaN(d[key]) ? null : d[key]));
+        if (smooth) v = rollmean(v, WINDOW);
+        return pos.map((p, i) => ({ pos: p, v: v[i] })).filter((d) => d.v != null);
+      }
+
+      // Class comparison is only legible in one plot, so all three varying-bases classes are always
+      // drawn together and selection controls opacity rather than presence: a class you deselect fades
+      // but stays for reference. Coverage has no class (classesSel = null) and is coloured by sample.
+      const CLASS_COLOR = { 1: "var(--s1)", 2: "var(--s2)", 3: "var(--s3)" };
+      const selSamples = () => SAMPLES.filter((s) => sel.has(s.id));
+
+      // one line per (selected sample x drawn class); x = ORF position
+      function lineChart(host, key, ylab, logY, classesSel, smooth, hline) {
+        host.innerHTML = "";
+        const chosen = selSamples();
+        if (!chosen.length) {
+          host.innerHTML = '<div class="empty">No files selected.</div>';
+          return;
+        }
+        // build the layers: coverage is one per sample; a split metric is sample x all three classes,
+        // faded where the class is deselected.
+        const layers = [];
+        chosen.forEach((s) => {
+          if (classesSel == null) {
+            const pts = series(s, key, null, smooth);
+            if (pts.length) layers.push({ pts, color: COLOR[s.id], opacity: 0.9, label: s.id });
+          } else {
+            [1, 2, 3].forEach((c) => {
+              const pts = series(s, key, String(c), smooth);
+              if (!pts.length) return;
+              const on = classesSel.has(String(c));
+              layers.push({
+                pts,
+                color: CLASS_COLOR[c],
+                opacity: on ? 0.95 : 0.16,
+                on,
+                label: (chosen.length > 1 ? s.id + " · " : "") + c + (c === 1 ? " varying base" : " varying bases"),
+              });
+            });
+          }
+        });
+        const live = layers.filter((l) => l.pts.length);
+        if (!live.length) {
+          host.innerHTML = '<div class="empty">No data for this metric in the selected files.</div>';
+          return;
+        }
+        const W = 1020,
+          H = 260,
+          m = { l: 0, r: 14, t: 10, b: 0 };
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+
+        let maxP = 1,
+          lo = Infinity,
+          hi = -Infinity;
+        live.forEach((r) =>
+          r.pts.forEach((p) => {
+            if (p.pos > maxP) maxP = p.pos;
+            if (!logY || p.v > 0) {
+              if (p.v < lo) lo = p.v;
+              if (p.v > hi) hi = p.v;
+            }
+          }),
+        );
+        // The x extent must span the whole ORF even when only a subset of positions carry a class, or
+        // the axis silently rescales as classes are toggled. Pin it to the coverage series' length.
+        chosen.forEach((s) =>
+          s.positions.forEach((p) => {
+            if (p.pos > maxP) maxP = p.pos;
+          }),
+        );
+        if (hline != null && hline > 0 && (!logY || hline > 0)) {
+          if (hline > hi) hi = hline;
+          if (hline < lo) lo = hline;
+        }
+        if (!isFinite(lo)) {
+          host.innerHTML = '<div class="empty">No data for this metric.</div>';
+          return;
+        }
+        if (!logY) lo = 0;
+        const yv = (v) => (logY ? Math.log10(Math.max(v, lo)) : v);
+        const y0 = yv(lo),
+          y1 = yv(hi) || 1;
+        const label = (gv) => (logY ? gv.toExponential(1) : fmt(gv, gv < 10 ? 4 : 0));
+        const gvs = [];
+        for (let i = 0; i <= 4; i++) gvs.push(logY ? Math.pow(10, y0 + ((y1 - y0) * i) / 4) : lo + ((hi - lo) * i) / 4);
+
+        // Derive the margins from what the text really measures: the rotated y title needs its own
+        // band, then a gap, then the widest tick. Hardcoding these is what let the centred x title
+        // collide with the centre x tick.
+        // The rotated y title occupies a band one font-size wide about its baseline, so it needs
+        // LBL_PX plus padding on both sides before the widest tick may start. Measured, not guessed:
+        // with the previous constants the title cleared the ticks by 0.4px, which reads as touching.
+        const maxYW = Math.max(...gvs.map((g) => textW(svg, label(g), "tick")));
+        m.l = Math.ceil(LBL_PX + 18 + maxYW + 8);
+        m.b = TICK_PX + 8 + LBL_PX + 8;
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const x = (p) => m.l + ((p - 1) / Math.max(1, maxP - 1)) * iw;
+        const y = (v) => m.t + ih - ((yv(v) - y0) / (y1 - y0 || 1)) * ih;
+
+        gvs.forEach((gv) => {
+          const gy = y(gv);
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = label(gv);
+          svg.appendChild(t);
+        });
+        const step = Math.max(1, Math.round(maxP / 10));
+        for (let p = 1; p <= maxP; p += step) {
+          const t = el("text", { class: "tick", x: x(p), y: m.t + ih + TICK_PX + 5, "text-anchor": "middle" });
+          t.textContent = p;
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: LBL_PX,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = ylab;
+        svg.appendChild(yl);
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 4, "text-anchor": "middle" });
+        xl.textContent = "ORF residue position";
+        svg.appendChild(xl);
+        if (hline != null && hline > 0) {
+          svg.appendChild(
+            el("line", {
+              x1: m.l,
+              x2: m.l + iw,
+              y1: y(hline),
+              y2: y(hline),
+              stroke: "var(--ink2)",
+              "stroke-width": 1,
+              "stroke-dasharray": "5 4",
+            }),
+          );
+          const t = el("text", { class: "tick", x: m.l + iw - 4, y: y(hline) - 4, "text-anchor": "end" });
+          t.textContent = "required coverage";
+          svg.appendChild(t);
+        }
+        // Draw faded layers first so the selected ones sit on top.
+        [...live]
+          .sort((a, b) => a.opacity - b.opacity)
+          .forEach((L) => {
+            const pathPts = L.pts.filter((p) => !logY || p.v > 0);
+            if (!pathPts.length) return;
+            let d = "";
+            pathPts.forEach((p, i) => (d += (i ? " L " : "M ") + x(p.pos) + " " + y(p.v)));
+            svg.appendChild(
+              el("path", {
+                d,
+                fill: "none",
+                stroke: L.color,
+                "stroke-width": 1.6,
+                "stroke-linejoin": "round",
+                "stroke-opacity": L.opacity,
+              }),
+            );
+          });
+        const ov = el("rect", { x: m.l, y: m.t, width: iw, height: ih, fill: "transparent" });
+        svg.appendChild(ov);
+        ov.addEventListener("mousemove", (e) => {
+          const r = svg.getBoundingClientRect(),
+            px = ((e.clientX - r.left) / r.width) * W;
+          const pp = Math.round(((px - m.l) / iw) * (maxP - 1)) + 1;
+          // Deselected (faded) layers stay out of the tooltip so it reads the current selection.
+          const parts = live
+            .filter((L) => L.opacity > 0.5)
+            .map((L) => {
+              const d = L.pts.find((z) => z.pos === pp);
+              return d
+                ? `<span style="color:${L.color}">&#9679;</span> ${esc(L.label)}: <b>${fmt(d.v, d.v < 10 ? 5 : 0)}</b>`
+                : null;
+            })
+            .filter(Boolean);
+          if (!parts.length) return hideTT();
+          showTT(
+            e,
+            `<b>position ${pp}</b>${smooth ? ` <span style="opacity:.7">(rolling mean, w=${WINDOW})</span>` : ""}<br>` +
+              parts.join("<br>"),
+          );
+        });
+        ov.addEventListener("mouseleave", hideTT);
+      }
+
+      // position x amino-acid grid for one sample
+      function heatChart(host, s) {
+        host.innerHTML = "";
+        if (!s || !s.heatmap || !s.heatmap.cells.length) {
+          host.innerHTML = '<div class="empty">No heatmap data for this sample.</div>';
+          return;
+        }
+        const AAS = "ACDEFGHIKLMNPQRSTVWY*".split("");
+        const cells = s.heatmap.cells;
+        const maxP = Math.max(...cells.map((c) => c.p));
+        const vals = cells.map((c) => c.v).filter((v) => v > 0);
+        const lo = Math.log10(Math.min(...vals)),
+          hi = Math.log10(Math.max(...vals));
+        const W = 1020,
+          m = { l: 26, r: 10, t: 8, b: 26 };
+        const cw = (W - m.l - m.r) / maxP,
+          ch = 11;
+        const H = m.t + m.b + ch * AAS.length;
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.appendChild(svg);
+        const VIR = [0x440154, 0x414487, 0x2a788e, 0x22a884, 0x7ad151, 0xfde725];
+        const lerp = (a, b, t) => a + (b - a) * t;
+        const hex2 = (h) => [(h >> 16) & 255, (h >> 8) & 255, h & 255];
+        const ramp = (t) => {
+          t = Math.max(0, Math.min(1, t));
+          const n = VIR.length - 1,
+            xx = t * n,
+            i = Math.min(n - 1, Math.floor(xx)),
+            f = xx - i;
+          const a = hex2(VIR[i]),
+            b = hex2(VIR[i + 1]);
+          return `rgb(${Math.round(lerp(a[0], b[0], f))},${Math.round(lerp(a[1], b[1], f))},${Math.round(lerp(a[2], b[2], f))})`;
+        };
+        AAS.forEach((a, i) => {
+          const t = el("text", { class: "tick", x: m.l - 5, y: m.t + i * ch + 8, "text-anchor": "end" });
+          t.textContent = a;
+          svg.appendChild(t);
+        });
+        const step = Math.max(1, Math.round(maxP / 12));
+        for (let p = 1; p <= maxP; p += step) {
+          const t = el("text", { class: "tick", x: m.l + (p - 0.5) * cw, y: H - 8, "text-anchor": "middle" });
+          t.textContent = p;
+          svg.appendChild(t);
+        }
+        cells.forEach((c) => {
+          const ai = AAS.indexOf(c.a);
+          if (ai < 0) return;
+          const r = el("rect", {
+            x: m.l + (c.p - 1) * cw,
+            y: m.t + ai * ch,
+            width: Math.max(0.6, cw - 0.4),
+            height: ch - 0.6,
+            fill: c.v > 0 ? ramp((Math.log10(c.v) - lo) / (hi - lo || 1)) : "var(--na)",
+          });
+          r.addEventListener("mousemove", (e) =>
+            showTT(e, `<b>${(s.heatmap.wt[c.p] || "") + c.p + c.a}</b> · counts/cov ${c.v.toExponential(2)}`),
+          );
+          r.addEventListener("mouseleave", hideTT);
+          svg.appendChild(r);
+        });
+      }
+
+      /* ---------- sample chips ---------- */
+      function chips(host, onChange) {
+        function draw() {
+          host.innerHTML =
+            SIDS.map(
+              (id) =>
+                `<span class="chip${sel.has(id) ? "" : " off"}" data-s="${esc(id)}">` +
+                `<span class="sw" style="background:${COLOR[id]}"></span>${esc(id)}</span>`,
+            ).join("") + `<button class="act" data-all="1">All</button>`;
+        }
+        host.addEventListener("click", (e) => {
+          if (e.target.closest("[data-all]")) {
+            SIDS.forEach((s) => sel.add(s));
+          } else {
+            const c = e.target.closest(".chip");
+            if (!c) return;
+            const id = c.dataset.s;
+            // click isolates; clicking the isolated one brings everything back
+            if (sel.size === SIDS.length) {
+              sel.clear();
+              sel.add(id);
+            } else if (sel.has(id) && sel.size === 1) SIDS.forEach((s) => sel.add(s));
+            else if (sel.has(id)) sel.delete(id);
+            else sel.add(id);
+          }
+          draw();
+          onChange();
+        });
+        draw();
+      }
+
+      /* ---------- plot downloads ---------- */
+      // The report's own plots are what you download - PDF (vector, publication-quality) and PNG (a
+      // high-resolution raster for slides). Both are generated from the live chart, so the file is
+      // exactly what is on screen, including the current selection and smoothing.
+      function dlButtons(name) {
+        return `<button class="act" data-dl="png" data-name="${esc(name)}">PNG</button>`;
+      }
+      // Inline the computed colours: the chart paints from CSS variables that do not exist once the
+      // file is opened on its own, so a naive serialisation comes out black-on-black.
+      function inlineStyles(src) {
+        const svg = src.cloneNode(true);
+        const from = src.querySelectorAll("*"),
+          to = svg.querySelectorAll("*");
+        const PROPS = [
+          "fill",
+          "stroke",
+          "stroke-width",
+          "stroke-opacity",
+          "stroke-dasharray",
+          "font-size",
+          "font-family",
+          "text-anchor",
+          "opacity",
+        ];
+        for (let i = 0; i < from.length; i++) {
+          const cs = getComputedStyle(from[i]);
+          to[i].removeAttribute("class");
+          PROPS.forEach((p) => {
+            const v = cs.getPropertyValue(p);
+            if (v) to[i].setAttribute(p, v); // keep fill:none - dropping it would flood-fill the lines
+          });
+        }
+        svg.setAttribute("xmlns", SVGNS);
+        const vb = (svg.getAttribute("viewBox") || "0 0 1020 260").split(/\s+/).map(Number);
+        svg.setAttribute("width", vb[2]);
+        svg.setAttribute("height", vb[3]);
+        const bg = el("rect", {
+          x: 0,
+          y: 0,
+          width: vb[2],
+          height: vb[3],
+          fill: getComputedStyle(document.body).backgroundColor,
+        });
+        svg.insertBefore(bg, svg.firstChild);
+        return { svg, w: vb[2], h: vb[3] };
+      }
+      function saveBlob(blob, name) {
+        const u = URL.createObjectURL(blob);
+        const a = document.createElement("a");
+        a.href = u;
+        a.download = name;
+        a.click();
+        setTimeout(() => URL.revokeObjectURL(u), 4000);
+      }
+      // High-resolution PNG: 3x the on-screen size, which is enough for a full-width slide. The old
+      // export was 2x, which looked soft when scaled up.
+      const PNG_SCALE = 3;
+      function exportPNG(src, name) {
+        const { svg, w, h } = inlineStyles(src);
+        const txt = new XMLSerializer().serializeToString(svg);
+        const img = new Image();
+        img.onload = () => {
+          const c = document.createElement("canvas");
+          c.width = w * PNG_SCALE;
+          c.height = h * PNG_SCALE;
+          const ctx = c.getContext("2d");
+          ctx.scale(PNG_SCALE, PNG_SCALE);
+          ctx.drawImage(img, 0, 0);
+          c.toBlob((bl) => saveBlob(bl, name + ".png"), "image/png");
+        };
+        img.src = "data:image/svg+xml;base64," + btoa(unescape(encodeURIComponent(txt)));
+      }
+      // Render the chart to a high-resolution image once, for either target.
+      function rasterise(src, cb) {
+        const { svg, w, h } = inlineStyles(src);
+        const txt = new XMLSerializer().serializeToString(svg);
+        const img = new Image();
+        img.onload = () => {
+          const c = document.createElement("canvas");
+          c.width = w * PNG_SCALE;
+          c.height = h * PNG_SCALE;
+          const ctx = c.getContext("2d");
+          ctx.scale(PNG_SCALE, PNG_SCALE);
+          ctx.drawImage(img, 0, 0);
+          cb(c, w, h);
+        };
+        img.onerror = () => cb(null);
+        img.src = "data:image/svg+xml;base64," + btoa(unescape(encodeURIComponent(txt)));
+      }
+      // Download is PNG only (high-resolution raster of exactly what is on screen).
+      document.addEventListener("click", (e) => {
+        const b = e.target.closest("[data-dl]");
+        if (!b) return;
+        const src = b.closest(".panel").querySelector("svg");
+        if (!src) return;
+        const name = b.dataset.name;
+        rasterise(src, (c) => {
+          if (!c) return;
+          c.toBlob((bl) => saveBlob(bl, name + ".png"), "image/png");
+        });
+      });
+
+      /* ---------- lazy iframes: only mount the payload when the tab is first opened ---------- */
+      // Reports are held as strings and assigned to srcdoc, not src="data:...". A data: URI gives the
+      // frame an opaque origin, and MultiQC dies on the spot there (it reads localStorage unguarded
+      // while bootstrapping and never recovers). srcdoc inherits this document's origin instead.
+      // Keyed by group+name, not group alone: every per-file error-correction report shares the group
+      // "ec", so a group-keyed map silently collapses all six onto whichever was appended last.
+      const fkey = (r) => r.group + "" + r.name;
+      const FRAME_HTML = new Map((DATA.reports || []).map((r) => [fkey(r), r.html]));
+      function mountFrames(root) {
+        root.querySelectorAll("iframe[data-frame]").forEach((f) => {
+          const html = FRAME_HTML.get(f.dataset.frame);
+          if (html && !f.srcdoc) {
+            // srcdoc parses asynchronously, so the theme has to wait for load; do it once more on a
+            // short delay too, since MultiQC rewrites <html> during its own bootstrap.
+            f.addEventListener("load", () => {
+              applyTheme(f);
+              setTimeout(() => applyTheme(f), 400);
+            });
+            f.srcdoc = html;
+          }
+        });
+      }
+
+      /* ---------- sections ---------- */
+      // [dataKey, title, description, logY, classSplit]
+      // classSplit marks the metrics that are only meaningful within one varying-bases class. Coverage
+      // is a property of the position itself, so it has no class.
+      const QC_PLOTS = [
+        [
+          "cov",
+          "Sequencing coverage along the ORF",
+          "Reads covering each residue position, averaged over the substitutions measured there. Dips mark regions the library or the sequencing reached poorly. The dashed line is the coverage needed to reach the targeted counts per variant (--aimed_cov), assuming all 21 residue outcomes are equally represented.",
+          true,
+          false,
+        ],
+        [
+          "cpc",
+          "Counts per coverage along the ORF",
+          "Variant counts normalised by the coverage at that position, i.e. the frequency at which the library's variants appear. This is the coverage-independent view of library representation.",
+          true,
+          true,
+        ],
+      ];
+
+      // The fitness-coloured structure viewer is built once, up front, into an off-screen holder, so it
+      // is warmed up (WebGL context created, model parsed, already spinning) by the time the reader opens
+      // the Variant effects page. It is then moved into that page's panel on show, and moved back to the
+      // holder before any page switch — so main.innerHTML = "" never destroys its WebGL context, and the
+      // model never has to be re-parsed. `attach`/`detach` are no-ops when there is no structure.
+      const STRUCT = (function () {
+        const S = DATA.structure;
+        if (!S || !window.$3Dmol) return { attach() {}, detach() {} };
+        const CW = [0x3a4cc0, 0x6f8fe8, 0xc3cfe2, 0xf2f2f2, 0xf2c0a4, 0xe26a52, 0xb40426];
+        const a = S.fmax || 1;
+        const colFit = (f) => {
+          let t = 1 - (f + a) / (2 * a);
+          t = Math.max(0, Math.min(1, t));
+          const n = CW.length - 1,
+            x = t * n,
+            i = Math.min(n - 1, Math.floor(x)),
+            fr = x - i;
+          const p = CW[i],
+            q = CW[i + 1],
+            lp = (u, v) => Math.round(u + (v - u) * fr);
+          return (
+            (lp((p >> 16) & 255, (q >> 16) & 255) << 16) |
+            (lp((p >> 8) & 255, (q >> 8) & 255) << 8) |
+            lp(p & 255, q & 255)
+          );
+        };
+        const GREY = 0xcfd5df;
+        const resiColor = {};
+        for (const k in S.resi_fit) resiColor[k] = colFit(S.resi_fit[k]);
+        const OFF = "position:fixed;left:-99999px;top:0;width:980px;height:560px;";
+        const holder = document.createElement("div");
+        holder.style.cssText = OFF;
+        document.body.appendChild(holder);
+        let viewer = null;
+        try {
+          // Frame rate, not fidelity, is what matters for a small auto-spinning thumbnail, and 3Dmol's
+          // defaults are tuned the other way: `cartoonQuality: 10` tessellates the ribbon far more
+          // finely than this size can show, and `antialias: true` additionally *doubles* the internal
+          // devicePixelRatio (see Renderer.setSize), so the canvas is rendered at 4x the pixels it
+          // displays. Both are dropped here - on a laptop GPU that is the difference between a stuttery
+          // and a smooth spin, and at this scale the visual difference is not perceptible.
+          viewer = $3Dmol.createViewer(holder, { backgroundAlpha: 0, antialias: false, cartoonQuality: 4 });
+          viewer.addModel(S.pdb, "pdb");
+          viewer.setStyle(
+            {},
+            { cartoon: { colorfunc: (atom) => (atom.resi in resiColor ? resiColor[atom.resi] : GREY) } },
+          );
+          viewer.zoomTo();
+          // zoomTo() fits the whole model, which on a wide panel leaves the fold floating in dead space
+          // above and below; tighten it so the structure fills the frame. Kept modest so the silhouette
+          // still never clips as it turns.
+          viewer.zoom(1.35);
+          viewer.render();
+        } catch (e) {
+          viewer = null;
+        }
+        // 3Dmol's own viewer.spin() drives the rotation from setInterval(..., 25): a hard 40 fps
+        // ceiling that is also not aligned to the display refresh, so on a 60 Hz screen successive
+        // frames land one or two refreshes apart and the spin visibly judders no matter how fast the
+        // GPU is. It also keeps ticking while the panel is off-screen. Drive the rotation from
+        // requestAnimationFrame instead: vsync-locked (so it is smooth), time-based (so the speed is
+        // the same on a 60 or 120 Hz display), and paused whenever the structure is not on screen.
+        // viewer.rotate() re-renders internally, so no separate render call is needed per frame.
+        const DEG_PER_SEC = 16; // identical to the old 0.4 deg per 25 ms tick
+        let raf = 0,
+          last = 0;
+        function frame(t) {
+          // Clamp the step: browsers throttle rAF hard in a background tab, so without this the first
+          // frame after the reader comes back would carry the whole elapsed time and snap the model
+          // round by hundreds of degrees.
+          if (last) viewer.rotate((DEG_PER_SEC * Math.min(t - last, 100)) / 1000, "y");
+          last = t;
+          raf = requestAnimationFrame(frame);
+        }
+        function startSpin() {
+          if (viewer && !raf) {
+            last = 0;
+            raf = requestAnimationFrame(frame);
+          }
+        }
+        function stopSpin() {
+          if (raf) {
+            cancelAnimationFrame(raf);
+            raf = 0;
+          }
+        }
+        addEventListener("resize", () => {
+          if (viewer && holder.parentElement && holder.parentElement.id === "f-struct") viewer.resize();
+        });
+        return {
+          attach(target) {
+            if (!target) return;
+            if (!viewer) {
+              target.innerHTML = '<div class="empty">Structure could not be rendered in this browser.</div>';
+              return;
+            }
+            holder.style.cssText = "position:relative;width:100%;height:100%;";
+            target.appendChild(holder);
+            viewer.resize();
+            viewer.render();
+            startSpin();
+          },
+          detach() {
+            stopSpin(); // nothing is watching it off-screen - do not burn a frame budget on every page
+            if (viewer && holder.parentElement !== document.body) {
+              holder.style.cssText = OFF;
+              document.body.appendChild(holder);
+            }
+          },
+        };
+      })();
+
+      const SECT = [];
+
+      // --- overview ---
+      SECT.push({
+        id: "overview",
+        label: "Overview",
+        build(root) {
+          const r = DATA.run || {};
+          const nvar = SAMPLES.reduce((a, s) => a + (s.ec ? s.ec.n_variants : 0), 0);
+          const tiles = [
+            ["Sequencing files", fmt(SIDS.length)],
+            ["Protein / sample", r.sample || "–"],
+            ["Reading frame", r.reading_frame || "–"],
+            ["Mutagenesis", r.mutagenesis_type || "–"],
+            ["Error correction", r.error_correction || "none"],
+            ["Pipeline version", r.version || "–"],
+          ];
+          let h = `<h1>Run overview</h1><p class="lead">A self-contained tour of this deep mutational scanning run — sequencing quality, library representation, error correction, variant effects. Every figure is redrawn from the run's own tables and the detailed tool reports are embedded, so the file reads and travels on its own.</p>`;
+          h += `<div class="tiles">${tiles.map(([k, v]) => `<div class="tile"><div class="k">${esc(k)}</div><div class="v">${esc(v)}</div></div>`).join("")}</div>`;
+          h += `<div class="panel"><h2>Sequencing files ${info("Every fastq pair in the run, by selection side (input/output) and replicate.")}</h2>`;
+          h += `<div class="tblwrap"><table><thead><tr><th>File</th><th>Library variants</th><th>Total counts</th></tr></thead><tbody>${SAMPLES.map(
+            (s) =>
+              `<tr><td>${esc(s.id)}</td><td>${s.ec ? fmt(s.ec.n_variants) : "–"}</td><td>${s.ec ? fmt(s.ec.total_corrected) : "–"}</td></tr>`,
+          ).join("")}</tbody></table></div></div>`;
+          // Run statistics from the Nextflow execution trace (Overview), when it was available.
+          const RS = (DATA.run || {}).stats;
+          if (RS && RS.processes && RS.processes.length) {
+            const dur = (s) => {
+              s = Math.round(s);
+              if (s < 60) return s + " s";
+              const m = Math.floor(s / 60),
+                ss = s % 60;
+              if (m < 60) return m + "m" + (ss ? " " + ss + "s" : "");
+              return Math.floor(m / 60) + "h " + (m % 60) + "m";
+            };
+            const sz = (b) => {
+              if (!b) return "–";
+              const u = ["B", "KB", "MB", "GB", "TB"];
+              let i = 0;
+              while (b >= 1000 && i < u.length - 1) ((b /= 1000), i++);
+              return (b < 10 ? b.toFixed(1) : Math.round(b)) + " " + u[i];
+            };
+            const t = RS.total;
+            h += `<div class="panel"><h2>Run statistics ${info(
+              "Per-process compute from Nextflow's execution trace at report time (excludes this report and the trailing version collation). CPU time sums task runtimes; CACHED tasks reused earlier results.",
+            )}</h2>`;
+            h += `<div class="tiles">${[
+              ["Tasks", fmt(t.tasks)],
+              ["Cached", fmt(t.cached)],
+              ["CPU time", dur(t.cpu_time)],
+              ["Peak memory", sz(t.peak_rss)],
+            ]
+              .map(([k, v]) => `<div class="tile"><div class="k">${esc(k)}</div><div class="v">${esc(v)}</div></div>`)
+              .join("")}</div>`;
+            h += `<div class="tblwrap"><table><thead><tr><th>Process</th><th>Tasks</th><th>Cached</th><th>CPU time</th><th>Mean %CPU</th><th>Peak RSS</th></tr></thead><tbody>${RS.processes
+              .map(
+                (p) =>
+                  `<tr><td>${esc(p.process)}</td><td>${fmt(p.tasks)}</td><td>${fmt(p.cached)}</td><td>${dur(
+                    p.cpu_time,
+                  )}</td><td>${p.cpu_pct == null ? "–" : fmt(p.cpu_pct) + "%"}</td><td>${sz(p.peak_rss)}</td></tr>`,
+              )
+              .join("")}</tbody></table></div>`;
+            // Straight into Nextflow's own richer report + timeline, which sit next to this file in
+            // pipeline_info/. Relative links, so they resolve when the report is opened from the results
+            // folder (the location it is published to).
+            const sfx = (DATA.run || {}).trace_report_suffix;
+            if (sfx)
+              h += `<p class="desc" style="margin-top:10px">Nextflow's own outputs, next to this file: <a href="pipeline_info/execution_report_${esc(
+                sfx,
+              )}.html" target="_blank" rel="noopener" style="color:var(--accent);font-weight:600">execution report ↗</a> · <a href="pipeline_info/execution_timeline_${esc(
+                sfx,
+              )}.html" target="_blank" rel="noopener" style="color:var(--accent);font-weight:600">timeline ↗</a></p>`;
+            h += `</div>`;
+          }
+          // README: what this file is and how it maps onto the results folder. The report is meant as
+          // the way in - a reader should be able to orient here and then find the underlying files.
+          const ec = r.error_correction && r.error_correction !== "none";
+          const struct = [
+            ["deepmutscan_report.html", "This report — the guided overview of the whole run."],
+            ["fastqc/", "Per-file FastQC reports on raw read quality."],
+            ["multiqc/", "The aggregated MultiQC report (also embedded under Sequencing QC)."],
+            [
+              "library_QC/&lt;file&gt;/",
+              "Per-file library-representation plots: coverage, counts and counts-per-coverage along the ORF, count heatmaps and log-difference plots.",
+            ],
+            ...(ec
+              ? [
+                  [
+                    "library_QC/error_correction_report.html",
+                    "The sequencing-error-correction report (also under Error correction).",
+                  ],
+                ]
+              : []),
+            [
+              "fitness/default_results/",
+              "Fitness estimates, heatmap and correlation plots from the built-in estimator.",
+            ],
+            ["fitness/DiMSum_results/", "DiMSum output, if DiMSum ran: fitness table, diagnostics and its own report."],
+            ["fitness/mutscan_results/", "mutscan output, if it ran: edgeR/limma fitness tables and volcano plots."],
+            ["variant_effect_inspection_tool/", "The interactive 3D structure viewer, if a --pdb was given."],
+            ["intermediate_files/", "Alignments and per-step variant-count tables — the inputs behind every plot."],
+            ["pipeline_info/", "Nextflow execution report, timeline, trace and software versions."],
+          ];
+          h += `<div class="panel"><h2>About this report ${info("Self-contained overview from sequencing QC to variant effects; detailed reports are embedded and underlying tables re-plotted client-side.")}</h2>
+            <p class="desc">Guided entry point: pages follow the analysis order — sequencing quality, library representation, then (where run) error correction and variant effects. Plots are regenerated from the run's tables and the original detailed reports embedded, so the file opens offline and sends as one attachment.</p>
+            <h3 style="margin:14px 0 6px;font-size:13px;color:var(--ink2)">Where everything lives in the results folder</h3>
+            <div class="tblwrap"><table><thead><tr><th>Path</th><th>Contents</th></tr></thead><tbody>${struct
+              .map(([p, d]) => `<tr><td><code>${p}</code></td><td>${d}</td></tr>`)
+              .join("")}</tbody></table></div></div>`;
+          root.innerHTML = h;
+        },
+      });
+
+      // --- sequencing QC (MultiQC) ---
+      const mqc = (DATA.reports || []).find((r) => r.group === "multiqc");
+      const sdSamples = SAMPLES.filter((s) => s.seqdepth && s.seqdepth.length);
+      if (mqc || sdSamples.length)
+        SECT.push({
+          id: "seqqc",
+          label: "Sequencing QC",
+          build(root) {
+            let h = `<h1>Sequencing QC</h1><p class="lead">Read quality per fastq, and sequencing saturation per library.</p>`;
+            if (sdSamples.length)
+              h += `<div class="panel"><h2>Sequencing-depth saturation ${info(
+                "Expected fraction of the designed library detected (variants ≥ count threshold) as depth is rarefied from 1.0 to 0, by closed-form hypergeometric. A plateau indicates saturation; a rising tail, undersequencing.",
+              )}</h2><p class="desc">Detected variants (% of the possible library) against the fraction of achieved sequencing depth.</p><div class="row" id="sd-legend"></div><div id="sd-plot"></div></div>`;
+            if (mqc)
+              h += `<div class="panel"><h2>Read quality ${info(
+                "Aggregated MultiQC over all fastqs in the run.",
+              )}</h2><iframe class="frame" data-frame="${esc(fkey(mqc))}" title="MultiQC report"></iframe></div>`;
+            root.innerHTML = h;
+            mountFrames(root);
+            if (sdSamples.length) drawSeqDepth(sdSamples);
+          },
+        });
+
+      // Sequencing-depth rarefaction: one line per file, depth fraction (x) vs % of the library
+      // detected (y). Overlaid so saturation can be compared across files at a glance. `sdSel` holds the
+      // files currently drawn; it persists across page visits and is seeded (to inputs) on first draw.
+      const sdSel = new Set();
+      function drawSeqDepth(sdSamples) {
+        const host = document.getElementById("sd-plot");
+        if (!host) return;
+        const W = 1000,
+          H = 340,
+          m = { l: 56, r: 14, t: 12, b: 42 };
+        const iw = W - m.l - m.r,
+          ih = H - m.t - m.b;
+        const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+        host.innerHTML = "";
+        host.appendChild(svg);
+        const x = (v) => m.l + v * iw; // depth fraction 0..1
+        const y = (v) => m.t + ih - (v / 100) * ih; // percent 0..100
+        // grid + axes
+        for (let i = 0; i <= 10; i++) {
+          const gx = m.l + (iw * i) / 10;
+          svg.appendChild(el("line", { class: "grid", x1: gx, x2: gx, y1: m.t, y2: m.t + ih }));
+          const t = el("text", { class: "tick", x: gx, y: m.t + ih + 16, "text-anchor": "middle" });
+          t.textContent = (i / 10).toFixed(1);
+          svg.appendChild(t);
+        }
+        for (let i = 0; i <= 5; i++) {
+          const gy = m.t + (ih * i) / 5;
+          svg.appendChild(el("line", { class: "grid", x1: m.l, x2: m.l + iw, y1: gy, y2: gy }));
+          const t = el("text", { class: "tick", x: m.l - 8, y: gy + 3, "text-anchor": "end" });
+          t.textContent = 100 - i * 20 + "%";
+          svg.appendChild(t);
+        }
+        svg.appendChild(el("line", { class: "axis", x1: m.l, x2: m.l + iw, y1: m.t + ih, y2: m.t + ih }));
+        const xl = el("text", { class: "albl", x: m.l + iw / 2, y: H - 6, "text-anchor": "middle" });
+        xl.textContent = "Fraction of sequencing depth";
+        svg.appendChild(xl);
+        const yl = el("text", {
+          class: "albl",
+          x: -(m.t + ih / 2),
+          y: 14,
+          transform: "rotate(-90)",
+          "text-anchor": "middle",
+        });
+        yl.textContent = "Variants detected (% of library)";
+        svg.appendChild(yl);
+        // Default to the input libraries only - they are what "how deeply did we sequence the starting
+        // pool" is usually asked of; outputs (and helper samples) are one legend click away. Fall back to
+        // all files if the run has no input-typed file.
+        if (sdSel.size === 0) {
+          const inputs = sdSamples.filter((s) => s.type === "input");
+          (inputs.length ? inputs : sdSamples).forEach((s) => sdSel.add(s.id));
+        }
+        sdSamples
+          .filter((s) => sdSel.has(s.id))
+          .forEach((s) => {
+            let d = "";
+            s.seqdepth.forEach((p, i) => (d += (i ? " L " : "M ") + x(p.x) + " " + y(p.y)));
+            const path = el("path", {
+              d,
+              fill: "none",
+              stroke: COLOR[s.id],
+              "stroke-width": 1.6,
+              "stroke-opacity": 0.9,
+            });
+            path.addEventListener("mousemove", (e) => showTT(e, `<b>${esc(s.id)}</b>`));
+            path.addEventListener("mouseleave", hideTT);
+            svg.appendChild(path);
+          });
+        const legend = document.getElementById("sd-legend");
+        legend.innerHTML = sdSamples
+          .map(
+            (s) =>
+              `<span class="chip${sdSel.has(s.id) ? "" : " off"}" data-sd="${esc(s.id)}"><span class="sw" style="background:${COLOR[s.id]}"></span>${esc(s.id)}</span>`,
+          )
+          .join("");
+        legend.onclick = (e) => {
+          const c = e.target.closest("[data-sd]");
+          if (!c) return;
+          const id = c.dataset.sd;
+          if (sdSel.has(id)) {
+            if (sdSel.size > 1) sdSel.delete(id); // never leave the plot empty
+          } else sdSel.add(id);
+          drawSeqDepth(sdSamples);
+        };
+      }
+
+      // --- library QC ---
+      if (SAMPLES.some((s) => s.positions.length))
+        SECT.push({
+          id: "libqc",
+          label: "Library QC",
+          build(root) {
+            // Heading + sequencing-file selector pin to the top while the plots scroll, so it stays clear
+            // which files the curves are showing and the selection is always reachable.
+            let h = `<div class="stick"><h1>Library QC</h1><div class="row" id="lchips"></div><div class="row"><label class="sw-lbl">rolling-mean window&nbsp;<input type="number" id="rm-window" min="1" max="200" value="${WINDOW}" style="width:60px" /></label></div></div>`;
+            h += `<p class="lead">Library build and sequencing quality: uniformity of variant representation along the ORF, per-position coverage, and where representation drops off — the basis for trusting the downstream fitness estimates.</p>`;
+            // One selection of varying-bases classes, shared by the two split plots. Comparing 1 vs 2
+            // vs 3 only reads clearly in a single plot, so all three are always drawn; this set just
+            // controls which are opaque. Default: the single-nucleotide class.
+            const vbSel = new Set(["1"]);
+            QC_PLOTS.forEach(([dataKey, title, desc, logY, split]) => {
+              h += `<div class="panel"><h2>${esc(title)} ${info(desc)}</h2><p class="desc">${esc(desc)}</p>`;
+              h += `<div class="row opts">`;
+              if (split)
+                h += `<span class="seg" id="vb-${dataKey}">${VB.map(
+                  ([v, lbl]) =>
+                    `<button data-vb="${v}"${vbSel.has(v) ? ' class="on"' : ""}><span class="sw" style="background:${CLASS_COLOR[v]}"></span>${esc(lbl)}</button>`,
+                ).join("")}</span>`;
+              h += `<label class="sw-lbl"><input type="checkbox" id="rm-${dataKey}" checked /> rolling mean ${info(
+                "Rolling mean over the window set above (default --sliding_window_size), gaps skipped rather than zero-filled. Off shows the unsmoothed per-position values.",
+              )}</label>`;
+              h += `<span class="spacer"></span><span class="row" id="dl-${dataKey}"></span></div>`;
+              h += `<div id="c-${dataKey}"></div></div>`;
+            });
+            h += `<div class="panel"><h2>Counts per coverage — one file at a time ${info("Counts ÷ coverage per substitution (rows) × residue (columns), log colour; grey = unobserved. Single file only.")}</h2>
+                  <div class="row" id="hsel"></div><div id="c-heat"></div></div>`;
+            root.innerHTML = h;
+
+            const redraw = () => {
+              QC_PLOTS.forEach(([dataKey, t, d, logY, split]) => {
+                const host = document.getElementById("c-" + dataKey);
+                const smooth = document.getElementById("rm-" + dataKey).checked;
+                const hl = dataKey === "cov" ? (DATA.run || {}).required_cov : null;
+                lineChart(host, dataKey, t.split(" along")[0], logY, split ? vbSel : null, smooth, hl);
+                document.getElementById("dl-" + dataKey).innerHTML = dlButtons(`${dataKey}_along_ORF`);
+              });
+              const first = SIDS.find((id) => sel.has(id));
+              heatChart(
+                document.getElementById("c-heat"),
+                SAMPLES.find((s) => s.id === first),
+              );
+              document.getElementById("hsel").innerHTML = SIDS.map(
+                (id) =>
+                  `<span class="chip${id === first ? "" : " off"}" data-h="${esc(id)}"><span class="sw" style="background:${COLOR[id]}"></span>${esc(id)}</span>`,
+              ).join("");
+            };
+            chips(document.getElementById("lchips"), redraw);
+            document.getElementById("rm-window").addEventListener("input", (e) => {
+              const w = parseInt(e.target.value, 10);
+              if (w >= 1) {
+                WINDOW = w;
+                redraw();
+              }
+            });
+            QC_PLOTS.forEach(([dataKey, t, d, logY, split]) => {
+              document.getElementById("rm-" + dataKey).addEventListener("change", redraw);
+              const seg = document.getElementById("vb-" + dataKey);
+              if (seg)
+                seg.addEventListener("click", (e) => {
+                  const b = e.target.closest("[data-vb]");
+                  if (!b) return;
+                  const v = b.dataset.vb;
+                  // Toggle, but never let the last class off - an empty plot is not a useful state.
+                  if (vbSel.has(v) && vbSel.size > 1) vbSel.delete(v);
+                  else vbSel.add(v);
+                  document
+                    .querySelectorAll(".seg [data-vb]")
+                    .forEach((x) => x.classList.toggle("on", vbSel.has(x.dataset.vb)));
+                  redraw();
+                });
+            });
+            document.getElementById("hsel").addEventListener("click", (e) => {
+              const c = e.target.closest("[data-h]");
+              if (!c) return;
+              heatChart(
+                document.getElementById("c-heat"),
+                SAMPLES.find((s) => s.id === c.dataset.h),
+              );
+              document.querySelectorAll("#hsel .chip").forEach((x) => x.classList.toggle("off", x !== c));
+            });
+            redraw();
+            if (!OVERLAY_OK)
+              root.insertAdjacentHTML(
+                "afterbegin",
+                `<div class="note">This run has ${SIDS.length} sequencing files — more than the palette can keep distinguishable. Isolate files with the chips to compare them reliably.</div>`,
+              );
+          },
+        });
+
+      // --- error correction ---
+      const ecReports = (DATA.reports || []).filter((r) => r.group === "ec");
+      if (SAMPLES.some((s) => s.ec))
+        SECT.push({
+          id: "ec",
+          label: "Error correction",
+          build(root) {
+            let h = `<h1>Error correction</h1><p class="lead">What sequencing-error correction removed. The full nucleotide-level report is embedded below.</p>`;
+            h += `<div class="panel"><h2>Removed per file ${info("Counts removed by correction as a fraction of raw; 'variants adjusted' counts variants that changed at all.")}</h2><div class="tblwrap"><table><thead><tr><th>File</th><th>Library variants</th><th>Variants adjusted</th><th>Raw counts</th><th>Corrected</th><th>% removed</th></tr></thead><tbody>`;
+            h += SAMPLES.filter((s) => s.ec)
+              .map(
+                (s) =>
+                  `<tr><td>${esc(s.id)}</td><td>${fmt(s.ec.n_variants)}</td><td>${fmt(s.ec.n_corrected)}</td><td>${fmt(s.ec.total_raw)}</td><td>${fmt(s.ec.total_corrected)}</td><td>${fmt(s.ec.pct_removed, 2)} %</td></tr>`,
+              )
+              .join("");
+            h += `</tbody></table></div></div>`;
+            // One run-level report now, carrying every file and its own selector, so there is nothing
+            // left for this page to switch between.
+            if (ecReports.length) {
+              h += `<div class="panel"><h2>Full report ${info(
+                "The full error-correction report; its file selector drives subset or single-file views.",
+              )}</h2><iframe class="frame" style="height:84vh" data-frame="${esc(fkey(ecReports[0]))}" title="Error-correction report"></iframe></div>`;
+            }
+            root.innerHTML = h;
+            mountFrames(root);
+          },
+        });
+
+      // --- variant effects ---
+      // Which estimators ran is a per-run choice (--fitness / --dimsum / --mutscan), so nothing here
+      // may assume any particular one is present: every block is driven off what actually arrived.
+      const METHODS = [
+        [
+          "default",
+          "Default",
+          "fitness/default_results/",
+          "Replicate-wise log-ratio of output to input counts, rescaled so synonymous variants sit at 0 and stops at -1.",
+        ],
+        [
+          "dimsum",
+          "DiMSum",
+          "fitness/DiMSum_results/dimsum_results/",
+          "Fits an error model across replicates and reports fitness with per-variant error estimates.",
+        ],
+        [
+          "mutscan",
+          "mutscan",
+          "fitness/mutscan_results/",
+          "Differential abundance via edgeR/limma, reporting log fold-changes with significance.",
+        ],
+      ].filter(
+        ([g]) =>
+          (DATA.reports || []).some((r) => r.group === g) ||
+          (DATA.downloads || []).some((d) => d.group === g) ||
+          (g === "default" && DATA.fitness),
+      );
+
+      if (DATA.fitness || METHODS.length)
+        SECT.push({
+          id: "fitness",
+          label: "Variant effects",
+          build(root) {
+            const F = DATA.fitness;
+            let h = `<h1>Variant effects</h1><p class="lead">How each substitution changes fitness. ${
+              METHODS.length ? "Estimators in this run: " + METHODS.map(([, n]) => n).join(", ") + "." : ""
+            }</p>`;
+            const hasStruct = DATA.structure && window.$3Dmol;
+            // The blue-white-red fitness scale, faithful to colFit: −fmax red (deleterious), 0 white,
+            // +fmax blue (fitness gain) — the same ramp the landscape heatmap uses, so one legend serves
+            // both. Placed BETWEEN the landscape and the structure when the two share a block.
+            const legendHTML = (() => {
+              const fm = (DATA.structure && DATA.structure.fmax) || 1;
+              const grad = "linear-gradient(to right,#b40426,#e26a52,#f2c0a4,#f2f2f2,#c3cfe2,#6f8fe8,#3a4cc0)";
+              return `<div style="max-width:360px;margin:16px auto"><div style="height:12px;border-radius:6px;background:${grad}"></div><div style="display:flex;justify-content:space-between;font-size:11px;color:var(--muted);margin-top:3px"><span>−${fm.toFixed(2)} · less fit</span><span>0 · neutral</span><span>+${fm.toFixed(2)} · fitter</span></div></div>`;
+            })();
+            const landInfo = info(
+              "Mean fitness per substitution across replicates: blue fitter than wildtype, red less fit, white neutral. Symmetric about 0 (anchored on synonymous variants), stops at −1; grey = unmeasured. Click a cell for the contributing nucleotide changes.",
+            );
+            const structInfo = info(
+              "Wildtype fold coloured by mean missense fitness per residue, on the landscape's scale; grey = unmeasured. Auto-spins; use the inspection tool for interaction.",
+            );
+            const structDesc = `<p class="desc">Each residue is coloured by its mean fitness${
+              DATA.structure && DATA.structure.identity < 0.9
+                ? ` (structure↔sequence identity ${(DATA.structure.identity * 100).toFixed(0)}%)`
+                : ""
+            }.${
+              (DATA.reports || []).some((r) => r.group === "viewer")
+                ? ` <a href="#" id="open-viewer" style="color:var(--accent);font-weight:600">Open the full interactive inspection tool ↗</a>`
+                : ""
+            }</p>`;
+            const landBlock = `<h2>Fitness landscape ${landInfo}</h2><div id="f-heat"></div><div id="f-cell"></div>`;
+            const structBlock = `<h2 style="margin-top:4px">Fitness on the structure ${structInfo}</h2>${structDesc}<div id="f-struct" style="height:560px;position:relative"></div>`;
+            // One block with the shared legend between the two views when both are present; otherwise
+            // whichever one exists, with the legend kept alongside the structure.
+            if (F && hasStruct) h += `<div class="panel">${landBlock}${legendHTML}${structBlock}</div>`;
+            else if (F) h += `<div class="panel">${landBlock}</div>`;
+            else if (hasStruct) h += `<div class="panel">${structBlock}${legendHTML}</div>`;
+            // Point to the full per-method outputs on disk: the landscape here is a summary, and each
+            // estimator writes its complete tables, diagnostics and report to the results folder.
+            if (METHODS.length)
+              h +=
+                `<div class="panel"><h2>Complete fitness outputs</h2><p class="desc">The landscape above summarises the built-in estimator. Every estimator's full tables, diagnostic plots and reports are written to the results folder:</p>` +
+                `<div class="tblwrap"><table><thead><tr><th>Path</th><th>Estimator</th></tr></thead><tbody>` +
+                METHODS.map(
+                  ([, n, path, desc]) => `<tr><td><code>${esc(path)}</code></td><td>${esc(n)} — ${esc(desc)}</td></tr>`,
+                ).join("") +
+                `</tbody></table></div></div>`;
+            root.innerHTML = h;
+            mountFrames(root);
+
+            // Move the pre-built, already-spinning structure viewer into this page's panel.
+            if (DATA.structure && window.$3Dmol) {
+              STRUCT.attach(document.getElementById("f-struct"));
+              const ov = document.getElementById("open-viewer");
+              if (ov) {
+                // Open the full inspection tool in a new tab from the copy embedded in this file, so the
+                // link keeps working after the report is downloaded and sent on (no server, no sidecar).
+                const vrep = (DATA.reports || []).find((r) => r.group === "viewer");
+                ov.onclick = (e) => {
+                  e.preventDefault();
+                  if (!vrep) return;
+                  const url = URL.createObjectURL(new Blob([vrep.html], { type: "text/html" }));
+                  window.open(url, "_blank", "noopener");
+                };
+              }
+            }
+
+            // fitness heatmap (diverging, fixed symmetric range)
+            (function () {
+              const host = document.getElementById("f-heat");
+              const AAS = "ACDEFGHIKLMNPQRSTVWY*".split("");
+              const cells = F.cells;
+              if (!cells.length) return (host.innerHTML = '<div class="empty">No fitness data.</div>');
+              const maxP = Math.max(...cells.map((c) => c.p));
+              const a = Math.max(...cells.map((c) => Math.abs(c.f))) || 1;
+              const W = 1020,
+                m = { l: 26, r: 10, t: 8, b: 26 },
+                cw = (W - m.l - m.r) / maxP,
+                ch = 11;
+              const H = m.t + m.b + ch * AAS.length;
+              const svg = el("svg", { viewBox: `0 0 ${W} ${H}` });
+              host.innerHTML = "";
+              host.appendChild(svg);
+              const CW = [0x3a4cc0, 0x6f8fe8, 0xc3cfe2, 0xf2f2f2, 0xf2c0a4, 0xe26a52, 0xb40426];
+              const hex2 = (x) => [(x >> 16) & 255, (x >> 8) & 255, x & 255];
+              const lerp = (p, q, t) => p + (q - p) * t;
+              const ramp = (t) => {
+                t = Math.max(0, Math.min(1, t));
+                const n = CW.length - 1,
+                  xx = t * n,
+                  i = Math.min(n - 1, Math.floor(xx)),
+                  f = xx - i;
+                const p = hex2(CW[i]),
+                  q = hex2(CW[i + 1]);
+                return `rgb(${Math.round(lerp(p[0], q[0], f))},${Math.round(lerp(p[1], q[1], f))},${Math.round(lerp(p[2], q[2], f))})`;
+              };
+              AAS.forEach((aa, i) => {
+                const t = el("text", { class: "tick", x: m.l - 5, y: m.t + i * ch + 8, "text-anchor": "end" });
+                t.textContent = aa;
+                svg.appendChild(t);
+              });
+              const step = Math.max(1, Math.round(maxP / 12));
+              for (let p = 1; p <= maxP; p += step) {
+                const t = el("text", { class: "tick", x: m.l + (p - 0.5) * cw, y: H - 8, "text-anchor": "middle" });
+                t.textContent = p;
+                svg.appendChild(t);
+              }
+              cells.forEach((c) => {
+                const ai = AAS.indexOf(c.a);
+                if (ai < 0) return;
+                const r = el("rect", {
+                  x: m.l + (c.p - 1) * cw,
+                  y: m.t + ai * ch,
+                  width: Math.max(0.6, cw - 0.4),
+                  height: ch - 0.6,
+                  fill: ramp(1 - (c.f + a) / (2 * a)),
+                });
+                r.addEventListener("mousemove", (e) =>
+                  showTT(
+                    e,
+                    `<b>${c.wt + c.p + c.a}</b> · fitness ${c.f.toFixed(2)}${c.sd != null ? " ± " + c.sd.toFixed(2) : ""}${c.syn ? " (syn)" : c.stop ? " (stop)" : ""}<span style="opacity:.7"> · click for detail</span>`,
+                  ),
+                );
+                r.addEventListener("mouseleave", hideTT);
+                r.style.cursor = "pointer";
+                r.addEventListener("click", () => {
+                  svg.querySelectorAll("rect.selcell").forEach((x) => {
+                    x.classList.remove("selcell");
+                    x.removeAttribute("stroke");
+                  });
+                  r.classList.add("selcell");
+                  r.setAttribute("stroke", "var(--ink)");
+                  r.setAttribute("stroke-width", "1.4");
+                  showCell(c);
+                });
+                svg.appendChild(r);
+              });
+            })();
+
+            // Detail for a clicked cell. One amino-acid substitution can be reached by several
+            // different codons, and the pipeline reports them together on one row - so the honest
+            // answer to "what nucleotide change is this?" is usually a list, not a single value.
+            function showCell(c) {
+              const host = document.getElementById("f-cell");
+              const nt = c.nt || [];
+              let x = `<div class="cellbox"><h3>${esc(c.wt + c.p + c.a)}${
+                c.syn ? ' <span class="tag">synonymous</span>' : c.stop ? ' <span class="tag">stop</span>' : ""
+              }</h3>`;
+              x += `<div class="tiles small">`;
+              x += `<div class="tile"><div class="k">Mean fitness</div><div class="v">${fmt(c.f, 3)}</div></div>`;
+              x += `<div class="tile"><div class="k">SD across replicates</div><div class="v">${
+                c.sd == null ? "–" : fmt(c.sd, 3)
+              }</div></div>`;
+              if (c.reps && c.reps.length)
+                x += `<div class="tile"><div class="k">Per replicate</div><div class="v small">${c.reps
+                  .map((v) => (v == null ? "–" : fmt(v, 2)))
+                  .join(" · ")}</div></div>`;
+              if (c.n_in != null)
+                x += `<div class="tile"><div class="k">Input counts</div><div class="v">${fmt(c.n_in)}</div></div>`;
+              if (c.n_out != null)
+                x += `<div class="tile"><div class="k">Output counts</div><div class="v">${fmt(c.n_out)}</div></div>`;
+              x += `<div class="tile"><div class="k">Coding variants</div><div class="v">${nt.length || "–"}</div></div>`;
+              x += `</div>`;
+              if (nt.length) {
+                x += `<h4>Nucleotide changes reaching this substitution ${info(
+                  "Every codon in the data producing this amino-acid change, as nucleotide substitutions vs the wildtype ORF. Pooled into one fitness value, so disagreement between them is worth checking.",
+                )}</h4>`;
+                x += `<div class="tblwrap"><table><thead><tr><th>Codon</th><th>Nucleotide change(s)</th><th>ORF positions</th></tr></thead><tbody>`;
+                nt.forEach((v) => {
+                  x += `<tr><td><code>${esc(v.wt_codon)} → ${esc(v.mut_codon)}</code></td><td><code>${esc(
+                    v.changes.join(", "),
+                  )}</code></td><td>${esc(v.positions.join(", "))}</td></tr>`;
+                });
+                x += `</tbody></table></div>`;
+              } else {
+                x += `<p class="desc">No nucleotide-level detail available for this substitution.</p>`;
+              }
+              x += `</div>`;
+              host.innerHTML = x;
+              host.scrollIntoView({ behavior: "smooth", block: "nearest" });
+            }
+          },
+        });
+
+      // --- citation ---
+      const CITE = DATA.citations || { pipeline: [], tools: [] };
+      // Format one reference as a readable line; the same data also produces the biblatex block.
+      function citeLine(c) {
+        const bits = [`<b>${esc(c.authors)}</b>`, esc(c.title) + "."];
+        if (c.journal) {
+          let j = `<i>${esc(c.journal)}</i>`;
+          if (c.year) j += " " + esc(c.year);
+          if (c.volume) j += ";" + esc(c.volume) + (c.number ? "(" + esc(c.number) + ")" : "");
+          if (c.pages) j += ":" + esc(c.pages);
+          bits.push(j + ".");
+        } else if (c.year) bits.push(esc(c.year) + ".");
+        if (c.doi)
+          bits.push(`doi:<a href="https://doi.org/${esc(c.doi)}" target="_blank" rel="noopener">${esc(c.doi)}</a>`);
+        else if (c.eprint)
+          bits.push(
+            `arXiv:<a href="https://arxiv.org/abs/${esc(c.eprint)}" target="_blank" rel="noopener">${esc(c.eprint)}</a>`,
+          );
+        else if (c.url) bits.push(`<a href="${esc(c.url)}" target="_blank" rel="noopener">${esc(c.url)}</a>`);
+        return bits.join(" ") + (c.note ? ` <span style="opacity:.7">${esc(c.note)}</span>` : "");
+      }
+      // Open the biblatex source in a new tab as plain text - a view, not a download, as asked. A new
+      // window's document is same-origin and writable even from file://, unlike a blob: URL.
+      function openBib(title, src) {
+        const w = window.open("", "_blank");
+        if (!w) return;
+        w.document.write(
+          `<!doctype html><title>${esc(title)}</title>` +
+            `<body style="margin:0"><pre style="margin:0;padding:20px;font:13px/1.5 ui-monospace,Menlo,monospace;white-space:pre-wrap">${esc(src)}</pre></body>`,
+        );
+        w.document.close();
+      }
+      window.__bib = { pipeline: CITE.biblatex_pipeline || "", tools: CITE.biblatex_tools || "" };
+      if ((CITE.pipeline || []).length || (CITE.tools || []).length)
+        SECT.push({
+          id: "citation",
+          label: "Citation",
+          build(root) {
+            const sect = (heading, items, which, note) =>
+              `<div class="panel"><h2>${esc(heading)}</h2>${note ? `<p class="desc">${note}</p>` : ""}` +
+              `<ol class="cites">${items.map((c) => `<li>${citeLine(c)}</li>`).join("")}</ol>` +
+              `<button class="act" data-bib="${which}">Get biblatex</button></div>`;
+            let h = `<h1>Citation</h1><p class="lead">If this run supports your work, cite the pipeline and the tools below — exactly those it invoked.</p>`;
+            h += sect(
+              "Pipeline",
+              CITE.pipeline || [],
+              "pipeline",
+              "Cite nf-core/deepmutscan together with the nf-core framework and Nextflow.",
+            );
+            h += sect("Tools used in this run", CITE.tools || [], "tools", "");
+            root.innerHTML = h;
+            root.addEventListener("click", (e) => {
+              const b = e.target.closest("[data-bib]");
+              if (!b) return;
+              openBib("biblatex — " + b.dataset.bib, window.__bib[b.dataset.bib]);
+            });
+          },
+        });
+
+      /* ---------- nav ---------- */
+      const nav = document.getElementById("nav"),
+        main = document.getElementById("main");
+      SECT.forEach((s, i) => {
+        const b = document.createElement("button");
+        b.textContent = s.label;
+        b.className = "";
+        b.onclick = () => {
+          nav.querySelectorAll("button").forEach((x) => x.classList.remove("on"));
+          b.classList.add("on");
+          show(s);
+        };
+        nav.appendChild(b);
+      });
+      const built = {};
+      function show(s) {
+        STRUCT.detach(); // rescue the live WebGL viewer before the page is wiped
+        main.innerHTML = "";
+        const d = document.createElement("div");
+        d.className = "sect on";
+        main.appendChild(d);
+        s.build(d);
+        // `main` is the scroll container, so without this a page opens at whatever offset the previous
+        // one was scrolled to - landing the reader in the middle of a page they have not seen the top of.
+        main.scrollTop = 0;
+      }
+      document.getElementById("run-tag").innerHTML =
+        "sample <strong>" + esc((DATA.run || {}).sample || "run") + "</strong> · " + SIDS.length + " sequencing files";
+      // Open on the Overview page - the guided entry point to the run.
+      const startIdx = 0;
+      nav.children[startIdx].classList.add("on");
+      show(SECT[startIdx]);
+    </script>
+  </body>
+</html>
diff --git a/assets/test_structure/GID1A_AFDB.pdb b/assets/test_structure/GID1A_AFDB.pdb
new file mode 100644
index 0000000..3d20acc
--- /dev/null
+++ b/assets/test_structure/GID1A_AFDB.pdb
@@ -0,0 +1,2803 @@
+HEADER                                            01-AUG-25                     
+TITLE     ALPHAFOLD MONOMER V2.0 PREDICTION FOR GIBBERELLIN RECEPTOR GID1A      
+TITLE    2 (Q9MAA7)                                                             
+COMPND    MOL_ID: 1;                                                            
+COMPND   2 MOLECULE: GIBBERELLIN RECEPTOR GID1A;                                
+COMPND   3 CHAIN: A                                                             
+SOURCE    MOL_ID: 1;                                                            
+SOURCE   2 ORGANISM_SCIENTIFIC: ARABIDOPSIS THALIANA;                           
+SOURCE   3 ORGANISM_TAXID: 3702                                                 
+REMARK   1                                                                      
+REMARK   1 REFERENCE 1                                                          
+REMARK   1  AUTH   JOHN JUMPER, RICHARD EVANS, ALEXANDER PRITZEL, TIM GREEN,    
+REMARK   1  AUTH 2 MICHAEL FIGURNOV, OLAF RONNEBERGER, KATHRYN TUNYASUVUNAKOOL, 
+REMARK   1  AUTH 3 RUSS BATES, AUGUSTIN ZIDEK, ANNA POTAPENKO, ALEX BRIDGLAND,  
+REMARK   1  AUTH 4 CLEMENS MEYER, SIMON A A KOHL, ANDREW J BALLARD,             
+REMARK   1  AUTH 5 ANDREW COWIE, BERNARDINO ROMERA-PAREDES, STANISLAV NIKOLOV,  
+REMARK   1  AUTH 6 RISHUB JAIN, JONAS ADLER, TREVOR BACK, STIG PETERSEN,        
+REMARK   1  AUTH 7 DAVID REIMAN, ELLEN CLANCY, MICHAL ZIELINSKI,                
+REMARK   1  AUTH 8 MARTIN STEINEGGER, MICHALINA PACHOLSKA, TAMAS BERGHAMMER,    
+REMARK   1  AUTH 9 DAVID SILVER, ORIOL VINYALS, ANDREW W SENIOR,                
+REMARK   1  AUTH10 KORAY KAVUKCUOGLU, PUSHMEET KOHLI, DEMIS HASSABIS            
+REMARK   1  TITL   HIGHLY ACCURATE PROTEIN STRUCTURE PREDICTION WITH ALPHAFOLD  
+REMARK   1  REF    NATURE                        V. 596   583 2021              
+REMARK   1  REFN                   ISSN 0028-0836                               
+REMARK   1  PMID   34265844                                                     
+REMARK   1  DOI    10.1038/s41586-021-03819-2                                   
+REMARK   1                                                                      
+REMARK   1 DISCLAIMERS                                                          
+REMARK   1 ALPHAFOLD DATA, COPYRIGHT (2021) DEEPMIND TECHNOLOGIES LIMITED. THE  
+REMARK   1 INFORMATION PROVIDED IS THEORETICAL MODELLING ONLY AND CAUTION SHOULD
+REMARK   1 BE EXERCISED IN ITS USE. IT IS PROVIDED "AS-IS" WITHOUT ANY WARRANTY 
+REMARK   1 OF ANY KIND, WHETHER EXPRESSED OR IMPLIED. NO WARRANTY IS GIVEN THAT 
+REMARK   1 USE OF THE INFORMATION SHALL NOT INFRINGE THE RIGHTS OF ANY THIRD    
+REMARK   1 PARTY. THE INFORMATION IS NOT INTENDED TO BE A SUBSTITUTE FOR        
+REMARK   1 PROFESSIONAL MEDICAL ADVICE, DIAGNOSIS, OR TREATMENT, AND DOES NOT   
+REMARK   1 CONSTITUTE MEDICAL OR OTHER PROFESSIONAL ADVICE. IT IS AVAILABLE FOR 
+REMARK   1 ACADEMIC AND COMMERCIAL PURPOSES, UNDER CC-BY 4.0 LICENCE.           
+DBREF  XXXX A    1   345  UNP    Q9MAA7   GID1A_ARATH      1    345             
+SEQRES   1 A  345  MET ALA ALA SER ASP GLU VAL ASN LEU ILE GLU SER ARG          
+SEQRES   2 A  345  THR VAL VAL PRO LEU ASN THR TRP VAL LEU ILE SER ASN          
+SEQRES   3 A  345  PHE LYS VAL ALA TYR ASN ILE LEU ARG ARG PRO ASP GLY          
+SEQRES   4 A  345  THR PHE ASN ARG HIS LEU ALA GLU TYR LEU ASP ARG LYS          
+SEQRES   5 A  345  VAL THR ALA ASN ALA ASN PRO VAL ASP GLY VAL PHE SER          
+SEQRES   6 A  345  PHE ASP VAL LEU ILE ASP ARG ARG ILE ASN LEU LEU SER          
+SEQRES   7 A  345  ARG VAL TYR ARG PRO ALA TYR ALA ASP GLN GLU GLN PRO          
+SEQRES   8 A  345  PRO SER ILE LEU ASP LEU GLU LYS PRO VAL ASP GLY ASP          
+SEQRES   9 A  345  ILE VAL PRO VAL ILE LEU PHE PHE HIS GLY GLY SER PHE          
+SEQRES  10 A  345  ALA HIS SER SER ALA ASN SER ALA ILE TYR ASP THR LEU          
+SEQRES  11 A  345  CYS ARG ARG LEU VAL GLY LEU CYS LYS CYS VAL VAL VAL          
+SEQRES  12 A  345  SER VAL ASN TYR ARG ARG ALA PRO GLU ASN PRO TYR PRO          
+SEQRES  13 A  345  CYS ALA TYR ASP ASP GLY TRP ILE ALA LEU ASN TRP VAL          
+SEQRES  14 A  345  ASN SER ARG SER TRP LEU LYS SER LYS LYS ASP SER LYS          
+SEQRES  15 A  345  VAL HIS ILE PHE LEU ALA GLY ASP SER SER GLY GLY ASN          
+SEQRES  16 A  345  ILE ALA HIS ASN VAL ALA LEU ARG ALA GLY GLU SER GLY          
+SEQRES  17 A  345  ILE ASP VAL LEU GLY ASN ILE LEU LEU ASN PRO MET PHE          
+SEQRES  18 A  345  GLY GLY ASN GLU ARG THR GLU SER GLU LYS SER LEU ASP          
+SEQRES  19 A  345  GLY LYS TYR PHE VAL THR VAL ARG ASP ARG ASP TRP TYR          
+SEQRES  20 A  345  TRP LYS ALA PHE LEU PRO GLU GLY GLU ASP ARG GLU HIS          
+SEQRES  21 A  345  PRO ALA CYS ASN PRO PHE SER PRO ARG GLY LYS SER LEU          
+SEQRES  22 A  345  GLU GLY VAL SER PHE PRO LYS SER LEU VAL VAL VAL ALA          
+SEQRES  23 A  345  GLY LEU ASP LEU ILE ARG ASP TRP GLN LEU ALA TYR ALA          
+SEQRES  24 A  345  GLU GLY LEU LYS LYS ALA GLY GLN GLU VAL LYS LEU MET          
+SEQRES  25 A  345  HIS LEU GLU LYS ALA THR VAL GLY PHE TYR LEU LEU PRO          
+SEQRES  26 A  345  ASN ASN ASN HIS PHE HIS ASN VAL MET ASP GLU ILE SER          
+SEQRES  27 A  345  ALA PHE VAL ASN ALA GLU CYS                                  
+CRYST1    1.000    1.000    1.000  90.00  90.00  90.00 P 1           1          
+ORIGX1      1.000000  0.000000  0.000000        0.00000                         
+ORIGX2      0.000000  1.000000  0.000000        0.00000                         
+ORIGX3      0.000000  0.000000  1.000000        0.00000                         
+SCALE1      1.000000  0.000000  0.000000        0.00000                         
+SCALE2      0.000000  1.000000  0.000000        0.00000                         
+SCALE3      0.000000  0.000000  1.000000        0.00000                         
+MODEL        1                                                                  
+ATOM      1  N   MET A   1      32.551  13.921 -16.818  1.00 37.16           N  
+ATOM      2  CA  MET A   1      31.146  13.594 -17.145  1.00 37.16           C  
+ATOM      3  C   MET A   1      30.154  14.268 -16.181  1.00 37.16           C  
+ATOM      4  CB  MET A   1      30.942  12.062 -17.182  1.00 37.16           C  
+ATOM      5  O   MET A   1      29.177  13.650 -15.808  1.00 37.16           O  
+ATOM      6  CG  MET A   1      31.702  11.397 -18.334  1.00 37.16           C  
+ATOM      7  SD  MET A   1      30.973   9.830 -18.876  1.00 37.16           S  
+ATOM      8  CE  MET A   1      31.719   8.639 -17.733  1.00 37.16           C  
+ATOM      9  N   ALA A   2      30.353  15.534 -15.785  1.00 45.28           N  
+ATOM     10  CA  ALA A   2      29.441  16.208 -14.840  1.00 45.28           C  
+ATOM     11  C   ALA A   2      28.253  16.926 -15.520  1.00 45.28           C  
+ATOM     12  CB  ALA A   2      30.273  17.166 -13.979  1.00 45.28           C  
+ATOM     13  O   ALA A   2      27.290  17.287 -14.858  1.00 45.28           O  
+ATOM     14  N   ALA A   3      28.305  17.111 -16.844  1.00 42.53           N  
+ATOM     15  CA  ALA A   3      27.326  17.919 -17.572  1.00 42.53           C  
+ATOM     16  C   ALA A   3      25.971  17.221 -17.822  1.00 42.53           C  
+ATOM     17  CB  ALA A   3      27.979  18.386 -18.880  1.00 42.53           C  
+ATOM     18  O   ALA A   3      24.993  17.909 -18.095  1.00 42.53           O  
+ATOM     19  N   SER A   4      25.878  15.885 -17.740  1.00 52.66           N  
+ATOM     20  CA  SER A   4      24.606  15.179 -17.984  1.00 52.66           C  
+ATOM     21  C   SER A   4      23.632  15.298 -16.817  1.00 52.66           C  
+ATOM     22  CB  SER A   4      24.838  13.695 -18.294  1.00 52.66           C  
+ATOM     23  O   SER A   4      22.430  15.450 -17.023  1.00 52.66           O  
+ATOM     24  OG  SER A   4      25.556  13.043 -17.259  1.00 52.66           O  
+ATOM     25  N   ASP A   5      24.142  15.239 -15.589  1.00 48.25           N  
+ATOM     26  CA  ASP A   5      23.291  15.106 -14.407  1.00 48.25           C  
+ATOM     27  C   ASP A   5      22.691  16.450 -13.990  1.00 48.25           C  
+ATOM     28  CB  ASP A   5      24.075  14.440 -13.267  1.00 48.25           C  
+ATOM     29  O   ASP A   5      21.531  16.502 -13.586  1.00 48.25           O  
+ATOM     30  CG  ASP A   5      24.444  12.978 -13.556  1.00 48.25           C  
+ATOM     31  OD1 ASP A   5      24.081  12.474 -14.648  1.00 48.25           O  
+ATOM     32  OD2 ASP A   5      25.125  12.394 -12.686  1.00 48.25           O  
+ATOM     33  N   GLU A   6      23.431  17.553 -14.148  1.00 51.66           N  
+ATOM     34  CA  GLU A   6      22.931  18.891 -13.811  1.00 51.66           C  
+ATOM     35  C   GLU A   6      21.745  19.312 -14.685  1.00 51.66           C  
+ATOM     36  CB  GLU A   6      24.048  19.939 -13.908  1.00 51.66           C  
+ATOM     37  O   GLU A   6      20.742  19.789 -14.152  1.00 51.66           O  
+ATOM     38  CG  GLU A   6      24.992  19.887 -12.698  1.00 51.66           C  
+ATOM     39  CD  GLU A   6      25.985  21.062 -12.668  1.00 51.66           C  
+ATOM     40  OE1 GLU A   6      26.514  21.327 -11.564  1.00 51.66           O  
+ATOM     41  OE2 GLU A   6      26.211  21.684 -13.730  1.00 51.66           O  
+ATOM     42  N   VAL A   7      21.811  19.089 -16.005  1.00 47.53           N  
+ATOM     43  CA  VAL A   7      20.706  19.420 -16.926  1.00 47.53           C  
+ATOM     44  C   VAL A   7      19.460  18.598 -16.587  1.00 47.53           C  
+ATOM     45  CB  VAL A   7      21.127  19.226 -18.397  1.00 47.53           C  
+ATOM     46  O   VAL A   7      18.372  19.157 -16.444  1.00 47.53           O  
+ATOM     47  CG1 VAL A   7      19.978  19.530 -19.366  1.00 47.53           C  
+ATOM     48  CG2 VAL A   7      22.294  20.156 -18.759  1.00 47.53           C  
+ATOM     49  N   ASN A   8      19.631  17.293 -16.341  1.00 58.84           N  
+ATOM     50  CA  ASN A   8      18.542  16.404 -15.927  1.00 58.84           C  
+ATOM     51  C   ASN A   8      17.887  16.863 -14.607  1.00 58.84           C  
+ATOM     52  CB  ASN A   8      19.113  14.982 -15.776  1.00 58.84           C  
+ATOM     53  O   ASN A   8      16.663  16.823 -14.458  1.00 58.84           O  
+ATOM     54  CG  ASN A   8      19.412  14.281 -17.090  1.00 58.84           C  
+ATOM     55  ND2 ASN A   8      20.333  13.347 -17.094  1.00 58.84           N  
+ATOM     56  OD1 ASN A   8      18.758  14.455 -18.102  1.00 58.84           O  
+ATOM     57  N   LEU A   9      18.690  17.328 -13.643  1.00 60.03           N  
+ATOM     58  CA  LEU A   9      18.189  17.832 -12.364  1.00 60.03           C  
+ATOM     59  C   LEU A   9      17.413  19.145 -12.521  1.00 60.03           C  
+ATOM     60  CB  LEU A   9      19.357  18.003 -11.378  1.00 60.03           C  
+ATOM     61  O   LEU A   9      16.423  19.339 -11.817  1.00 60.03           O  
+ATOM     62  CG  LEU A   9      19.898  16.680 -10.807  1.00 60.03           C  
+ATOM     63  CD1 LEU A   9      21.210  16.952 -10.067  1.00 60.03           C  
+ATOM     64  CD2 LEU A   9      18.924  16.037  -9.815  1.00 60.03           C  
+ATOM     65  N   ILE A  10      17.812  20.033 -13.434  1.00 62.09           N  
+ATOM     66  CA  ILE A  10      17.093  21.291 -13.683  1.00 62.09           C  
+ATOM     67  C   ILE A  10      15.707  21.014 -14.277  1.00 62.09           C  
+ATOM     68  CB  ILE A  10      17.938  22.244 -14.558  1.00 62.09           C  
+ATOM     69  O   ILE A  10      14.724  21.551 -13.768  1.00 62.09           O  
+ATOM     70  CG1 ILE A  10      19.169  22.736 -13.762  1.00 62.09           C  
+ATOM     71  CG2 ILE A  10      17.108  23.458 -15.023  1.00 62.09           C  
+ATOM     72  CD1 ILE A  10      20.241  23.403 -14.634  1.00 62.09           C  
+ATOM     73  N   GLU A  11      15.604  20.140 -15.282  1.00 66.12           N  
+ATOM     74  CA  GLU A  11      14.314  19.758 -15.878  1.00 66.12           C  
+ATOM     75  C   GLU A  11      13.386  19.103 -14.851  1.00 66.12           C  
+ATOM     76  CB  GLU A  11      14.532  18.789 -17.046  1.00 66.12           C  
+ATOM     77  O   GLU A  11      12.216  19.458 -14.736  1.00 66.12           O  
+ATOM     78  CG  GLU A  11      15.164  19.466 -18.270  1.00 66.12           C  
+ATOM     79  CD  GLU A  11      15.323  18.507 -19.462  1.00 66.12           C  
+ATOM     80  OE1 GLU A  11      15.684  19.013 -20.548  1.00 66.12           O  
+ATOM     81  OE2 GLU A  11      15.079  17.288 -19.293  1.00 66.12           O  
+ATOM     82  N   SER A  12      13.906  18.195 -14.025  1.00 67.88           N  
+ATOM     83  CA  SER A  12      13.073  17.517 -13.025  1.00 67.88           C  
+ATOM     84  C   SER A  12      12.460  18.477 -11.987  1.00 67.88           C  
+ATOM     85  CB  SER A  12      13.883  16.405 -12.363  1.00 67.88           C  
+ATOM     86  O   SER A  12      11.333  18.261 -11.531  1.00 67.88           O  
+ATOM     87  OG  SER A  12      14.937  16.960 -11.608  1.00 67.88           O  
+ATOM     88  N   ARG A  13      13.149  19.584 -11.662  1.00 67.12           N  
+ATOM     89  CA  ARG A  13      12.662  20.623 -10.736  1.00 67.12           C  
+ATOM     90  C   ARG A  13      11.535  21.478 -11.311  1.00 67.12           C  
+ATOM     91  CB  ARG A  13      13.818  21.539 -10.316  1.00 67.12           C  
+ATOM     92  O   ARG A  13      10.814  22.088 -10.527  1.00 67.12           O  
+ATOM     93  CG  ARG A  13      14.783  20.867  -9.337  1.00 67.12           C  
+ATOM     94  CD  ARG A  13      15.946  21.825  -9.068  1.00 67.12           C  
+ATOM     95  NE  ARG A  13      16.982  21.200  -8.230  1.00 67.12           N  
+ATOM     96  NH1 ARG A  13      18.138  23.118  -7.722  1.00 67.12           N  
+ATOM     97  NH2 ARG A  13      18.910  21.148  -7.017  1.00 67.12           N  
+ATOM     98  CZ  ARG A  13      17.998  21.822  -7.660  1.00 67.12           C  
+ATOM     99  N   THR A  14      11.377  21.549 -12.635  1.00 75.06           N  
+ATOM    100  CA  THR A  14      10.277  22.308 -13.259  1.00 75.06           C  
+ATOM    101  C   THR A  14       8.999  21.482 -13.390  1.00 75.06           C  
+ATOM    102  CB  THR A  14      10.671  22.895 -14.623  1.00 75.06           C  
+ATOM    103  O   THR A  14       7.915  22.052 -13.487  1.00 75.06           O  
+ATOM    104  CG2 THR A  14      11.909  23.789 -14.550  1.00 75.06           C  
+ATOM    105  OG1 THR A  14      10.944  21.877 -15.546  1.00 75.06           O  
+ATOM    106  N   VAL A  15       9.107  20.149 -13.343  1.00 82.19           N  
+ATOM    107  CA  VAL A  15       7.970  19.222 -13.448  1.00 82.19           C  
+ATOM    108  C   VAL A  15       7.313  18.954 -12.091  1.00 82.19           C  
+ATOM    109  CB  VAL A  15       8.427  17.904 -14.104  1.00 82.19           C  
+ATOM    110  O   VAL A  15       6.089  18.864 -11.997  1.00 82.19           O  
+ATOM    111  CG1 VAL A  15       7.275  16.904 -14.237  1.00 82.19           C  
+ATOM    112  CG2 VAL A  15       8.987  18.142 -15.514  1.00 82.19           C  
+ATOM    113  N   VAL A  16       8.105  18.818 -11.023  1.00 83.50           N  
+ATOM    114  CA  VAL A  16       7.589  18.466  -9.691  1.00 83.50           C  
+ATOM    115  C   VAL A  16       7.214  19.724  -8.904  1.00 83.50           C  
+ATOM    116  CB  VAL A  16       8.606  17.646  -8.885  1.00 83.50           C  
+ATOM    117  O   VAL A  16       8.070  20.589  -8.719  1.00 83.50           O  
+ATOM    118  CG1 VAL A  16       8.007  17.134  -7.567  1.00 83.50           C  
+ATOM    119  CG2 VAL A  16       9.062  16.405  -9.647  1.00 83.50           C  
+ATOM    120  N   PRO A  17       5.996  19.816  -8.334  1.00 86.50           N  
+ATOM    121  CA  PRO A  17       5.652  20.900  -7.418  1.00 86.50           C  
+ATOM    122  C   PRO A  17       6.669  21.020  -6.275  1.00 86.50           C  
+ATOM    123  CB  PRO A  17       4.253  20.563  -6.888  1.00 86.50           C  
+ATOM    124  O   PRO A  17       7.023  20.016  -5.653  1.00 86.50           O  
+ATOM    125  CG  PRO A  17       3.649  19.702  -7.996  1.00 86.50           C  
+ATOM    126  CD  PRO A  17       4.854  18.934  -8.534  1.00 86.50           C  
+ATOM    127  N   LEU A  18       7.099  22.244  -5.949  1.00 88.38           N  
+ATOM    128  CA  LEU A  18       8.189  22.497  -4.993  1.00 88.38           C  
+ATOM    129  C   LEU A  18       8.002  21.770  -3.650  1.00 88.38           C  
+ATOM    130  CB  LEU A  18       8.319  24.019  -4.791  1.00 88.38           C  
+ATOM    131  O   LEU A  18       8.936  21.148  -3.151  1.00 88.38           O  
+ATOM    132  CG  LEU A  18       9.426  24.444  -3.803  1.00 88.38           C  
+ATOM    133  CD1 LEU A  18      10.818  24.013  -4.270  1.00 88.38           C  
+ATOM    134  CD2 LEU A  18       9.418  25.964  -3.648  1.00 88.38           C  
+ATOM    135  N   ASN A  19       6.790  21.782  -3.089  1.00 88.56           N  
+ATOM    136  CA  ASN A  19       6.497  21.109  -1.817  1.00 88.56           C  
+ATOM    137  C   ASN A  19       6.683  19.585  -1.905  1.00 88.56           C  
+ATOM    138  CB  ASN A  19       5.065  21.465  -1.375  1.00 88.56           C  
+ATOM    139  O   ASN A  19       7.212  18.974  -0.977  1.00 88.56           O  
+ATOM    140  CG  ASN A  19       4.902  22.929  -1.002  1.00 88.56           C  
+ATOM    141  ND2 ASN A  19       3.688  23.419  -0.931  1.00 88.56           N  
+ATOM    142  OD1 ASN A  19       5.853  23.654  -0.782  1.00 88.56           O  
+ATOM    143  N   THR A  20       6.295  18.975  -3.030  1.00 88.31           N  
+ATOM    144  CA  THR A  20       6.513  17.544  -3.290  1.00 88.31           C  
+ATOM    145  C   THR A  20       8.000  17.258  -3.472  1.00 88.31           C  
+ATOM    146  CB  THR A  20       5.724  17.079  -4.525  1.00 88.31           C  
+ATOM    147  O   THR A  20       8.514  16.309  -2.883  1.00 88.31           O  
+ATOM    148  CG2 THR A  20       5.820  15.574  -4.758  1.00 88.31           C  
+ATOM    149  OG1 THR A  20       4.357  17.377  -4.356  1.00 88.31           O  
+ATOM    150  N   TRP A  21       8.716  18.111  -4.212  1.00 88.81           N  
+ATOM    151  CA  TRP A  21      10.160  17.979  -4.391  1.00 88.81           C  
+ATOM    152  C   TRP A  21      10.905  18.020  -3.054  1.00 88.81           C  
+ATOM    153  CB  TRP A  21      10.680  19.066  -5.342  1.00 88.81           C  
+ATOM    154  O   TRP A  21      11.747  17.160  -2.796  1.00 88.81           O  
+ATOM    155  CG  TRP A  21      12.148  18.965  -5.617  1.00 88.81           C  
+ATOM    156  CD1 TRP A  21      13.135  19.398  -4.800  1.00 88.81           C  
+ATOM    157  CD2 TRP A  21      12.820  18.322  -6.740  1.00 88.81           C  
+ATOM    158  CE2 TRP A  21      14.227  18.355  -6.507  1.00 88.81           C  
+ATOM    159  CE3 TRP A  21      12.386  17.676  -7.913  1.00 88.81           C  
+ATOM    160  NE1 TRP A  21      14.362  19.030  -5.314  1.00 88.81           N  
+ATOM    161  CH2 TRP A  21      14.673  17.097  -8.518  1.00 88.81           C  
+ATOM    162  CZ2 TRP A  21      15.150  17.755  -7.374  1.00 88.81           C  
+ATOM    163  CZ3 TRP A  21      13.296  17.065  -8.787  1.00 88.81           C  
+ATOM    164  N   VAL A  22      10.580  18.980  -2.183  1.00 90.19           N  
+ATOM    165  CA  VAL A  22      11.181  19.107  -0.846  1.00 90.19           C  
+ATOM    166  C   VAL A  22      10.861  17.885   0.017  1.00 90.19           C  
+ATOM    167  CB  VAL A  22      10.725  20.414  -0.163  1.00 90.19           C  
+ATOM    168  O   VAL A  22      11.774  17.307   0.609  1.00 90.19           O  
+ATOM    169  CG1 VAL A  22      11.168  20.494   1.305  1.00 90.19           C  
+ATOM    170  CG2 VAL A  22      11.319  21.635  -0.879  1.00 90.19           C  
+ATOM    171  N   LEU A  23       9.595  17.451   0.054  1.00 90.62           N  
+ATOM    172  CA  LEU A  23       9.169  16.282   0.828  1.00 90.62           C  
+ATOM    173  C   LEU A  23       9.942  15.021   0.418  1.00 90.62           C  
+ATOM    174  CB  LEU A  23       7.647  16.096   0.656  1.00 90.62           C  
+ATOM    175  O   LEU A  23      10.543  14.358   1.263  1.00 90.62           O  
+ATOM    176  CG  LEU A  23       7.076  14.860   1.377  1.00 90.62           C  
+ATOM    177  CD1 LEU A  23       7.238  14.962   2.895  1.00 90.62           C  
+ATOM    178  CD2 LEU A  23       5.589  14.705   1.060  1.00 90.62           C  
+ATOM    179  N   ILE A  24       9.967  14.708  -0.879  1.00 90.00           N  
+ATOM    180  CA  ILE A  24      10.604  13.483  -1.374  1.00 90.00           C  
+ATOM    181  C   ILE A  24      12.133  13.585  -1.300  1.00 90.00           C  
+ATOM    182  CB  ILE A  24      10.094  13.120  -2.786  1.00 90.00           C  
+ATOM    183  O   ILE A  24      12.794  12.593  -0.995  1.00 90.00           O  
+ATOM    184  CG1 ILE A  24       8.556  12.949  -2.783  1.00 90.00           C  
+ATOM    185  CG2 ILE A  24      10.752  11.804  -3.239  1.00 90.00           C  
+ATOM    186  CD1 ILE A  24       7.968  12.689  -4.171  1.00 90.00           C  
+ATOM    187  N   SER A  25      12.717  14.773  -1.486  1.00 90.50           N  
+ATOM    188  CA  SER A  25      14.160  14.979  -1.292  1.00 90.50           C  
+ATOM    189  C   SER A  25      14.590  14.687   0.147  1.00 90.50           C  
+ATOM    190  CB  SER A  25      14.582  16.402  -1.662  1.00 90.50           C  
+ATOM    191  O   SER A  25      15.621  14.044   0.352  1.00 90.50           O  
+ATOM    192  OG  SER A  25      14.370  16.631  -3.038  1.00 90.50           O  
+ATOM    193  N   ASN A  26      13.786  15.076   1.142  1.00 91.38           N  
+ATOM    194  CA  ASN A  26      14.047  14.732   2.542  1.00 91.38           C  
+ATOM    195  C   ASN A  26      13.986  13.215   2.773  1.00 91.38           C  
+ATOM    196  CB  ASN A  26      13.063  15.486   3.450  1.00 91.38           C  
+ATOM    197  O   ASN A  26      14.878  12.668   3.425  1.00 91.38           O  
+ATOM    198  CG  ASN A  26      13.403  16.958   3.590  1.00 91.38           C  
+ATOM    199  ND2 ASN A  26      12.427  17.788   3.871  1.00 91.38           N  
+ATOM    200  OD1 ASN A  26      14.540  17.383   3.483  1.00 91.38           O  
+ATOM    201  N   PHE A  27      13.005  12.518   2.184  1.00 92.12           N  
+ATOM    202  CA  PHE A  27      12.968  11.052   2.230  1.00 92.12           C  
+ATOM    203  C   PHE A  27      14.186  10.425   1.564  1.00 92.12           C  
+ATOM    204  CB  PHE A  27      11.684  10.498   1.600  1.00 92.12           C  
+ATOM    205  O   PHE A  27      14.791   9.535   2.148  1.00 92.12           O  
+ATOM    206  CG  PHE A  27      10.402  10.857   2.320  1.00 92.12           C  
+ATOM    207  CD1 PHE A  27      10.364  10.923   3.728  1.00 92.12           C  
+ATOM    208  CD2 PHE A  27       9.225  11.082   1.580  1.00 92.12           C  
+ATOM    209  CE1 PHE A  27       9.174  11.256   4.388  1.00 92.12           C  
+ATOM    210  CE2 PHE A  27       8.029  11.403   2.243  1.00 92.12           C  
+ATOM    211  CZ  PHE A  27       8.014  11.504   3.645  1.00 92.12           C  
+ATOM    212  N   LYS A  28      14.607  10.916   0.395  1.00 91.25           N  
+ATOM    213  CA  LYS A  28      15.803  10.423  -0.300  1.00 91.25           C  
+ATOM    214  C   LYS A  28      17.047  10.531   0.583  1.00 91.25           C  
+ATOM    215  CB  LYS A  28      15.966  11.184  -1.626  1.00 91.25           C  
+ATOM    216  O   LYS A  28      17.787   9.558   0.715  1.00 91.25           O  
+ATOM    217  CG  LYS A  28      17.151  10.658  -2.451  1.00 91.25           C  
+ATOM    218  CD  LYS A  28      17.311  11.455  -3.752  1.00 91.25           C  
+ATOM    219  CE  LYS A  28      18.440  10.847  -4.596  1.00 91.25           C  
+ATOM    220  NZ  LYS A  28      18.562  11.493  -5.930  1.00 91.25           N  
+ATOM    221  N   VAL A  29      17.270  11.688   1.211  1.00 92.88           N  
+ATOM    222  CA  VAL A  29      18.415  11.895   2.114  1.00 92.88           C  
+ATOM    223  C   VAL A  29      18.339  10.948   3.310  1.00 92.88           C  
+ATOM    224  CB  VAL A  29      18.515  13.365   2.564  1.00 92.88           C  
+ATOM    225  O   VAL A  29      19.307  10.231   3.567  1.00 92.88           O  
+ATOM    226  CG1 VAL A  29      19.594  13.569   3.638  1.00 92.88           C  
+ATOM    227  CG2 VAL A  29      18.884  14.262   1.375  1.00 92.88           C  
+ATOM    228  N   ALA A  30      17.190  10.886   3.992  1.00 93.94           N  
+ATOM    229  CA  ALA A  30      16.987   9.984   5.122  1.00 93.94           C  
+ATOM    230  C   ALA A  30      17.223   8.520   4.713  1.00 93.94           C  
+ATOM    231  CB  ALA A  30      15.580  10.206   5.691  1.00 93.94           C  
+ATOM    232  O   ALA A  30      18.037   7.823   5.304  1.00 93.94           O  
+ATOM    233  N   TYR A  31      16.600   8.053   3.636  1.00 94.69           N  
+ATOM    234  CA  TYR A  31      16.704   6.666   3.182  1.00 94.69           C  
+ATOM    235  C   TYR A  31      18.125   6.275   2.766  1.00 94.69           C  
+ATOM    236  CB  TYR A  31      15.725   6.446   2.026  1.00 94.69           C  
+ATOM    237  O   TYR A  31      18.502   5.108   2.895  1.00 94.69           O  
+ATOM    238  CG  TYR A  31      14.240   6.486   2.357  1.00 94.69           C  
+ATOM    239  CD1 TYR A  31      13.754   6.744   3.661  1.00 94.69           C  
+ATOM    240  CD2 TYR A  31      13.324   6.237   1.318  1.00 94.69           C  
+ATOM    241  CE1 TYR A  31      12.372   6.744   3.921  1.00 94.69           C  
+ATOM    242  CE2 TYR A  31      11.942   6.226   1.577  1.00 94.69           C  
+ATOM    243  OH  TYR A  31      10.127   6.431   3.110  1.00 94.69           O  
+ATOM    244  CZ  TYR A  31      11.464   6.473   2.878  1.00 94.69           C  
+ATOM    245  N   ASN A  32      18.929   7.226   2.288  1.00 92.88           N  
+ATOM    246  CA  ASN A  32      20.327   6.986   1.942  1.00 92.88           C  
+ATOM    247  C   ASN A  32      21.214   6.800   3.175  1.00 92.88           C  
+ATOM    248  CB  ASN A  32      20.823   8.125   1.038  1.00 92.88           C  
+ATOM    249  O   ASN A  32      22.059   5.910   3.170  1.00 92.88           O  
+ATOM    250  CG  ASN A  32      20.259   8.023  -0.366  1.00 92.88           C  
+ATOM    251  ND2 ASN A  32      20.500   9.000  -1.206  1.00 92.88           N  
+ATOM    252  OD1 ASN A  32      19.637   7.046  -0.746  1.00 92.88           O  
+ATOM    253  N   ILE A  33      21.016   7.580   4.242  1.00 95.38           N  
+ATOM    254  CA  ILE A  33      21.836   7.455   5.461  1.00 95.38           C  
+ATOM    255  C   ILE A  33      21.488   6.215   6.301  1.00 95.38           C  
+ATOM    256  CB  ILE A  33      21.806   8.745   6.307  1.00 95.38           C  
+ATOM    257  O   ILE A  33      22.294   5.795   7.132  1.00 95.38           O  
+ATOM    258  CG1 ILE A  33      20.398   9.076   6.838  1.00 95.38           C  
+ATOM    259  CG2 ILE A  33      22.380   9.916   5.487  1.00 95.38           C  
+ATOM    260  CD1 ILE A  33      20.370  10.186   7.890  1.00 95.38           C  
+ATOM    261  N   LEU A  34      20.310   5.621   6.073  1.00 97.50           N  
+ATOM    262  CA  LEU A  34      19.822   4.432   6.781  1.00 97.50           C  
+ATOM    263  C   LEU A  34      20.388   3.116   6.244  1.00 97.50           C  
+ATOM    264  CB  LEU A  34      18.285   4.404   6.705  1.00 97.50           C  
+ATOM    265  O   LEU A  34      20.367   2.117   6.959  1.00 97.50           O  
+ATOM    266  CG  LEU A  34      17.600   5.548   7.468  1.00 97.50           C  
+ATOM    267  CD1 LEU A  34      16.100   5.547   7.176  1.00 97.50           C  
+ATOM    268  CD2 LEU A  34      17.849   5.474   8.962  1.00 97.50           C  
+ATOM    269  N   ARG A  35      20.884   3.102   5.004  1.00 95.75           N  
+ATOM    270  CA  ARG A  35      21.355   1.895   4.319  1.00 95.75           C  
+ATOM    271  C   ARG A  35      22.866   1.888   4.197  1.00 95.75           C  
+ATOM    272  CB  ARG A  35      20.718   1.801   2.932  1.00 95.75           C  
+ATOM    273  O   ARG A  35      23.490   2.927   3.973  1.00 95.75           O  
+ATOM    274  CG  ARG A  35      19.205   1.582   3.027  1.00 95.75           C  
+ATOM    275  CD  ARG A  35      18.590   1.550   1.635  1.00 95.75           C  
+ATOM    276  NE  ARG A  35      18.659   2.870   0.995  1.00 95.75           N  
+ATOM    277  NH1 ARG A  35      19.006   2.171  -1.174  1.00 95.75           N  
+ATOM    278  NH2 ARG A  35      19.004   4.316  -0.715  1.00 95.75           N  
+ATOM    279  CZ  ARG A  35      18.883   3.104  -0.279  1.00 95.75           C  
+ATOM    280  N   ARG A  36      23.457   0.701   4.282  1.00 93.69           N  
+ATOM    281  CA  ARG A  36      24.890   0.494   4.069  1.00 93.69           C  
+ATOM    282  C   ARG A  36      25.147  -0.442   2.883  1.00 93.69           C  
+ATOM    283  CB  ARG A  36      25.510  -0.046   5.360  1.00 93.69           C  
+ATOM    284  O   ARG A  36      24.294  -1.271   2.555  1.00 93.69           O  
+ATOM    285  CG  ARG A  36      25.356   0.850   6.598  1.00 93.69           C  
+ATOM    286  CD  ARG A  36      25.957   2.245   6.393  1.00 93.69           C  
+ATOM    287  NE  ARG A  36      25.836   3.064   7.611  1.00 93.69           N  
+ATOM    288  NH1 ARG A  36      26.206   5.104   6.627  1.00 93.69           N  
+ATOM    289  NH2 ARG A  36      25.857   4.989   8.827  1.00 93.69           N  
+ATOM    290  CZ  ARG A  36      25.963   4.376   7.682  1.00 93.69           C  
+ATOM    291  N   PRO A  37      26.310  -0.319   2.211  1.00 89.31           N  
+ATOM    292  CA  PRO A  37      26.663  -1.191   1.089  1.00 89.31           C  
+ATOM    293  C   PRO A  37      26.723  -2.685   1.434  1.00 89.31           C  
+ATOM    294  CB  PRO A  37      28.030  -0.698   0.605  1.00 89.31           C  
+ATOM    295  O   PRO A  37      26.521  -3.508   0.549  1.00 89.31           O  
+ATOM    296  CG  PRO A  37      28.058   0.767   1.031  1.00 89.31           C  
+ATOM    297  CD  PRO A  37      27.306   0.738   2.356  1.00 89.31           C  
+ATOM    298  N   ASP A  38      26.966  -3.032   2.700  1.00 91.00           N  
+ATOM    299  CA  ASP A  38      26.983  -4.413   3.205  1.00 91.00           C  
+ATOM    300  C   ASP A  38      25.578  -5.030   3.374  1.00 91.00           C  
+ATOM    301  CB  ASP A  38      27.793  -4.464   4.515  1.00 91.00           C  
+ATOM    302  O   ASP A  38      25.460  -6.192   3.748  1.00 91.00           O  
+ATOM    303  CG  ASP A  38      27.175  -3.664   5.669  1.00 91.00           C  
+ATOM    304  OD1 ASP A  38      26.068  -3.104   5.491  1.00 91.00           O  
+ATOM    305  OD2 ASP A  38      27.837  -3.522   6.712  1.00 91.00           O  
+ATOM    306  N   GLY A  39      24.513  -4.276   3.074  1.00 89.31           N  
+ATOM    307  CA  GLY A  39      23.124  -4.718   3.214  1.00 89.31           C  
+ATOM    308  C   GLY A  39      22.503  -4.416   4.579  1.00 89.31           C  
+ATOM    309  O   GLY A  39      21.310  -4.645   4.759  1.00 89.31           O  
+ATOM    310  N   THR A  40      23.257  -3.853   5.529  1.00 94.31           N  
+ATOM    311  CA  THR A  40      22.709  -3.487   6.841  1.00 94.31           C  
+ATOM    312  C   THR A  40      21.812  -2.245   6.771  1.00 94.31           C  
+ATOM    313  CB  THR A  40      23.787  -3.334   7.926  1.00 94.31           C  
+ATOM    314  O   THR A  40      22.010  -1.333   5.956  1.00 94.31           O  
+ATOM    315  CG2 THR A  40      24.585  -4.617   8.146  1.00 94.31           C  
+ATOM    316  OG1 THR A  40      24.686  -2.297   7.635  1.00 94.31           O  
+ATOM    317  N   PHE A  41      20.807  -2.216   7.654  1.00 97.94           N  
+ATOM    318  CA  PHE A  41      19.811  -1.152   7.769  1.00 97.94           C  
+ATOM    319  C   PHE A  41      19.744  -0.619   9.203  1.00 97.94           C  
+ATOM    320  CB  PHE A  41      18.443  -1.693   7.349  1.00 97.94           C  
+ATOM    321  O   PHE A  41      19.549  -1.379  10.148  1.00 97.94           O  
+ATOM    322  CG  PHE A  41      17.390  -0.616   7.214  1.00 97.94           C  
+ATOM    323  CD1 PHE A  41      16.512  -0.333   8.275  1.00 97.94           C  
+ATOM    324  CD2 PHE A  41      17.305   0.120   6.023  1.00 97.94           C  
+ATOM    325  CE1 PHE A  41      15.568   0.700   8.143  1.00 97.94           C  
+ATOM    326  CE2 PHE A  41      16.346   1.132   5.882  1.00 97.94           C  
+ATOM    327  CZ  PHE A  41      15.477   1.429   6.946  1.00 97.94           C  
+ATOM    328  N   ASN A  42      19.872   0.695   9.391  1.00 97.94           N  
+ATOM    329  CA  ASN A  42      19.769   1.304  10.716  1.00 97.94           C  
+ATOM    330  C   ASN A  42      18.303   1.574  11.093  1.00 97.94           C  
+ATOM    331  CB  ASN A  42      20.676   2.541  10.799  1.00 97.94           C  
+ATOM    332  O   ASN A  42      17.823   2.704  11.000  1.00 97.94           O  
+ATOM    333  CG  ASN A  42      20.804   3.057  12.227  1.00 97.94           C  
+ATOM    334  ND2 ASN A  42      21.839   3.812  12.508  1.00 97.94           N  
+ATOM    335  OD1 ASN A  42      19.999   2.803  13.110  1.00 97.94           O  
+ATOM    336  N   ARG A  43      17.595   0.536  11.553  1.00 97.06           N  
+ATOM    337  CA  ARG A  43      16.190   0.624  11.989  1.00 97.06           C  
+ATOM    338  C   ARG A  43      15.943   1.679  13.047  1.00 97.06           C  
+ATOM    339  CB  ARG A  43      15.763  -0.731  12.556  1.00 97.06           C  
+ATOM    340  O   ARG A  43      14.984   2.430  12.940  1.00 97.06           O  
+ATOM    341  CG  ARG A  43      14.283  -0.838  12.973  1.00 97.06           C  
+ATOM    342  CD  ARG A  43      13.292  -0.454  11.875  1.00 97.06           C  
+ATOM    343  NE  ARG A  43      13.452  -1.312  10.690  1.00 97.06           N  
+ATOM    344  NH1 ARG A  43      12.315   0.033   9.256  1.00 97.06           N  
+ATOM    345  NH2 ARG A  43      13.150  -1.922   8.540  1.00 97.06           N  
+ATOM    346  CZ  ARG A  43      12.968  -1.067   9.497  1.00 97.06           C  
+ATOM    347  N   HIS A  44      16.799   1.738  14.063  1.00 97.31           N  
+ATOM    348  CA  HIS A  44      16.622   2.666  15.174  1.00 97.31           C  
+ATOM    349  C   HIS A  44      16.631   4.119  14.682  1.00 97.31           C  
+ATOM    350  CB  HIS A  44      17.719   2.409  16.210  1.00 97.31           C  
+ATOM    351  O   HIS A  44      15.750   4.903  15.029  1.00 97.31           O  
+ATOM    352  CG  HIS A  44      17.600   3.324  17.398  1.00 97.31           C  
+ATOM    353  CD2 HIS A  44      16.916   3.074  18.556  1.00 97.31           C  
+ATOM    354  ND1 HIS A  44      18.103   4.602  17.490  1.00 97.31           N  
+ATOM    355  CE1 HIS A  44      17.730   5.109  18.675  1.00 97.31           C  
+ATOM    356  NE2 HIS A  44      17.011   4.210  19.362  1.00 97.31           N  
+ATOM    357  N   LEU A  45      17.585   4.470  13.813  1.00 97.88           N  
+ATOM    358  CA  LEU A  45      17.608   5.788  13.185  1.00 97.88           C  
+ATOM    359  C   LEU A  45      16.412   5.991  12.241  1.00 97.88           C  
+ATOM    360  CB  LEU A  45      18.957   5.977  12.475  1.00 97.88           C  
+ATOM    361  O   LEU A  45      15.879   7.094  12.183  1.00 97.88           O  
+ATOM    362  CG  LEU A  45      19.160   7.370  11.853  1.00 97.88           C  
+ATOM    363  CD1 LEU A  45      19.098   8.492  12.892  1.00 97.88           C  
+ATOM    364  CD2 LEU A  45      20.529   7.430  11.172  1.00 97.88           C  
+ATOM    365  N   ALA A  46      15.960   4.948  11.536  1.00 97.88           N  
+ATOM    366  CA  ALA A  46      14.789   5.033  10.661  1.00 97.88           C  
+ATOM    367  C   ALA A  46      13.519   5.382  11.448  1.00 97.88           C  
+ATOM    368  CB  ALA A  46      14.615   3.717   9.890  1.00 97.88           C  
+ATOM    369  O   ALA A  46      12.772   6.266  11.038  1.00 97.88           O  
+ATOM    370  N   GLU A  47      13.305   4.736  12.598  1.00 97.19           N  
+ATOM    371  CA  GLU A  47      12.171   5.036  13.477  1.00 97.19           C  
+ATOM    372  C   GLU A  47      12.268   6.427  14.104  1.00 97.19           C  
+ATOM    373  CB  GLU A  47      12.040   3.982  14.590  1.00 97.19           C  
+ATOM    374  O   GLU A  47      11.242   7.079  14.276  1.00 97.19           O  
+ATOM    375  CG  GLU A  47      11.753   2.555  14.104  1.00 97.19           C  
+ATOM    376  CD  GLU A  47      10.672   2.491  13.021  1.00 97.19           C  
+ATOM    377  OE1 GLU A  47      10.898   1.805  11.996  1.00 97.19           O  
+ATOM    378  OE2 GLU A  47       9.590   3.101  13.170  1.00 97.19           O  
+ATOM    379  N   TYR A  48      13.482   6.896  14.408  1.00 96.31           N  
+ATOM    380  CA  TYR A  48      13.709   8.244  14.931  1.00 96.31           C  
+ATOM    381  C   TYR A  48      13.434   9.342  13.890  1.00 96.31           C  
+ATOM    382  CB  TYR A  48      15.149   8.332  15.453  1.00 96.31           C  
+ATOM    383  O   TYR A  48      12.873  10.384  14.222  1.00 96.31           O  
+ATOM    384  CG  TYR A  48      15.499   9.690  16.027  1.00 96.31           C  
+ATOM    385  CD1 TYR A  48      16.164  10.646  15.233  1.00 96.31           C  
+ATOM    386  CD2 TYR A  48      15.112  10.011  17.341  1.00 96.31           C  
+ATOM    387  CE1 TYR A  48      16.440  11.924  15.755  1.00 96.31           C  
+ATOM    388  CE2 TYR A  48      15.386  11.288  17.867  1.00 96.31           C  
+ATOM    389  OH  TYR A  48      16.310  13.484  17.569  1.00 96.31           O  
+ATOM    390  CZ  TYR A  48      16.048  12.248  17.071  1.00 96.31           C  
+ATOM    391  N   LEU A  49      13.838   9.127  12.634  1.00 96.44           N  
+ATOM    392  CA  LEU A  49      13.672  10.106  11.553  1.00 96.44           C  
+ATOM    393  C   LEU A  49      12.261  10.113  10.955  1.00 96.44           C  
+ATOM    394  CB  LEU A  49      14.705   9.828  10.449  1.00 96.44           C  
+ATOM    395  O   LEU A  49      11.853  11.116  10.366  1.00 96.44           O  
+ATOM    396  CG  LEU A  49      16.165  10.088  10.851  1.00 96.44           C  
+ATOM    397  CD1 LEU A  49      17.076   9.675   9.696  1.00 96.44           C  
+ATOM    398  CD2 LEU A  49      16.443  11.558  11.173  1.00 96.44           C  
+ATOM    399  N   ASP A  50      11.523   9.010  11.067  1.00 95.69           N  
+ATOM    400  CA  ASP A  50      10.157   8.939  10.568  1.00 95.69           C  
+ATOM    401  C   ASP A  50       9.202   9.714  11.481  1.00 95.69           C  
+ATOM    402  CB  ASP A  50       9.721   7.485  10.382  1.00 95.69           C  
+ATOM    403  O   ASP A  50       8.969   9.362  12.638  1.00 95.69           O  
+ATOM    404  CG  ASP A  50       8.328   7.384   9.757  1.00 95.69           C  
+ATOM    405  OD1 ASP A  50       7.851   8.344   9.117  1.00 95.69           O  
+ATOM    406  OD2 ASP A  50       7.715   6.304   9.895  1.00 95.69           O  
+ATOM    407  N   ARG A  51       8.602  10.774  10.938  1.00 96.19           N  
+ATOM    408  CA  ARG A  51       7.618  11.574  11.660  1.00 96.19           C  
+ATOM    409  C   ARG A  51       6.355  10.744  11.885  1.00 96.19           C  
+ATOM    410  CB  ARG A  51       7.347  12.870  10.890  1.00 96.19           C  
+ATOM    411  O   ARG A  51       5.556  10.567  10.967  1.00 96.19           O  
+ATOM    412  CG  ARG A  51       6.376  13.773  11.661  1.00 96.19           C  
+ATOM    413  CD  ARG A  51       6.117  15.057  10.875  1.00 96.19           C  
+ATOM    414  NE  ARG A  51       5.069  15.868  11.518  1.00 96.19           N  
+ATOM    415  NH1 ARG A  51       5.197  17.682  10.117  1.00 96.19           N  
+ATOM    416  NH2 ARG A  51       3.685  17.659  11.759  1.00 96.19           N  
+ATOM    417  CZ  ARG A  51       4.658  17.062  11.131  1.00 96.19           C  
+ATOM    418  N   LYS A  52       6.146  10.293  13.119  1.00 97.00           N  
+ATOM    419  CA  LYS A  52       4.953   9.538  13.520  1.00 97.00           C  
+ATOM    420  C   LYS A  52       3.800  10.455  13.932  1.00 97.00           C  
+ATOM    421  CB  LYS A  52       5.289   8.550  14.647  1.00 97.00           C  
+ATOM    422  O   LYS A  52       4.009  11.580  14.384  1.00 97.00           O  
+ATOM    423  CG  LYS A  52       6.527   7.668  14.421  1.00 97.00           C  
+ATOM    424  CD  LYS A  52       6.516   6.826  13.137  1.00 97.00           C  
+ATOM    425  CE  LYS A  52       7.710   5.868  13.233  1.00 97.00           C  
+ATOM    426  NZ  LYS A  52       7.753   4.871  12.147  1.00 97.00           N  
+ATOM    427  N   VAL A  53       2.581   9.939  13.826  1.00 97.38           N  
+ATOM    428  CA  VAL A  53       1.355  10.533  14.376  1.00 97.38           C  
+ATOM    429  C   VAL A  53       0.778   9.569  15.411  1.00 97.38           C  
+ATOM    430  CB  VAL A  53       0.359  10.871  13.246  1.00 97.38           C  
+ATOM    431  O   VAL A  53       0.826   8.350  15.230  1.00 97.38           O  
+ATOM    432  CG1 VAL A  53      -0.954  11.459  13.768  1.00 97.38           C  
+ATOM    433  CG2 VAL A  53       0.967  11.911  12.299  1.00 97.38           C  
+ATOM    434  N   THR A  54       0.246  10.074  16.520  1.00 96.31           N  
+ATOM    435  CA  THR A  54      -0.457   9.246  17.511  1.00 96.31           C  
+ATOM    436  C   THR A  54      -1.887   8.969  17.057  1.00 96.31           C  
+ATOM    437  CB  THR A  54      -0.459   9.900  18.898  1.00 96.31           C  
+ATOM    438  O   THR A  54      -2.488   9.780  16.355  1.00 96.31           O  
+ATOM    439  CG2 THR A  54       0.964  10.041  19.439  1.00 96.31           C  
+ATOM    440  OG1 THR A  54      -1.018  11.189  18.827  1.00 96.31           O  
+ATOM    441  N   ALA A  55      -2.451   7.834  17.466  1.00 95.50           N  
+ATOM    442  CA  ALA A  55      -3.881   7.601  17.310  1.00 95.50           C  
+ATOM    443  C   ALA A  55      -4.661   8.733  17.997  1.00 95.50           C  
+ATOM    444  CB  ALA A  55      -4.225   6.237  17.901  1.00 95.50           C  
+ATOM    445  O   ALA A  55      -4.227   9.237  19.037  1.00 95.50           O  
+ATOM    446  N   ASN A  56      -5.774   9.157  17.402  1.00 92.06           N  
+ATOM    447  CA  ASN A  56      -6.552  10.279  17.901  1.00 92.06           C  
+ATOM    448  C   ASN A  56      -8.054   9.985  17.838  1.00 92.06           C  
+ATOM    449  CB  ASN A  56      -6.153  11.557  17.140  1.00 92.06           C  
+ATOM    450  O   ASN A  56      -8.658   9.970  16.764  1.00 92.06           O  
+ATOM    451  CG  ASN A  56      -6.598  12.810  17.871  1.00 92.06           C  
+ATOM    452  ND2 ASN A  56      -6.236  13.970  17.379  1.00 92.06           N  
+ATOM    453  OD1 ASN A  56      -7.231  12.768  18.915  1.00 92.06           O  
+ATOM    454  N   ALA A  57      -8.647   9.789  19.017  1.00 88.44           N  
+ATOM    455  CA  ALA A  57     -10.092   9.684  19.189  1.00 88.44           C  
+ATOM    456  C   ALA A  57     -10.795  11.051  19.125  1.00 88.44           C  
+ATOM    457  CB  ALA A  57     -10.385   8.948  20.501  1.00 88.44           C  
+ATOM    458  O   ALA A  57     -12.016  11.091  19.127  1.00 88.44           O  
+ATOM    459  N   ASN A  58     -10.064  12.168  19.031  1.00 88.94           N  
+ATOM    460  CA  ASN A  58     -10.635  13.434  18.577  1.00 88.94           C  
+ATOM    461  C   ASN A  58     -10.563  13.487  17.044  1.00 88.94           C  
+ATOM    462  CB  ASN A  58      -9.913  14.625  19.216  1.00 88.94           C  
+ATOM    463  O   ASN A  58      -9.481  13.242  16.493  1.00 88.94           O  
+ATOM    464  CG  ASN A  58     -10.029  14.596  20.722  1.00 88.94           C  
+ATOM    465  ND2 ASN A  58      -8.931  14.587  21.438  1.00 88.94           N  
+ATOM    466  OD1 ASN A  58     -11.107  14.570  21.275  1.00 88.94           O  
+ATOM    467  N   PRO A  59     -11.664  13.808  16.339  1.00 89.50           N  
+ATOM    468  CA  PRO A  59     -11.663  13.847  14.885  1.00 89.50           C  
+ATOM    469  C   PRO A  59     -10.626  14.837  14.355  1.00 89.50           C  
+ATOM    470  CB  PRO A  59     -13.077  14.256  14.457  1.00 89.50           C  
+ATOM    471  O   PRO A  59     -10.630  16.010  14.722  1.00 89.50           O  
+ATOM    472  CG  PRO A  59     -13.939  13.901  15.661  1.00 89.50           C  
+ATOM    473  CD  PRO A  59     -12.993  14.134  16.838  1.00 89.50           C  
+ATOM    474  N   VAL A  60      -9.775  14.378  13.442  1.00 91.44           N  
+ATOM    475  CA  VAL A  60      -8.938  15.251  12.606  1.00 91.44           C  
+ATOM    476  C   VAL A  60      -9.426  15.079  11.179  1.00 91.44           C  
+ATOM    477  CB  VAL A  60      -7.437  14.949  12.745  1.00 91.44           C  
+ATOM    478  O   VAL A  60      -9.480  13.947  10.692  1.00 91.44           O  
+ATOM    479  CG1 VAL A  60      -6.590  15.908  11.903  1.00 91.44           C  
+ATOM    480  CG2 VAL A  60      -6.973  15.035  14.204  1.00 91.44           C  
+ATOM    481  N   ASP A  61      -9.841  16.182  10.557  1.00 87.81           N  
+ATOM    482  CA  ASP A  61     -10.486  16.217   9.237  1.00 87.81           C  
+ATOM    483  C   ASP A  61     -11.724  15.304   9.140  1.00 87.81           C  
+ATOM    484  CB  ASP A  61      -9.445  15.997   8.127  1.00 87.81           C  
+ATOM    485  O   ASP A  61     -11.965  14.644   8.132  1.00 87.81           O  
+ATOM    486  CG  ASP A  61      -8.288  16.996   8.212  1.00 87.81           C  
+ATOM    487  OD1 ASP A  61      -8.553  18.170   8.559  1.00 87.81           O  
+ATOM    488  OD2 ASP A  61      -7.140  16.570   7.947  1.00 87.81           O  
+ATOM    489  N   GLY A  62     -12.507  15.235  10.224  1.00 92.38           N  
+ATOM    490  CA  GLY A  62     -13.709  14.396  10.300  1.00 92.38           C  
+ATOM    491  C   GLY A  62     -13.427  12.896  10.440  1.00 92.38           C  
+ATOM    492  O   GLY A  62     -14.308  12.085  10.160  1.00 92.38           O  
+ATOM    493  N   VAL A  63     -12.216  12.509  10.858  1.00 95.50           N  
+ATOM    494  CA  VAL A  63     -11.792  11.104  10.962  1.00 95.50           C  
+ATOM    495  C   VAL A  63     -11.243  10.795  12.354  1.00 95.50           C  
+ATOM    496  CB  VAL A  63     -10.765  10.777   9.857  1.00 95.50           C  
+ATOM    497  O   VAL A  63     -10.321  11.462  12.833  1.00 95.50           O  
+ATOM    498  CG1 VAL A  63     -10.283   9.324   9.911  1.00 95.50           C  
+ATOM    499  CG2 VAL A  63     -11.355  11.028   8.466  1.00 95.50           C  
+ATOM    500  N   PHE A  64     -11.763   9.745  12.988  1.00 95.31           N  
+ATOM    501  CA  PHE A  64     -11.158   9.142  14.178  1.00 95.31           C  
+ATOM    502  C   PHE A  64     -10.027   8.197  13.779  1.00 95.31           C  
+ATOM    503  CB  PHE A  64     -12.203   8.344  14.964  1.00 95.31           C  
+ATOM    504  O   PHE A  64     -10.112   7.543  12.737  1.00 95.31           O  
+ATOM    505  CG  PHE A  64     -13.352   9.147  15.530  1.00 95.31           C  
+ATOM    506  CD1 PHE A  64     -13.098  10.171  16.456  1.00 95.31           C  
+ATOM    507  CD2 PHE A  64     -14.680   8.810  15.215  1.00 95.31           C  
+ATOM    508  CE1 PHE A  64     -14.170  10.835  17.077  1.00 95.31           C  
+ATOM    509  CE2 PHE A  64     -15.746   9.487  15.834  1.00 95.31           C  
+ATOM    510  CZ  PHE A  64     -15.493  10.518  16.749  1.00 95.31           C  
+ATOM    511  N   SER A  65      -9.009   8.057  14.629  1.00 96.56           N  
+ATOM    512  CA  SER A  65      -8.051   6.958  14.519  1.00 96.56           C  
+ATOM    513  C   SER A  65      -7.750   6.282  15.853  1.00 96.56           C  
+ATOM    514  CB  SER A  65      -6.773   7.379  13.790  1.00 96.56           C  
+ATOM    515  O   SER A  65      -7.631   6.938  16.885  1.00 96.56           O  
+ATOM    516  OG  SER A  65      -6.019   8.344  14.508  1.00 96.56           O  
+ATOM    517  N   PHE A  66      -7.605   4.959  15.826  1.00 96.50           N  
+ATOM    518  CA  PHE A  66      -7.262   4.132  16.985  1.00 96.50           C  
+ATOM    519  C   PHE A  66      -6.407   2.939  16.560  1.00 96.50           C  
+ATOM    520  CB  PHE A  66      -8.520   3.711  17.753  1.00 96.50           C  
+ATOM    521  O   PHE A  66      -6.448   2.518  15.405  1.00 96.50           O  
+ATOM    522  CG  PHE A  66      -9.472   2.804  16.999  1.00 96.50           C  
+ATOM    523  CD1 PHE A  66     -10.339   3.338  16.027  1.00 96.50           C  
+ATOM    524  CD2 PHE A  66      -9.521   1.430  17.300  1.00 96.50           C  
+ATOM    525  CE1 PHE A  66     -11.264   2.506  15.374  1.00 96.50           C  
+ATOM    526  CE2 PHE A  66     -10.455   0.602  16.656  1.00 96.50           C  
+ATOM    527  CZ  PHE A  66     -11.332   1.141  15.700  1.00 96.50           C  
+ATOM    528  N   ASP A  67      -5.604   2.415  17.483  1.00 97.06           N  
+ATOM    529  CA  ASP A  67      -4.716   1.287  17.212  1.00 97.06           C  
+ATOM    530  C   ASP A  67      -5.321  -0.016  17.740  1.00 97.06           C  
+ATOM    531  CB  ASP A  67      -3.313   1.551  17.783  1.00 97.06           C  
+ATOM    532  O   ASP A  67      -5.845  -0.068  18.853  1.00 97.06           O  
+ATOM    533  CG  ASP A  67      -2.591   2.715  17.093  1.00 97.06           C  
+ATOM    534  OD1 ASP A  67      -2.793   2.936  15.881  1.00 97.06           O  
+ATOM    535  OD2 ASP A  67      -1.810   3.447  17.741  1.00 97.06           O  
+ATOM    536  N   VAL A  68      -5.212  -1.079  16.946  1.00 97.00           N  
+ATOM    537  CA  VAL A  68      -5.657  -2.433  17.291  1.00 97.00           C  
+ATOM    538  C   VAL A  68      -4.495  -3.408  17.170  1.00 97.00           C  
+ATOM    539  CB  VAL A  68      -6.859  -2.895  16.444  1.00 97.00           C  
+ATOM    540  O   VAL A  68      -3.670  -3.315  16.259  1.00 97.00           O  
+ATOM    541  CG1 VAL A  68      -8.085  -2.013  16.697  1.00 97.00           C  
+ATOM    542  CG2 VAL A  68      -6.561  -2.916  14.939  1.00 97.00           C  
+ATOM    543  N   LEU A  69      -4.429  -4.369  18.089  1.00 97.62           N  
+ATOM    544  CA  LEU A  69      -3.474  -5.471  18.018  1.00 97.62           C  
+ATOM    545  C   LEU A  69      -4.151  -6.663  17.337  1.00 97.62           C  
+ATOM    546  CB  LEU A  69      -2.948  -5.759  19.431  1.00 97.62           C  
+ATOM    547  O   LEU A  69      -4.929  -7.379  17.966  1.00 97.62           O  
+ATOM    548  CG  LEU A  69      -1.836  -6.819  19.494  1.00 97.62           C  
+ATOM    549  CD1 LEU A  69      -0.623  -6.468  18.637  1.00 97.62           C  
+ATOM    550  CD2 LEU A  69      -1.354  -6.914  20.942  1.00 97.62           C  
+ATOM    551  N   ILE A  70      -3.864  -6.842  16.048  1.00 98.06           N  
+ATOM    552  CA  ILE A  70      -4.466  -7.870  15.194  1.00 98.06           C  
+ATOM    553  C   ILE A  70      -3.999  -9.269  15.606  1.00 98.06           C  
+ATOM    554  CB  ILE A  70      -4.155  -7.559  13.709  1.00 98.06           C  
+ATOM    555  O   ILE A  70      -4.820 -10.174  15.757  1.00 98.06           O  
+ATOM    556  CG1 ILE A  70      -4.961  -6.314  13.269  1.00 98.06           C  
+ATOM    557  CG2 ILE A  70      -4.418  -8.788  12.830  1.00 98.06           C  
+ATOM    558  CD1 ILE A  70      -4.951  -6.031  11.762  1.00 98.06           C  
+ATOM    559  N   ASP A  71      -2.691  -9.445  15.814  1.00 98.25           N  
+ATOM    560  CA  ASP A  71      -2.105 -10.719  16.235  1.00 98.25           C  
+ATOM    561  C   ASP A  71      -1.022 -10.493  17.295  1.00 98.25           C  
+ATOM    562  CB  ASP A  71      -1.579 -11.504  15.022  1.00 98.25           C  
+ATOM    563  O   ASP A  71      -0.005  -9.837  17.058  1.00 98.25           O  
+ATOM    564  CG  ASP A  71      -1.249 -12.963  15.371  1.00 98.25           C  
+ATOM    565  OD1 ASP A  71      -0.629 -13.222  16.429  1.00 98.25           O  
+ATOM    566  OD2 ASP A  71      -1.640 -13.878  14.619  1.00 98.25           O  
+ATOM    567  N   ARG A  72      -1.244 -11.058  18.488  1.00 97.25           N  
+ATOM    568  CA  ARG A  72      -0.334 -10.925  19.635  1.00 97.25           C  
+ATOM    569  C   ARG A  72       0.963 -11.715  19.473  1.00 97.25           C  
+ATOM    570  CB  ARG A  72      -1.032 -11.363  20.932  1.00 97.25           C  
+ATOM    571  O   ARG A  72       1.959 -11.342  20.077  1.00 97.25           O  
+ATOM    572  CG  ARG A  72      -2.229 -10.477  21.294  1.00 97.25           C  
+ATOM    573  CD  ARG A  72      -2.725 -10.763  22.717  1.00 97.25           C  
+ATOM    574  NE  ARG A  72      -3.781  -9.812  23.120  1.00 97.25           N  
+ATOM    575  NH1 ARG A  72      -2.457  -8.238  24.159  1.00 97.25           N  
+ATOM    576  NH2 ARG A  72      -4.662  -7.930  24.051  1.00 97.25           N  
+ATOM    577  CZ  ARG A  72      -3.626  -8.671  23.774  1.00 97.25           C  
+ATOM    578  N   ARG A  73       0.970 -12.798  18.691  1.00 97.56           N  
+ATOM    579  CA  ARG A  73       2.130 -13.699  18.554  1.00 97.56           C  
+ATOM    580  C   ARG A  73       3.274 -13.020  17.810  1.00 97.56           C  
+ATOM    581  CB  ARG A  73       1.724 -14.973  17.801  1.00 97.56           C  
+ATOM    582  O   ARG A  73       4.433 -13.244  18.133  1.00 97.56           O  
+ATOM    583  CG  ARG A  73       0.569 -15.736  18.470  1.00 97.56           C  
+ATOM    584  CD  ARG A  73       0.091 -16.885  17.580  1.00 97.56           C  
+ATOM    585  NE  ARG A  73      -0.532 -16.390  16.336  1.00 97.56           N  
+ATOM    586  NH1 ARG A  73      -0.619 -18.398  15.223  1.00 97.56           N  
+ATOM    587  NH2 ARG A  73      -1.378 -16.545  14.241  1.00 97.56           N  
+ATOM    588  CZ  ARG A  73      -0.840 -17.111  15.275  1.00 97.56           C  
+ATOM    589  N   ILE A  74       2.922 -12.186  16.835  1.00 96.69           N  
+ATOM    590  CA  ILE A  74       3.862 -11.445  15.983  1.00 96.69           C  
+ATOM    591  C   ILE A  74       3.845  -9.933  16.247  1.00 96.69           C  
+ATOM    592  CB  ILE A  74       3.624 -11.783  14.497  1.00 96.69           C  
+ATOM    593  O   ILE A  74       4.509  -9.183  15.541  1.00 96.69           O  
+ATOM    594  CG1 ILE A  74       2.174 -11.473  14.072  1.00 96.69           C  
+ATOM    595  CG2 ILE A  74       3.969 -13.261  14.233  1.00 96.69           C  
+ATOM    596  CD1 ILE A  74       2.006 -11.392  12.553  1.00 96.69           C  
+ATOM    597  N   ASN A  75       3.101  -9.480  17.263  1.00 96.69           N  
+ATOM    598  CA  ASN A  75       2.888  -8.064  17.577  1.00 96.69           C  
+ATOM    599  C   ASN A  75       2.394  -7.236  16.374  1.00 96.69           C  
+ATOM    600  CB  ASN A  75       4.130  -7.476  18.267  1.00 96.69           C  
+ATOM    601  O   ASN A  75       2.832  -6.102  16.168  1.00 96.69           O  
+ATOM    602  CG  ASN A  75       4.414  -8.152  19.589  1.00 96.69           C  
+ATOM    603  ND2 ASN A  75       5.577  -8.739  19.748  1.00 96.69           N  
+ATOM    604  OD1 ASN A  75       3.592  -8.163  20.489  1.00 96.69           O  
+ATOM    605  N   LEU A  76       1.475  -7.795  15.579  1.00 98.56           N  
+ATOM    606  CA  LEU A  76       0.927  -7.111  14.410  1.00 98.56           C  
+ATOM    607  C   LEU A  76      -0.073  -6.042  14.852  1.00 98.56           C  
+ATOM    608  CB  LEU A  76       0.331  -8.124  13.423  1.00 98.56           C  
+ATOM    609  O   LEU A  76      -1.255  -6.316  15.071  1.00 98.56           O  
+ATOM    610  CG  LEU A  76      -0.197  -7.486  12.123  1.00 98.56           C  
+ATOM    611  CD1 LEU A  76       0.903  -6.769  11.338  1.00 98.56           C  
+ATOM    612  CD2 LEU A  76      -0.794  -8.577  11.240  1.00 98.56           C  
+ATOM    613  N   LEU A  77       0.424  -4.819  15.007  1.00 97.94           N  
+ATOM    614  CA  LEU A  77      -0.389  -3.630  15.227  1.00 97.94           C  
+ATOM    615  C   LEU A  77      -1.000  -3.165  13.899  1.00 97.94           C  
+ATOM    616  CB  LEU A  77       0.487  -2.540  15.872  1.00 97.94           C  
+ATOM    617  O   LEU A  77      -0.430  -3.358  12.826  1.00 97.94           O  
+ATOM    618  CG  LEU A  77      -0.301  -1.381  16.514  1.00 97.94           C  
+ATOM    619  CD1 LEU A  77      -0.926  -1.815  17.842  1.00 97.94           C  
+ATOM    620  CD2 LEU A  77       0.640  -0.208  16.787  1.00 97.94           C  
+ATOM    621  N   SER A  78      -2.151  -2.516  13.941  1.00 98.50           N  
+ATOM    622  CA  SER A  78      -2.691  -1.772  12.802  1.00 98.50           C  
+ATOM    623  C   SER A  78      -3.425  -0.540  13.294  1.00 98.50           C  
+ATOM    624  CB  SER A  78      -3.613  -2.659  11.968  1.00 98.50           C  
+ATOM    625  O   SER A  78      -3.982  -0.548  14.392  1.00 98.50           O  
+ATOM    626  OG  SER A  78      -2.830  -3.680  11.383  1.00 98.50           O  
+ATOM    627  N   ARG A  79      -3.424   0.515  12.480  1.00 98.50           N  
+ATOM    628  CA  ARG A  79      -4.207   1.714  12.762  1.00 98.50           C  
+ATOM    629  C   ARG A  79      -5.501   1.682  11.977  1.00 98.50           C  
+ATOM    630  CB  ARG A  79      -3.398   2.977  12.486  1.00 98.50           C  
+ATOM    631  O   ARG A  79      -5.483   1.578  10.755  1.00 98.50           O  
+ATOM    632  CG  ARG A  79      -4.261   4.202  12.833  1.00 98.50           C  
+ATOM    633  CD  ARG A  79      -3.377   5.400  13.127  1.00 98.50           C  
+ATOM    634  NE  ARG A  79      -2.757   5.231  14.441  1.00 98.50           N  
+ATOM    635  NH1 ARG A  79      -1.328   6.969  14.274  1.00 98.50           N  
+ATOM    636  NH2 ARG A  79      -1.140   5.526  15.988  1.00 98.50           N  
+ATOM    637  CZ  ARG A  79      -1.747   5.911  14.904  1.00 98.50           C  
+ATOM    638  N   VAL A  80      -6.613   1.819  12.679  1.00 98.62           N  
+ATOM    639  CA  VAL A  80      -7.933   1.976  12.082  1.00 98.62           C  
+ATOM    640  C   VAL A  80      -8.247   3.461  11.969  1.00 98.62           C  
+ATOM    641  CB  VAL A  80      -8.999   1.244  12.907  1.00 98.62           C  
+ATOM    642  O   VAL A  80      -8.073   4.208  12.930  1.00 98.62           O  
+ATOM    643  CG1 VAL A  80     -10.370   1.355  12.231  1.00 98.62           C  
+ATOM    644  CG2 VAL A  80      -8.657  -0.243  13.078  1.00 98.62           C  
+ATOM    645  N   TYR A  81      -8.741   3.870  10.807  1.00 98.12           N  
+ATOM    646  CA  TYR A  81      -9.306   5.182  10.529  1.00 98.12           C  
+ATOM    647  C   TYR A  81     -10.771   5.015  10.149  1.00 98.12           C  
+ATOM    648  CB  TYR A  81      -8.560   5.856   9.379  1.00 98.12           C  
+ATOM    649  O   TYR A  81     -11.097   4.171   9.316  1.00 98.12           O  
+ATOM    650  CG  TYR A  81      -7.087   6.058   9.609  1.00 98.12           C  
+ATOM    651  CD1 TYR A  81      -6.646   7.297  10.097  1.00 98.12           C  
+ATOM    652  CD2 TYR A  81      -6.166   5.028   9.331  1.00 98.12           C  
+ATOM    653  CE1 TYR A  81      -5.281   7.510  10.323  1.00 98.12           C  
+ATOM    654  CE2 TYR A  81      -4.792   5.244   9.550  1.00 98.12           C  
+ATOM    655  OH  TYR A  81      -3.045   6.690  10.348  1.00 98.12           O  
+ATOM    656  CZ  TYR A  81      -4.353   6.483  10.060  1.00 98.12           C  
+ATOM    657  N   ARG A  82     -11.660   5.822  10.723  1.00 96.50           N  
+ATOM    658  CA  ARG A  82     -13.094   5.762  10.408  1.00 96.50           C  
+ATOM    659  C   ARG A  82     -13.751   7.141  10.453  1.00 96.50           C  
+ATOM    660  CB  ARG A  82     -13.802   4.762  11.337  1.00 96.50           C  
+ATOM    661  O   ARG A  82     -13.245   8.010  11.171  1.00 96.50           O  
+ATOM    662  CG  ARG A  82     -13.649   5.098  12.831  1.00 96.50           C  
+ATOM    663  CD  ARG A  82     -14.616   4.277  13.687  1.00 96.50           C  
+ATOM    664  NE  ARG A  82     -15.992   4.775  13.527  1.00 96.50           N  
+ATOM    665  NH1 ARG A  82     -17.079   3.017  14.522  1.00 96.50           N  
+ATOM    666  NH2 ARG A  82     -18.238   4.648  13.554  1.00 96.50           N  
+ATOM    667  CZ  ARG A  82     -17.091   4.141  13.876  1.00 96.50           C  
+ATOM    668  N   PRO A  83     -14.872   7.349   9.740  1.00 94.81           N  
+ATOM    669  CA  PRO A  83     -15.596   8.611   9.784  1.00 94.81           C  
+ATOM    670  C   PRO A  83     -16.023   8.949  11.213  1.00 94.81           C  
+ATOM    671  CB  PRO A  83     -16.804   8.446   8.855  1.00 94.81           C  
+ATOM    672  O   PRO A  83     -16.408   8.061  11.987  1.00 94.81           O  
+ATOM    673  CG  PRO A  83     -16.394   7.311   7.919  1.00 94.81           C  
+ATOM    674  CD  PRO A  83     -15.544   6.430   8.829  1.00 94.81           C  
+ATOM    675  N   ALA A  84     -15.943  10.232  11.551  1.00 90.50           N  
+ATOM    676  CA  ALA A  84     -16.513  10.762  12.773  1.00 90.50           C  
+ATOM    677  C   ALA A  84     -18.030  10.942  12.649  1.00 90.50           C  
+ATOM    678  CB  ALA A  84     -15.787  12.049  13.170  1.00 90.50           C  
+ATOM    679  O   ALA A  84     -18.549  11.218  11.568  1.00 90.50           O  
+ATOM    680  N   TYR A  85     -18.731  10.789  13.771  1.00 79.38           N  
+ATOM    681  CA  TYR A  85     -20.151  11.120  13.874  1.00 79.38           C  
+ATOM    682  C   TYR A  85     -20.314  12.642  14.000  1.00 79.38           C  
+ATOM    683  CB  TYR A  85     -20.761  10.385  15.073  1.00 79.38           C  
+ATOM    684  O   TYR A  85     -19.446  13.296  14.578  1.00 79.38           O  
+ATOM    685  CG  TYR A  85     -20.504   8.886  15.116  1.00 79.38           C  
+ATOM    686  CD1 TYR A  85     -21.231   8.015  14.279  1.00 79.38           C  
+ATOM    687  CD2 TYR A  85     -19.582   8.358  16.043  1.00 79.38           C  
+ATOM    688  CE1 TYR A  85     -21.072   6.620  14.400  1.00 79.38           C  
+ATOM    689  CE2 TYR A  85     -19.420   6.965  16.168  1.00 79.38           C  
+ATOM    690  OH  TYR A  85     -20.107   4.750  15.564  1.00 79.38           O  
+ATOM    691  CZ  TYR A  85     -20.195   6.095  15.372  1.00 79.38           C  
+ATOM    692  N   ALA A  86     -21.409  13.202  13.479  1.00 67.88           N  
+ATOM    693  CA  ALA A  86     -21.630  14.652  13.468  1.00 67.88           C  
+ATOM    694  C   ALA A  86     -21.754  15.269  14.880  1.00 67.88           C  
+ATOM    695  CB  ALA A  86     -22.869  14.942  12.609  1.00 67.88           C  
+ATOM    696  O   ALA A  86     -21.281  16.382  15.092  1.00 67.88           O  
+ATOM    697  N   ASP A  87     -22.304  14.523  15.848  1.00 65.94           N  
+ATOM    698  CA  ASP A  87     -22.799  15.074  17.121  1.00 65.94           C  
+ATOM    699  C   ASP A  87     -22.154  14.423  18.364  1.00 65.94           C  
+ATOM    700  CB  ASP A  87     -24.334  14.941  17.158  1.00 65.94           C  
+ATOM    701  O   ASP A  87     -22.830  13.780  19.168  1.00 65.94           O  
+ATOM    702  CG  ASP A  87     -25.046  15.637  15.997  1.00 65.94           C  
+ATOM    703  OD1 ASP A  87     -24.635  16.761  15.636  1.00 65.94           O  
+ATOM    704  OD2 ASP A  87     -25.995  15.020  15.463  1.00 65.94           O  
+ATOM    705  N   GLN A  88     -20.834  14.531  18.540  1.00 66.69           N  
+ATOM    706  CA  GLN A  88     -20.154  14.002  19.737  1.00 66.69           C  
+ATOM    707  C   GLN A  88     -19.533  15.131  20.571  1.00 66.69           C  
+ATOM    708  CB  GLN A  88     -19.119  12.936  19.339  1.00 66.69           C  
+ATOM    709  O   GLN A  88     -18.558  15.751  20.155  1.00 66.69           O  
+ATOM    710  CG  GLN A  88     -19.741  11.635  18.799  1.00 66.69           C  
+ATOM    711  CD  GLN A  88     -20.251  10.686  19.883  1.00 66.69           C  
+ATOM    712  NE2 GLN A  88     -21.500  10.284  19.840  1.00 66.69           N  
+ATOM    713  OE1 GLN A  88     -19.530  10.243  20.758  1.00 66.69           O  
+ATOM    714  N   GLU A  89     -20.075  15.370  21.772  1.00 69.81           N  
+ATOM    715  CA  GLU A  89     -19.503  16.312  22.753  1.00 69.81           C  
+ATOM    716  C   GLU A  89     -18.233  15.768  23.428  1.00 69.81           C  
+ATOM    717  CB  GLU A  89     -20.524  16.620  23.860  1.00 69.81           C  
+ATOM    718  O   GLU A  89     -17.376  16.540  23.859  1.00 69.81           O  
+ATOM    719  CG  GLU A  89     -21.737  17.438  23.393  1.00 69.81           C  
+ATOM    720  CD  GLU A  89     -22.655  17.854  24.559  1.00 69.81           C  
+ATOM    721  OE1 GLU A  89     -23.558  18.684  24.310  1.00 69.81           O  
+ATOM    722  OE2 GLU A  89     -22.454  17.354  25.691  1.00 69.81           O  
+ATOM    723  N   GLN A  90     -18.113  14.440  23.536  1.00 77.12           N  
+ATOM    724  CA  GLN A  90     -16.991  13.758  24.182  1.00 77.12           C  
+ATOM    725  C   GLN A  90     -16.312  12.769  23.225  1.00 77.12           C  
+ATOM    726  CB  GLN A  90     -17.447  13.047  25.465  1.00 77.12           C  
+ATOM    727  O   GLN A  90     -16.977  12.215  22.347  1.00 77.12           O  
+ATOM    728  CG  GLN A  90     -17.899  14.047  26.539  1.00 77.12           C  
+ATOM    729  CD  GLN A  90     -18.208  13.398  27.884  1.00 77.12           C  
+ATOM    730  NE2 GLN A  90     -18.600  14.179  28.865  1.00 77.12           N  
+ATOM    731  OE1 GLN A  90     -18.099  12.202  28.101  1.00 77.12           O  
+ATOM    732  N   PRO A  91     -15.000  12.509  23.389  1.00 79.38           N  
+ATOM    733  CA  PRO A  91     -14.299  11.531  22.568  1.00 79.38           C  
+ATOM    734  C   PRO A  91     -14.860  10.120  22.819  1.00 79.38           C  
+ATOM    735  CB  PRO A  91     -12.816  11.621  22.947  1.00 79.38           C  
+ATOM    736  O   PRO A  91     -14.985   9.722  23.981  1.00 79.38           O  
+ATOM    737  CG  PRO A  91     -12.708  12.925  23.737  1.00 79.38           C  
+ATOM    738  CD  PRO A  91     -14.088  13.109  24.346  1.00 79.38           C  
+ATOM    739  N   PRO A  92     -15.161   9.339  21.768  1.00 86.50           N  
+ATOM    740  CA  PRO A  92     -15.657   7.976  21.919  1.00 86.50           C  
+ATOM    741  C   PRO A  92     -14.606   7.064  22.562  1.00 86.50           C  
+ATOM    742  CB  PRO A  92     -16.024   7.524  20.502  1.00 86.50           C  
+ATOM    743  O   PRO A  92     -13.401   7.204  22.326  1.00 86.50           O  
+ATOM    744  CG  PRO A  92     -15.098   8.352  19.620  1.00 86.50           C  
+ATOM    745  CD  PRO A  92     -15.021   9.679  20.361  1.00 86.50           C  
+ATOM    746  N   SER A  93     -15.058   6.075  23.336  1.00 87.94           N  
+ATOM    747  CA  SER A  93     -14.176   5.000  23.794  1.00 87.94           C  
+ATOM    748  C   SER A  93     -13.784   4.078  22.630  1.00 87.94           C  
+ATOM    749  CB  SER A  93     -14.836   4.219  24.935  1.00 87.94           C  
+ATOM    750  O   SER A  93     -14.428   4.067  21.581  1.00 87.94           O  
+ATOM    751  OG  SER A  93     -15.861   3.379  24.451  1.00 87.94           O  
+ATOM    752  N   ILE A  94     -12.762   3.232  22.811  1.00 85.69           N  
+ATOM    753  CA  ILE A  94     -12.418   2.202  21.810  1.00 85.69           C  
+ATOM    754  C   ILE A  94     -13.621   1.287  21.527  1.00 85.69           C  
+ATOM    755  CB  ILE A  94     -11.171   1.399  22.250  1.00 85.69           C  
+ATOM    756  O   ILE A  94     -13.867   0.949  20.375  1.00 85.69           O  
+ATOM    757  CG1 ILE A  94      -9.924   2.310  22.164  1.00 85.69           C  
+ATOM    758  CG2 ILE A  94     -10.970   0.132  21.393  1.00 85.69           C  
+ATOM    759  CD1 ILE A  94      -8.657   1.706  22.784  1.00 85.69           C  
+ATOM    760  N   LEU A  95     -14.419   0.952  22.547  1.00 87.00           N  
+ATOM    761  CA  LEU A  95     -15.631   0.143  22.371  1.00 87.00           C  
+ATOM    762  C   LEU A  95     -16.691   0.860  21.526  1.00 87.00           C  
+ATOM    763  CB  LEU A  95     -16.218  -0.214  23.748  1.00 87.00           C  
+ATOM    764  O   LEU A  95     -17.440   0.207  20.805  1.00 87.00           O  
+ATOM    765  CG  LEU A  95     -15.346  -1.148  24.603  1.00 87.00           C  
+ATOM    766  CD1 LEU A  95     -15.969  -1.290  25.993  1.00 87.00           C  
+ATOM    767  CD2 LEU A  95     -15.223  -2.545  23.990  1.00 87.00           C  
+ATOM    768  N   ASP A  96     -16.754   2.191  21.587  1.00 88.88           N  
+ATOM    769  CA  ASP A  96     -17.636   2.986  20.729  1.00 88.88           C  
+ATOM    770  C   ASP A  96     -17.106   3.073  19.293  1.00 88.88           C  
+ATOM    771  CB  ASP A  96     -17.818   4.389  21.316  1.00 88.88           C  
+ATOM    772  O   ASP A  96     -17.885   3.052  18.338  1.00 88.88           O  
+ATOM    773  CG  ASP A  96     -18.447   4.356  22.701  1.00 88.88           C  
+ATOM    774  OD1 ASP A  96     -19.536   3.743  22.822  1.00 88.88           O  
+ATOM    775  OD2 ASP A  96     -17.805   4.893  23.632  1.00 88.88           O  
+ATOM    776  N   LEU A  97     -15.781   3.126  19.129  1.00 90.06           N  
+ATOM    777  CA  LEU A  97     -15.112   3.114  17.827  1.00 90.06           C  
+ATOM    778  C   LEU A  97     -15.176   1.745  17.131  1.00 90.06           C  
+ATOM    779  CB  LEU A  97     -13.655   3.576  17.998  1.00 90.06           C  
+ATOM    780  O   LEU A  97     -15.109   1.699  15.903  1.00 90.06           O  
+ATOM    781  CG  LEU A  97     -13.492   5.057  18.384  1.00 90.06           C  
+ATOM    782  CD1 LEU A  97     -12.022   5.365  18.671  1.00 90.06           C  
+ATOM    783  CD2 LEU A  97     -13.950   5.986  17.256  1.00 90.06           C  
+ATOM    784  N   GLU A  98     -15.375   0.662  17.882  1.00 91.19           N  
+ATOM    785  CA  GLU A  98     -15.646  -0.692  17.378  1.00 91.19           C  
+ATOM    786  C   GLU A  98     -17.152  -0.998  17.245  1.00 91.19           C  
+ATOM    787  CB  GLU A  98     -14.918  -1.746  18.231  1.00 91.19           C  
+ATOM    788  O   GLU A  98     -17.562  -2.157  17.198  1.00 91.19           O  
+ATOM    789  CG  GLU A  98     -13.388  -1.669  18.109  1.00 91.19           C  
+ATOM    790  CD  GLU A  98     -12.676  -2.881  18.737  1.00 91.19           C  
+ATOM    791  OE1 GLU A  98     -11.476  -3.067  18.424  1.00 91.19           O  
+ATOM    792  OE2 GLU A  98     -13.318  -3.670  19.468  1.00 91.19           O  
+ATOM    793  N   LYS A  99     -18.011   0.026  17.163  1.00 90.75           N  
+ATOM    794  CA  LYS A  99     -19.402  -0.130  16.700  1.00 90.75           C  
+ATOM    795  C   LYS A  99     -19.477   0.028  15.177  1.00 90.75           C  
+ATOM    796  CB  LYS A  99     -20.328   0.870  17.404  1.00 90.75           C  
+ATOM    797  O   LYS A  99     -18.703   0.821  14.632  1.00 90.75           O  
+ATOM    798  CG  LYS A  99     -20.472   0.531  18.894  1.00 90.75           C  
+ATOM    799  CD  LYS A  99     -21.215   1.635  19.652  1.00 90.75           C  
+ATOM    800  CE  LYS A  99     -21.308   1.255  21.134  1.00 90.75           C  
+ATOM    801  NZ  LYS A  99     -21.701   2.410  21.976  1.00 90.75           N  
+ATOM    802  N   PRO A 100     -20.408  -0.658  14.485  1.00 89.44           N  
+ATOM    803  CA  PRO A 100     -20.624  -0.451  13.056  1.00 89.44           C  
+ATOM    804  C   PRO A 100     -20.770   1.030  12.704  1.00 89.44           C  
+ATOM    805  CB  PRO A 100     -21.886  -1.240  12.694  1.00 89.44           C  
+ATOM    806  O   PRO A 100     -21.449   1.776  13.410  1.00 89.44           O  
+ATOM    807  CG  PRO A 100     -21.956  -2.322  13.769  1.00 89.44           C  
+ATOM    808  CD  PRO A 100     -21.328  -1.663  14.997  1.00 89.44           C  
+ATOM    809  N   VAL A 101     -20.118   1.455  11.624  1.00 87.88           N  
+ATOM    810  CA  VAL A 101     -20.333   2.789  11.051  1.00 87.88           C  
+ATOM    811  C   VAL A 101     -21.761   2.856  10.501  1.00 87.88           C  
+ATOM    812  CB  VAL A 101     -19.296   3.096   9.954  1.00 87.88           C  
+ATOM    813  O   VAL A 101     -22.299   1.857  10.018  1.00 87.88           O  
+ATOM    814  CG1 VAL A 101     -19.471   4.486   9.328  1.00 87.88           C  
+ATOM    815  CG2 VAL A 101     -17.850   3.029  10.467  1.00 87.88           C  
+ATOM    816  N   ASP A 102     -22.389   4.026  10.582  1.00 85.00           N  
+ATOM    817  CA  ASP A 102     -23.706   4.223   9.989  1.00 85.00           C  
+ATOM    818  C   ASP A 102     -23.628   4.283   8.461  1.00 85.00           C  
+ATOM    819  CB  ASP A 102     -24.400   5.449  10.591  1.00 85.00           C  
+ATOM    820  O   ASP A 102     -22.894   5.083   7.886  1.00 85.00           O  
+ATOM    821  CG  ASP A 102     -24.880   5.172  12.017  1.00 85.00           C  
+ATOM    822  OD1 ASP A 102     -25.372   4.031  12.250  1.00 85.00           O  
+ATOM    823  OD2 ASP A 102     -24.762   6.095  12.847  1.00 85.00           O  
+ATOM    824  N   GLY A 103     -24.400   3.406   7.824  1.00 85.38           N  
+ATOM    825  CA  GLY A 103     -24.471   3.207   6.382  1.00 85.38           C  
+ATOM    826  C   GLY A 103     -24.984   1.798   6.080  1.00 85.38           C  
+ATOM    827  O   GLY A 103     -24.753   0.872   6.865  1.00 85.38           O  
+ATOM    828  N   ASP A 104     -25.709   1.634   4.973  1.00 89.00           N  
+ATOM    829  CA  ASP A 104     -26.188   0.316   4.537  1.00 89.00           C  
+ATOM    830  C   ASP A 104     -25.023  -0.550   4.052  1.00 89.00           C  
+ATOM    831  CB  ASP A 104     -27.255   0.469   3.440  1.00 89.00           C  
+ATOM    832  O   ASP A 104     -24.880  -1.693   4.495  1.00 89.00           O  
+ATOM    833  CG  ASP A 104     -28.647   0.813   3.978  1.00 89.00           C  
+ATOM    834  OD1 ASP A 104     -28.871   0.658   5.201  1.00 89.00           O  
+ATOM    835  OD2 ASP A 104     -29.490   1.196   3.140  1.00 89.00           O  
+ATOM    836  N   ILE A 105     -24.162   0.027   3.207  1.00 93.06           N  
+ATOM    837  CA  ILE A 105     -22.852  -0.512   2.844  1.00 93.06           C  
+ATOM    838  C   ILE A 105     -21.771   0.462   3.309  1.00 93.06           C  
+ATOM    839  CB  ILE A 105     -22.751  -0.855   1.340  1.00 93.06           C  
+ATOM    840  O   ILE A 105     -21.838   1.661   3.048  1.00 93.06           O  
+ATOM    841  CG1 ILE A 105     -23.703  -2.028   1.008  1.00 93.06           C  
+ATOM    842  CG2 ILE A 105     -21.299  -1.262   1.017  1.00 93.06           C  
+ATOM    843  CD1 ILE A 105     -23.874  -2.319  -0.491  1.00 93.06           C  
+ATOM    844  N   VAL A 106     -20.775  -0.070   4.014  1.00 95.31           N  
+ATOM    845  CA  VAL A 106     -19.579   0.655   4.442  1.00 95.31           C  
+ATOM    846  C   VAL A 106     -18.362  -0.121   3.936  1.00 95.31           C  
+ATOM    847  CB  VAL A 106     -19.532   0.850   5.967  1.00 95.31           C  
+ATOM    848  O   VAL A 106     -18.054  -1.184   4.487  1.00 95.31           O  
+ATOM    849  CG1 VAL A 106     -18.283   1.663   6.351  1.00 95.31           C  
+ATOM    850  CG2 VAL A 106     -20.778   1.594   6.473  1.00 95.31           C  
+ATOM    851  N   PRO A 107     -17.663   0.380   2.902  1.00 97.81           N  
+ATOM    852  CA  PRO A 107     -16.454  -0.264   2.418  1.00 97.81           C  
+ATOM    853  C   PRO A 107     -15.349  -0.250   3.478  1.00 97.81           C  
+ATOM    854  CB  PRO A 107     -16.059   0.444   1.119  1.00 97.81           C  
+ATOM    855  O   PRO A 107     -15.245   0.667   4.302  1.00 97.81           O  
+ATOM    856  CG  PRO A 107     -16.914   1.710   1.055  1.00 97.81           C  
+ATOM    857  CD  PRO A 107     -18.026   1.529   2.081  1.00 97.81           C  
+ATOM    858  N   VAL A 108     -14.490  -1.266   3.431  1.00 98.69           N  
+ATOM    859  CA  VAL A 108     -13.315  -1.396   4.294  1.00 98.69           C  
+ATOM    860  C   VAL A 108     -12.072  -1.525   3.432  1.00 98.69           C  
+ATOM    861  CB  VAL A 108     -13.438  -2.578   5.268  1.00 98.69           C  
+ATOM    862  O   VAL A 108     -11.881  -2.530   2.755  1.00 98.69           O  
+ATOM    863  CG1 VAL A 108     -12.237  -2.601   6.226  1.00 98.69           C  
+ATOM    864  CG2 VAL A 108     -14.717  -2.465   6.103  1.00 98.69           C  
+ATOM    865  N   ILE A 109     -11.202  -0.522   3.481  1.00 98.88           N  
+ATOM    866  CA  ILE A 109      -9.926  -0.525   2.770  1.00 98.88           C  
+ATOM    867  C   ILE A 109      -8.835  -1.057   3.704  1.00 98.88           C  
+ATOM    868  CB  ILE A 109      -9.602   0.876   2.212  1.00 98.88           C  
+ATOM    869  O   ILE A 109      -8.480  -0.408   4.687  1.00 98.88           O  
+ATOM    870  CG1 ILE A 109     -10.700   1.358   1.231  1.00 98.88           C  
+ATOM    871  CG2 ILE A 109      -8.222   0.862   1.530  1.00 98.88           C  
+ATOM    872  CD1 ILE A 109     -10.472   2.757   0.644  1.00 98.88           C  
+ATOM    873  N   LEU A 110      -8.257  -2.213   3.386  1.00 98.94           N  
+ATOM    874  CA  LEU A 110      -7.026  -2.693   4.009  1.00 98.94           C  
+ATOM    875  C   LEU A 110      -5.831  -2.052   3.293  1.00 98.94           C  
+ATOM    876  CB  LEU A 110      -7.001  -4.228   3.963  1.00 98.94           C  
+ATOM    877  O   LEU A 110      -5.539  -2.377   2.141  1.00 98.94           O  
+ATOM    878  CG  LEU A 110      -5.860  -4.860   4.777  1.00 98.94           C  
+ATOM    879  CD1 LEU A 110      -6.030  -4.598   6.272  1.00 98.94           C  
+ATOM    880  CD2 LEU A 110      -5.870  -6.375   4.582  1.00 98.94           C  
+ATOM    881  N   PHE A 111      -5.176  -1.105   3.962  1.00 98.94           N  
+ATOM    882  CA  PHE A 111      -4.113  -0.287   3.391  1.00 98.94           C  
+ATOM    883  C   PHE A 111      -2.719  -0.797   3.779  1.00 98.94           C  
+ATOM    884  CB  PHE A 111      -4.318   1.192   3.746  1.00 98.94           C  
+ATOM    885  O   PHE A 111      -2.411  -0.991   4.958  1.00 98.94           O  
+ATOM    886  CG  PHE A 111      -3.314   2.108   3.070  1.00 98.94           C  
+ATOM    887  CD1 PHE A 111      -2.145   2.502   3.747  1.00 98.94           C  
+ATOM    888  CD2 PHE A 111      -3.518   2.519   1.739  1.00 98.94           C  
+ATOM    889  CE1 PHE A 111      -1.177   3.281   3.087  1.00 98.94           C  
+ATOM    890  CE2 PHE A 111      -2.547   3.291   1.076  1.00 98.94           C  
+ATOM    891  CZ  PHE A 111      -1.367   3.657   1.746  1.00 98.94           C  
+ATOM    892  N   PHE A 112      -1.853  -0.939   2.779  1.00 98.94           N  
+ATOM    893  CA  PHE A 112      -0.443  -1.279   2.921  1.00 98.94           C  
+ATOM    894  C   PHE A 112       0.408  -0.094   2.490  1.00 98.94           C  
+ATOM    895  CB  PHE A 112      -0.122  -2.514   2.080  1.00 98.94           C  
+ATOM    896  O   PHE A 112       0.361   0.341   1.340  1.00 98.94           O  
+ATOM    897  CG  PHE A 112      -0.956  -3.714   2.451  1.00 98.94           C  
+ATOM    898  CD1 PHE A 112      -0.645  -4.440   3.609  1.00 98.94           C  
+ATOM    899  CD2 PHE A 112      -2.077  -4.065   1.678  1.00 98.94           C  
+ATOM    900  CE1 PHE A 112      -1.448  -5.523   3.988  1.00 98.94           C  
+ATOM    901  CE2 PHE A 112      -2.871  -5.162   2.048  1.00 98.94           C  
+ATOM    902  CZ  PHE A 112      -2.555  -5.896   3.205  1.00 98.94           C  
+ATOM    903  N   HIS A 113       1.202   0.429   3.414  1.00 98.75           N  
+ATOM    904  CA  HIS A 113       2.052   1.569   3.122  1.00 98.75           C  
+ATOM    905  C   HIS A 113       3.231   1.205   2.203  1.00 98.75           C  
+ATOM    906  CB  HIS A 113       2.476   2.206   4.445  1.00 98.75           C  
+ATOM    907  O   HIS A 113       3.734   0.083   2.205  1.00 98.75           O  
+ATOM    908  CG  HIS A 113       3.431   1.408   5.291  1.00 98.75           C  
+ATOM    909  CD2 HIS A 113       3.140   0.696   6.423  1.00 98.75           C  
+ATOM    910  ND1 HIS A 113       4.783   1.299   5.080  1.00 98.75           N  
+ATOM    911  CE1 HIS A 113       5.294   0.525   6.044  1.00 98.75           C  
+ATOM    912  NE2 HIS A 113       4.334   0.148   6.902  1.00 98.75           N  
+ATOM    913  N   GLY A 114       3.706   2.166   1.419  1.00 98.12           N  
+ATOM    914  CA  GLY A 114       4.965   2.087   0.692  1.00 98.12           C  
+ATOM    915  C   GLY A 114       6.183   2.265   1.598  1.00 98.12           C  
+ATOM    916  O   GLY A 114       6.090   2.354   2.821  1.00 98.12           O  
+ATOM    917  N   GLY A 115       7.363   2.349   0.987  1.00 96.75           N  
+ATOM    918  CA  GLY A 115       8.636   2.386   1.719  1.00 96.75           C  
+ATOM    919  C   GLY A 115       9.620   1.300   1.297  1.00 96.75           C  
+ATOM    920  O   GLY A 115      10.612   1.065   1.979  1.00 96.75           O  
+ATOM    921  N   SER A 116       9.393   0.653   0.151  1.00 97.81           N  
+ATOM    922  CA  SER A 116      10.312  -0.348  -0.419  1.00 97.81           C  
+ATOM    923  C   SER A 116      10.556  -1.527   0.524  1.00 97.81           C  
+ATOM    924  CB  SER A 116      11.624   0.309  -0.860  1.00 97.81           C  
+ATOM    925  O   SER A 116      11.691  -1.967   0.681  1.00 97.81           O  
+ATOM    926  OG  SER A 116      11.333   1.532  -1.512  1.00 97.81           O  
+ATOM    927  N   PHE A 117       9.495  -1.964   1.211  1.00 98.62           N  
+ATOM    928  CA  PHE A 117       9.490  -2.998   2.258  1.00 98.62           C  
+ATOM    929  C   PHE A 117      10.313  -2.680   3.513  1.00 98.62           C  
+ATOM    930  CB  PHE A 117       9.889  -4.357   1.667  1.00 98.62           C  
+ATOM    931  O   PHE A 117      10.180  -3.392   4.504  1.00 98.62           O  
+ATOM    932  CG  PHE A 117       9.108  -4.736   0.429  1.00 98.62           C  
+ATOM    933  CD1 PHE A 117       7.833  -5.315   0.560  1.00 98.62           C  
+ATOM    934  CD2 PHE A 117       9.654  -4.509  -0.849  1.00 98.62           C  
+ATOM    935  CE1 PHE A 117       7.111  -5.677  -0.589  1.00 98.62           C  
+ATOM    936  CE2 PHE A 117       8.924  -4.859  -1.996  1.00 98.62           C  
+ATOM    937  CZ  PHE A 117       7.658  -5.449  -1.862  1.00 98.62           C  
+ATOM    938  N   ALA A 118      11.125  -1.619   3.501  1.00 98.44           N  
+ATOM    939  CA  ALA A 118      12.059  -1.293   4.569  1.00 98.44           C  
+ATOM    940  C   ALA A 118      11.650  -0.067   5.390  1.00 98.44           C  
+ATOM    941  CB  ALA A 118      13.450  -1.116   3.948  1.00 98.44           C  
+ATOM    942  O   ALA A 118      11.921  -0.021   6.585  1.00 98.44           O  
+ATOM    943  N   HIS A 119      10.994   0.921   4.786  1.00 97.88           N  
+ATOM    944  CA  HIS A 119      10.690   2.211   5.413  1.00 97.88           C  
+ATOM    945  C   HIS A 119       9.230   2.348   5.833  1.00 97.88           C  
+ATOM    946  CB  HIS A 119      11.089   3.345   4.466  1.00 97.88           C  
+ATOM    947  O   HIS A 119       8.384   1.558   5.420  1.00 97.88           O  
+ATOM    948  CG  HIS A 119      12.543   3.310   4.113  1.00 97.88           C  
+ATOM    949  CD2 HIS A 119      13.064   2.964   2.905  1.00 97.88           C  
+ATOM    950  ND1 HIS A 119      13.576   3.662   4.979  1.00 97.88           N  
+ATOM    951  CE1 HIS A 119      14.699   3.549   4.262  1.00 97.88           C  
+ATOM    952  NE2 HIS A 119      14.421   3.120   3.024  1.00 97.88           N  
+ATOM    953  N   SER A 120       8.968   3.399   6.618  1.00 97.62           N  
+ATOM    954  CA  SER A 120       7.636   3.832   7.064  1.00 97.62           C  
+ATOM    955  C   SER A 120       6.886   2.811   7.920  1.00 97.62           C  
+ATOM    956  CB  SER A 120       6.811   4.278   5.858  1.00 97.62           C  
+ATOM    957  O   SER A 120       7.391   1.729   8.196  1.00 97.62           O  
+ATOM    958  OG  SER A 120       7.448   5.375   5.222  1.00 97.62           O  
+ATOM    959  N   SER A 121       5.723   3.189   8.425  1.00 98.06           N  
+ATOM    960  CA  SER A 121       4.859   2.390   9.290  1.00 98.06           C  
+ATOM    961  C   SER A 121       3.409   2.843   9.126  1.00 98.06           C  
+ATOM    962  CB  SER A 121       5.309   2.533  10.748  1.00 98.06           C  
+ATOM    963  O   SER A 121       3.137   3.910   8.567  1.00 98.06           O  
+ATOM    964  OG  SER A 121       5.372   3.887  11.145  1.00 98.06           O  
+ATOM    965  N   ALA A 122       2.464   2.088   9.691  1.00 98.06           N  
+ATOM    966  CA  ALA A 122       1.041   2.449   9.663  1.00 98.06           C  
+ATOM    967  C   ALA A 122       0.731   3.829  10.281  1.00 98.06           C  
+ATOM    968  CB  ALA A 122       0.256   1.365  10.401  1.00 98.06           C  
+ATOM    969  O   ALA A 122      -0.303   4.421   9.983  1.00 98.06           O  
+ATOM    970  N   ASN A 123       1.620   4.341  11.139  1.00 97.25           N  
+ATOM    971  CA  ASN A 123       1.501   5.631  11.817  1.00 97.25           C  
+ATOM    972  C   ASN A 123       2.480   6.712  11.313  1.00 97.25           C  
+ATOM    973  CB  ASN A 123       1.591   5.412  13.341  1.00 97.25           C  
+ATOM    974  O   ASN A 123       2.605   7.747  11.972  1.00 97.25           O  
+ATOM    975  CG  ASN A 123       2.964   4.993  13.831  1.00 97.25           C  
+ATOM    976  ND2 ASN A 123       3.244   5.139  15.104  1.00 97.25           N  
+ATOM    977  OD1 ASN A 123       3.804   4.508  13.092  1.00 97.25           O  
+ATOM    978  N   SER A 124       3.180   6.514  10.187  1.00 98.25           N  
+ATOM    979  CA  SER A 124       3.944   7.602   9.553  1.00 98.25           C  
+ATOM    980  C   SER A 124       2.996   8.722   9.114  1.00 98.25           C  
+ATOM    981  CB  SER A 124       4.704   7.141   8.307  1.00 98.25           C  
+ATOM    982  O   SER A 124       1.943   8.458   8.535  1.00 98.25           O  
+ATOM    983  OG  SER A 124       5.527   6.024   8.537  1.00 98.25           O  
+ATOM    984  N   ALA A 125       3.376   9.984   9.310  1.00 96.94           N  
+ATOM    985  CA  ALA A 125       2.506  11.139   9.070  1.00 96.94           C  
+ATOM    986  C   ALA A 125       1.977  11.243   7.628  1.00 96.94           C  
+ATOM    987  CB  ALA A 125       3.281  12.407   9.454  1.00 96.94           C  
+ATOM    988  O   ALA A 125       0.842  11.673   7.412  1.00 96.94           O  
+ATOM    989  N   ILE A 126       2.777  10.827   6.640  1.00 95.75           N  
+ATOM    990  CA  ILE A 126       2.346  10.796   5.234  1.00 95.75           C  
+ATOM    991  C   ILE A 126       1.205   9.802   4.998  1.00 95.75           C  
+ATOM    992  CB  ILE A 126       3.523  10.513   4.277  1.00 95.75           C  
+ATOM    993  O   ILE A 126       0.256  10.123   4.286  1.00 95.75           O  
+ATOM    994  CG1 ILE A 126       4.337   9.245   4.641  1.00 95.75           C  
+ATOM    995  CG2 ILE A 126       4.417  11.761   4.216  1.00 95.75           C  
+ATOM    996  CD1 ILE A 126       5.270   8.773   3.521  1.00 95.75           C  
+ATOM    997  N   TYR A 127       1.258   8.634   5.639  1.00 97.88           N  
+ATOM    998  CA  TYR A 127       0.228   7.609   5.509  1.00 97.88           C  
+ATOM    999  C   TYR A 127      -0.965   7.889   6.408  1.00 97.88           C  
+ATOM   1000  CB  TYR A 127       0.821   6.216   5.738  1.00 97.88           C  
+ATOM   1001  O   TYR A 127      -2.082   7.599   5.997  1.00 97.88           O  
+ATOM   1002  CG  TYR A 127       1.814   5.842   4.659  1.00 97.88           C  
+ATOM   1003  CD1 TYR A 127       1.403   5.834   3.312  1.00 97.88           C  
+ATOM   1004  CD2 TYR A 127       3.154   5.564   4.987  1.00 97.88           C  
+ATOM   1005  CE1 TYR A 127       2.334   5.580   2.293  1.00 97.88           C  
+ATOM   1006  CE2 TYR A 127       4.080   5.304   3.961  1.00 97.88           C  
+ATOM   1007  OH  TYR A 127       4.594   5.022   1.666  1.00 97.88           O  
+ATOM   1008  CZ  TYR A 127       3.671   5.291   2.617  1.00 97.88           C  
+ATOM   1009  N   ASP A 128      -0.770   8.547   7.554  1.00 97.94           N  
+ATOM   1010  CA  ASP A 128      -1.879   9.075   8.353  1.00 97.94           C  
+ATOM   1011  C   ASP A 128      -2.727  10.054   7.531  1.00 97.94           C  
+ATOM   1012  CB  ASP A 128      -1.333   9.725   9.636  1.00 97.94           C  
+ATOM   1013  O   ASP A 128      -3.943   9.895   7.425  1.00 97.94           O  
+ATOM   1014  CG  ASP A 128      -2.446  10.041  10.639  1.00 97.94           C  
+ATOM   1015  OD1 ASP A 128      -2.793   9.130  11.428  1.00 97.94           O  
+ATOM   1016  OD2 ASP A 128      -2.958  11.180  10.650  1.00 97.94           O  
+ATOM   1017  N   THR A 129      -2.068  10.988   6.840  1.00 96.62           N  
+ATOM   1018  CA  THR A 129      -2.729  11.948   5.944  1.00 96.62           C  
+ATOM   1019  C   THR A 129      -3.452  11.244   4.791  1.00 96.62           C  
+ATOM   1020  CB  THR A 129      -1.709  12.946   5.375  1.00 96.62           C  
+ATOM   1021  O   THR A 129      -4.594  11.583   4.475  1.00 96.62           O  
+ATOM   1022  CG2 THR A 129      -2.370  14.041   4.537  1.00 96.62           C  
+ATOM   1023  OG1 THR A 129      -1.002  13.596   6.408  1.00 96.62           O  
+ATOM   1024  N   LEU A 130      -2.810  10.256   4.154  1.00 97.44           N  
+ATOM   1025  CA  LEU A 130      -3.409   9.511   3.044  1.00 97.44           C  
+ATOM   1026  C   LEU A 130      -4.611   8.668   3.498  1.00 97.44           C  
+ATOM   1027  CB  LEU A 130      -2.325   8.661   2.357  1.00 97.44           C  
+ATOM   1028  O   LEU A 130      -5.657   8.721   2.858  1.00 97.44           O  
+ATOM   1029  CG  LEU A 130      -2.818   7.857   1.136  1.00 97.44           C  
+ATOM   1030  CD1 LEU A 130      -3.431   8.745   0.049  1.00 97.44           C  
+ATOM   1031  CD2 LEU A 130      -1.638   7.107   0.520  1.00 97.44           C  
+ATOM   1032  N   CYS A 131      -4.510   7.949   4.617  1.00 98.50           N  
+ATOM   1033  CA  CYS A 131      -5.602   7.125   5.137  1.00 98.50           C  
+ATOM   1034  C   CYS A 131      -6.812   7.977   5.542  1.00 98.50           C  
+ATOM   1035  CB  CYS A 131      -5.115   6.284   6.322  1.00 98.50           C  
+ATOM   1036  O   CYS A 131      -7.937   7.644   5.176  1.00 98.50           O  
+ATOM   1037  SG  CYS A 131      -3.900   5.048   5.785  1.00 98.50           S  
+ATOM   1038  N   ARG A 132      -6.604   9.116   6.224  1.00 97.94           N  
+ATOM   1039  CA  ARG A 132      -7.694  10.060   6.545  1.00 97.94           C  
+ATOM   1040  C   ARG A 132      -8.393  10.571   5.292  1.00 97.94           C  
+ATOM   1041  CB  ARG A 132      -7.152  11.258   7.334  1.00 97.94           C  
+ATOM   1042  O   ARG A 132      -9.618  10.646   5.247  1.00 97.94           O  
+ATOM   1043  CG  ARG A 132      -6.776  10.866   8.761  1.00 97.94           C  
+ATOM   1044  CD  ARG A 132      -6.174  12.070   9.489  1.00 97.94           C  
+ATOM   1045  NE  ARG A 132      -5.695  11.676  10.819  1.00 97.94           N  
+ATOM   1046  NH1 ARG A 132      -7.738  11.531  11.844  1.00 97.94           N  
+ATOM   1047  NH2 ARG A 132      -5.876  10.888  12.924  1.00 97.94           N  
+ATOM   1048  CZ  ARG A 132      -6.443  11.372  11.859  1.00 97.94           C  
+ATOM   1049  N   ARG A 133      -7.620  10.871   4.250  1.00 97.31           N  
+ATOM   1050  CA  ARG A 133      -8.162  11.288   2.959  1.00 97.31           C  
+ATOM   1051  C   ARG A 133      -9.008  10.195   2.309  1.00 97.31           C  
+ATOM   1052  CB  ARG A 133      -6.997  11.736   2.086  1.00 97.31           C  
+ATOM   1053  O   ARG A 133     -10.107  10.498   1.859  1.00 97.31           O  
+ATOM   1054  CG  ARG A 133      -7.471  12.246   0.726  1.00 97.31           C  
+ATOM   1055  CD  ARG A 133      -6.278  12.744  -0.077  1.00 97.31           C  
+ATOM   1056  NE  ARG A 133      -5.614  13.873   0.615  1.00 97.31           N  
+ATOM   1057  NH1 ARG A 133      -3.417  13.169   0.463  1.00 97.31           N  
+ATOM   1058  NH2 ARG A 133      -3.908  15.065   1.516  1.00 97.31           N  
+ATOM   1059  CZ  ARG A 133      -4.323  14.023   0.856  1.00 97.31           C  
+ATOM   1060  N   LEU A 134      -8.544   8.946   2.302  1.00 98.31           N  
+ATOM   1061  CA  LEU A 134      -9.309   7.810   1.772  1.00 98.31           C  
+ATOM   1062  C   LEU A 134     -10.637   7.614   2.522  1.00 98.31           C  
+ATOM   1063  CB  LEU A 134      -8.453   6.533   1.831  1.00 98.31           C  
+ATOM   1064  O   LEU A 134     -11.673   7.442   1.880  1.00 98.31           O  
+ATOM   1065  CG  LEU A 134      -7.263   6.489   0.857  1.00 98.31           C  
+ATOM   1066  CD1 LEU A 134      -6.450   5.218   1.107  1.00 98.31           C  
+ATOM   1067  CD2 LEU A 134      -7.705   6.502  -0.604  1.00 98.31           C  
+ATOM   1068  N   VAL A 135     -10.633   7.736   3.856  1.00 97.88           N  
+ATOM   1069  CA  VAL A 135     -11.867   7.745   4.664  1.00 97.88           C  
+ATOM   1070  C   VAL A 135     -12.802   8.872   4.221  1.00 97.88           C  
+ATOM   1071  CB  VAL A 135     -11.555   7.875   6.168  1.00 97.88           C  
+ATOM   1072  O   VAL A 135     -13.983   8.634   3.971  1.00 97.88           O  
+ATOM   1073  CG1 VAL A 135     -12.815   8.096   7.010  1.00 97.88           C  
+ATOM   1074  CG2 VAL A 135     -10.874   6.617   6.706  1.00 97.88           C  
+ATOM   1075  N   GLY A 136     -12.286  10.096   4.085  1.00 95.25           N  
+ATOM   1076  CA  GLY A 136     -13.078  11.258   3.680  1.00 95.25           C  
+ATOM   1077  C   GLY A 136     -13.705  11.122   2.287  1.00 95.25           C  
+ATOM   1078  O   GLY A 136     -14.876  11.480   2.123  1.00 95.25           O  
+ATOM   1079  N   LEU A 137     -12.942  10.587   1.325  1.00 95.62           N  
+ATOM   1080  CA  LEU A 137     -13.310  10.431  -0.089  1.00 95.62           C  
+ATOM   1081  C   LEU A 137     -14.260   9.261  -0.358  1.00 95.62           C  
+ATOM   1082  CB  LEU A 137     -12.033  10.193  -0.915  1.00 95.62           C  
+ATOM   1083  O   LEU A 137     -15.158   9.388  -1.186  1.00 95.62           O  
+ATOM   1084  CG  LEU A 137     -11.120  11.412  -1.100  1.00 95.62           C  
+ATOM   1085  CD1 LEU A 137      -9.821  10.936  -1.741  1.00 95.62           C  
+ATOM   1086  CD2 LEU A 137     -11.744  12.476  -2.003  1.00 95.62           C  
+ATOM   1087  N   CYS A 138     -14.047   8.127   0.310  1.00 96.56           N  
+ATOM   1088  CA  CYS A 138     -14.793   6.890   0.046  1.00 96.56           C  
+ATOM   1089  C   CYS A 138     -15.834   6.584   1.130  1.00 96.56           C  
+ATOM   1090  CB  CYS A 138     -13.818   5.716  -0.131  1.00 96.56           C  
+ATOM   1091  O   CYS A 138     -16.474   5.543   1.078  1.00 96.56           O  
+ATOM   1092  SG  CYS A 138     -12.482   6.101  -1.296  1.00 96.56           S  
+ATOM   1093  N   LYS A 139     -15.948   7.440   2.161  1.00 94.50           N  
+ATOM   1094  CA  LYS A 139     -16.809   7.225   3.343  1.00 94.50           C  
+ATOM   1095  C   LYS A 139     -16.643   5.822   3.946  1.00 94.50           C  
+ATOM   1096  CB  LYS A 139     -18.273   7.580   3.015  1.00 94.50           C  
+ATOM   1097  O   LYS A 139     -17.593   5.203   4.409  1.00 94.50           O  
+ATOM   1098  CG  LYS A 139     -18.479   9.005   2.476  1.00 94.50           C  
+ATOM   1099  CD  LYS A 139     -17.896  10.075   3.407  1.00 94.50           C  
+ATOM   1100  CE  LYS A 139     -18.225  11.467   2.870  1.00 94.50           C  
+ATOM   1101  NZ  LYS A 139     -17.300  12.471   3.440  1.00 94.50           N  
+ATOM   1102  N   CYS A 140     -15.403   5.345   3.940  1.00 96.81           N  
+ATOM   1103  CA  CYS A 140     -15.024   3.979   4.275  1.00 96.81           C  
+ATOM   1104  C   CYS A 140     -14.339   3.899   5.645  1.00 96.81           C  
+ATOM   1105  CB  CYS A 140     -14.127   3.449   3.144  1.00 96.81           C  
+ATOM   1106  O   CYS A 140     -13.959   4.918   6.227  1.00 96.81           O  
+ATOM   1107  SG  CYS A 140     -12.503   4.262   3.135  1.00 96.81           S  
+ATOM   1108  N   VAL A 141     -14.113   2.682   6.135  1.00 98.38           N  
+ATOM   1109  CA  VAL A 141     -13.111   2.421   7.178  1.00 98.38           C  
+ATOM   1110  C   VAL A 141     -11.791   2.060   6.501  1.00 98.38           C  
+ATOM   1111  CB  VAL A 141     -13.574   1.310   8.139  1.00 98.38           C  
+ATOM   1112  O   VAL A 141     -11.776   1.238   5.593  1.00 98.38           O  
+ATOM   1113  CG1 VAL A 141     -12.532   1.031   9.230  1.00 98.38           C  
+ATOM   1114  CG2 VAL A 141     -14.881   1.706   8.838  1.00 98.38           C  
+ATOM   1115  N   VAL A 142     -10.672   2.627   6.950  1.00 98.81           N  
+ATOM   1116  CA  VAL A 142      -9.331   2.230   6.490  1.00 98.81           C  
+ATOM   1117  C   VAL A 142      -8.600   1.520   7.623  1.00 98.81           C  
+ATOM   1118  CB  VAL A 142      -8.511   3.416   5.948  1.00 98.81           C  
+ATOM   1119  O   VAL A 142      -8.462   2.071   8.713  1.00 98.81           O  
+ATOM   1120  CG1 VAL A 142      -7.125   2.972   5.466  1.00 98.81           C  
+ATOM   1121  CG2 VAL A 142      -9.198   4.131   4.780  1.00 98.81           C  
+ATOM   1122  N   VAL A 143      -8.086   0.320   7.371  1.00 98.88           N  
+ATOM   1123  CA  VAL A 143      -7.203  -0.419   8.281  1.00 98.88           C  
+ATOM   1124  C   VAL A 143      -5.791  -0.382   7.705  1.00 98.88           C  
+ATOM   1125  CB  VAL A 143      -7.708  -1.858   8.509  1.00 98.88           C  
+ATOM   1126  O   VAL A 143      -5.490  -1.077   6.744  1.00 98.88           O  
+ATOM   1127  CG1 VAL A 143      -6.810  -2.602   9.508  1.00 98.88           C  
+ATOM   1128  CG2 VAL A 143      -9.141  -1.854   9.062  1.00 98.88           C  
+ATOM   1129  N   SER A 144      -4.925   0.451   8.275  1.00 98.88           N  
+ATOM   1130  CA  SER A 144      -3.525   0.602   7.869  1.00 98.88           C  
+ATOM   1131  C   SER A 144      -2.650  -0.413   8.602  1.00 98.88           C  
+ATOM   1132  CB  SER A 144      -3.075   2.037   8.139  1.00 98.88           C  
+ATOM   1133  O   SER A 144      -2.513  -0.357   9.831  1.00 98.88           O  
+ATOM   1134  OG  SER A 144      -1.771   2.270   7.648  1.00 98.88           O  
+ATOM   1135  N   VAL A 145      -2.084  -1.364   7.858  1.00 98.88           N  
+ATOM   1136  CA  VAL A 145      -1.358  -2.513   8.416  1.00 98.88           C  
+ATOM   1137  C   VAL A 145       0.081  -2.136   8.747  1.00 98.88           C  
+ATOM   1138  CB  VAL A 145      -1.403  -3.736   7.479  1.00 98.88           C  
+ATOM   1139  O   VAL A 145       0.828  -1.689   7.877  1.00 98.88           O  
+ATOM   1140  CG1 VAL A 145      -0.805  -4.978   8.156  1.00 98.88           C  
+ATOM   1141  CG2 VAL A 145      -2.843  -4.076   7.093  1.00 98.88           C  
+ATOM   1142  N   ASN A 146       0.505  -2.360   9.994  1.00 98.62           N  
+ATOM   1143  CA  ASN A 146       1.890  -2.135  10.414  1.00 98.62           C  
+ATOM   1144  C   ASN A 146       2.738  -3.398  10.211  1.00 98.62           C  
+ATOM   1145  CB  ASN A 146       1.918  -1.613  11.857  1.00 98.62           C  
+ATOM   1146  O   ASN A 146       3.230  -3.991  11.172  1.00 98.62           O  
+ATOM   1147  CG  ASN A 146       3.236  -0.945  12.170  1.00 98.62           C  
+ATOM   1148  ND2 ASN A 146       3.798  -1.199  13.327  1.00 98.62           N  
+ATOM   1149  OD1 ASN A 146       3.754  -0.153  11.401  1.00 98.62           O  
+ATOM   1150  N   TYR A 147       2.839  -3.838   8.958  1.00 98.81           N  
+ATOM   1151  CA  TYR A 147       3.493  -5.091   8.593  1.00 98.81           C  
+ATOM   1152  C   TYR A 147       4.994  -5.090   8.952  1.00 98.81           C  
+ATOM   1153  CB  TYR A 147       3.225  -5.379   7.105  1.00 98.81           C  
+ATOM   1154  O   TYR A 147       5.657  -4.048   9.007  1.00 98.81           O  
+ATOM   1155  CG  TYR A 147       3.797  -4.351   6.144  1.00 98.81           C  
+ATOM   1156  CD1 TYR A 147       2.999  -3.318   5.607  1.00 98.81           C  
+ATOM   1157  CD2 TYR A 147       5.156  -4.432   5.799  1.00 98.81           C  
+ATOM   1158  CE1 TYR A 147       3.576  -2.369   4.737  1.00 98.81           C  
+ATOM   1159  CE2 TYR A 147       5.742  -3.473   4.961  1.00 98.81           C  
+ATOM   1160  OH  TYR A 147       5.553  -1.524   3.628  1.00 98.81           O  
+ATOM   1161  CZ  TYR A 147       4.952  -2.437   4.431  1.00 98.81           C  
+ATOM   1162  N   ARG A 148       5.555  -6.277   9.197  1.00 98.69           N  
+ATOM   1163  CA  ARG A 148       6.981  -6.479   9.471  1.00 98.69           C  
+ATOM   1164  C   ARG A 148       7.822  -6.151   8.237  1.00 98.69           C  
+ATOM   1165  CB  ARG A 148       7.216  -7.921   9.929  1.00 98.69           C  
+ATOM   1166  O   ARG A 148       7.507  -6.560   7.123  1.00 98.69           O  
+ATOM   1167  CG  ARG A 148       6.689  -8.204  11.345  1.00 98.69           C  
+ATOM   1168  CD  ARG A 148       6.837  -9.684  11.715  1.00 98.69           C  
+ATOM   1169  NE  ARG A 148       5.976 -10.547  10.888  1.00 98.69           N  
+ATOM   1170  NH1 ARG A 148       7.220 -12.476  10.986  1.00 98.69           N  
+ATOM   1171  NH2 ARG A 148       5.286 -12.436   9.863  1.00 98.69           N  
+ATOM   1172  CZ  ARG A 148       6.165 -11.817  10.591  1.00 98.69           C  
+ATOM   1173  N   ARG A 149       8.906  -5.404   8.451  1.00 98.31           N  
+ATOM   1174  CA  ARG A 149       9.728  -4.800   7.392  1.00 98.31           C  
+ATOM   1175  C   ARG A 149      11.067  -5.509   7.216  1.00 98.31           C  
+ATOM   1176  CB  ARG A 149       9.896  -3.295   7.667  1.00 98.31           C  
+ATOM   1177  O   ARG A 149      11.624  -6.056   8.169  1.00 98.31           O  
+ATOM   1178  CG  ARG A 149       8.588  -2.529   7.388  1.00 98.31           C  
+ATOM   1179  CD  ARG A 149       8.623  -1.085   7.892  1.00 98.31           C  
+ATOM   1180  NE  ARG A 149       8.679  -1.046   9.364  1.00 98.31           N  
+ATOM   1181  NH1 ARG A 149       9.541   1.072   9.678  1.00 98.31           N  
+ATOM   1182  NH2 ARG A 149       9.349  -0.288  11.388  1.00 98.31           N  
+ATOM   1183  CZ  ARG A 149       9.182  -0.093  10.119  1.00 98.31           C  
+ATOM   1184  N   ALA A 150      11.574  -5.444   5.993  1.00 98.44           N  
+ATOM   1185  CA  ALA A 150      12.913  -5.868   5.614  1.00 98.44           C  
+ATOM   1186  C   ALA A 150      13.972  -4.830   6.045  1.00 98.44           C  
+ATOM   1187  CB  ALA A 150      12.912  -6.083   4.097  1.00 98.44           C  
+ATOM   1188  O   ALA A 150      13.656  -3.638   6.141  1.00 98.44           O  
+ATOM   1189  N   PRO A 151      15.239  -5.236   6.244  1.00 97.56           N  
+ATOM   1190  CA  PRO A 151      15.776  -6.596   6.099  1.00 97.56           C  
+ATOM   1191  C   PRO A 151      15.535  -7.512   7.310  1.00 97.56           C  
+ATOM   1192  CB  PRO A 151      17.276  -6.387   5.876  1.00 97.56           C  
+ATOM   1193  O   PRO A 151      15.844  -8.693   7.239  1.00 97.56           O  
+ATOM   1194  CG  PRO A 151      17.574  -5.141   6.708  1.00 97.56           C  
+ATOM   1195  CD  PRO A 151      16.312  -4.299   6.534  1.00 97.56           C  
+ATOM   1196  N   GLU A 152      15.001  -7.006   8.425  1.00 97.19           N  
+ATOM   1197  CA  GLU A 152      14.849  -7.796   9.658  1.00 97.19           C  
+ATOM   1198  C   GLU A 152      13.817  -8.920   9.512  1.00 97.19           C  
+ATOM   1199  CB  GLU A 152      14.433  -6.891  10.829  1.00 97.19           C  
+ATOM   1200  O   GLU A 152      13.915  -9.955  10.165  1.00 97.19           O  
+ATOM   1201  CG  GLU A 152      15.389  -5.716  11.092  1.00 97.19           C  
+ATOM   1202  CD  GLU A 152      15.086  -4.446  10.273  1.00 97.19           C  
+ATOM   1203  OE1 GLU A 152      15.608  -3.376  10.632  1.00 97.19           O  
+ATOM   1204  OE2 GLU A 152      14.277  -4.447   9.319  1.00 97.19           O  
+ATOM   1205  N   ASN A 153      12.817  -8.695   8.663  1.00 98.00           N  
+ATOM   1206  CA  ASN A 153      11.764  -9.644   8.339  1.00 98.00           C  
+ATOM   1207  C   ASN A 153      11.593  -9.666   6.811  1.00 98.00           C  
+ATOM   1208  CB  ASN A 153      10.480  -9.228   9.067  1.00 98.00           C  
+ATOM   1209  O   ASN A 153      10.716  -8.970   6.287  1.00 98.00           O  
+ATOM   1210  CG  ASN A 153      10.624  -9.164  10.572  1.00 98.00           C  
+ATOM   1211  ND2 ASN A 153      11.017  -8.024  11.093  1.00 98.00           N  
+ATOM   1212  OD1 ASN A 153      10.319 -10.096  11.297  1.00 98.00           O  
+ATOM   1213  N   PRO A 154      12.459 -10.394   6.082  1.00 98.19           N  
+ATOM   1214  CA  PRO A 154      12.363 -10.499   4.632  1.00 98.19           C  
+ATOM   1215  C   PRO A 154      11.118 -11.309   4.231  1.00 98.19           C  
+ATOM   1216  CB  PRO A 154      13.688 -11.148   4.204  1.00 98.19           C  
+ATOM   1217  O   PRO A 154      10.278 -11.688   5.059  1.00 98.19           O  
+ATOM   1218  CG  PRO A 154      14.019 -12.062   5.380  1.00 98.19           C  
+ATOM   1219  CD  PRO A 154      13.521 -11.263   6.584  1.00 98.19           C  
+ATOM   1220  N   TYR A 155      10.985 -11.571   2.937  1.00 98.69           N  
+ATOM   1221  CA  TYR A 155       9.971 -12.450   2.375  1.00 98.69           C  
+ATOM   1222  C   TYR A 155       9.854 -13.763   3.180  1.00 98.69           C  
+ATOM   1223  CB  TYR A 155      10.340 -12.750   0.916  1.00 98.69           C  
+ATOM   1224  O   TYR A 155      10.882 -14.359   3.513  1.00 98.69           O  
+ATOM   1225  CG  TYR A 155       9.438 -13.769   0.247  1.00 98.69           C  
+ATOM   1226  CD1 TYR A 155       9.668 -15.145   0.444  1.00 98.69           C  
+ATOM   1227  CD2 TYR A 155       8.349 -13.349  -0.539  1.00 98.69           C  
+ATOM   1228  CE1 TYR A 155       8.842 -16.105  -0.163  1.00 98.69           C  
+ATOM   1229  CE2 TYR A 155       7.504 -14.312  -1.128  1.00 98.69           C  
+ATOM   1230  OH  TYR A 155       6.942 -16.590  -1.548  1.00 98.69           O  
+ATOM   1231  CZ  TYR A 155       7.754 -15.684  -0.950  1.00 98.69           C  
+ATOM   1232  N   PRO A 156       8.629 -14.253   3.465  1.00 98.12           N  
+ATOM   1233  CA  PRO A 156       7.318 -13.714   3.074  1.00 98.12           C  
+ATOM   1234  C   PRO A 156       6.631 -12.858   4.162  1.00 98.12           C  
+ATOM   1235  CB  PRO A 156       6.505 -14.961   2.715  1.00 98.12           C  
+ATOM   1236  O   PRO A 156       5.421 -12.654   4.094  1.00 98.12           O  
+ATOM   1237  CG  PRO A 156       6.989 -15.975   3.747  1.00 98.12           C  
+ATOM   1238  CD  PRO A 156       8.462 -15.621   3.943  1.00 98.12           C  
+ATOM   1239  N   CYS A 157       7.353 -12.341   5.167  1.00 98.81           N  
+ATOM   1240  CA  CYS A 157       6.742 -11.808   6.397  1.00 98.81           C  
+ATOM   1241  C   CYS A 157       5.675 -10.725   6.165  1.00 98.81           C  
+ATOM   1242  CB  CYS A 157       7.833 -11.252   7.320  1.00 98.81           C  
+ATOM   1243  O   CYS A 157       4.604 -10.791   6.761  1.00 98.81           O  
+ATOM   1244  SG  CYS A 157       8.891 -12.587   7.943  1.00 98.81           S  
+ATOM   1245  N   ALA A 158       5.931  -9.756   5.282  1.00 98.81           N  
+ATOM   1246  CA  ALA A 158       4.963  -8.700   4.977  1.00 98.81           C  
+ATOM   1247  C   ALA A 158       3.678  -9.241   4.315  1.00 98.81           C  
+ATOM   1248  CB  ALA A 158       5.652  -7.658   4.090  1.00 98.81           C  
+ATOM   1249  O   ALA A 158       2.590  -8.713   4.547  1.00 98.81           O  
+ATOM   1250  N   TYR A 159       3.791 -10.309   3.518  1.00 98.81           N  
+ATOM   1251  CA  TYR A 159       2.648 -10.967   2.879  1.00 98.81           C  
+ATOM   1252  C   TYR A 159       1.837 -11.776   3.890  1.00 98.81           C  
+ATOM   1253  CB  TYR A 159       3.129 -11.871   1.734  1.00 98.81           C  
+ATOM   1254  O   TYR A 159       0.608 -11.724   3.871  1.00 98.81           O  
+ATOM   1255  CG  TYR A 159       3.972 -11.217   0.652  1.00 98.81           C  
+ATOM   1256  CD1 TYR A 159       3.888  -9.832   0.386  1.00 98.81           C  
+ATOM   1257  CD2 TYR A 159       4.799 -12.030  -0.147  1.00 98.81           C  
+ATOM   1258  CE1 TYR A 159       4.625  -9.262  -0.669  1.00 98.81           C  
+ATOM   1259  CE2 TYR A 159       5.522 -11.458  -1.209  1.00 98.81           C  
+ATOM   1260  OH  TYR A 159       6.119  -9.585  -2.527  1.00 98.81           O  
+ATOM   1261  CZ  TYR A 159       5.438 -10.084  -1.471  1.00 98.81           C  
+ATOM   1262  N   ASP A 160       2.509 -12.475   4.806  1.00 98.88           N  
+ATOM   1263  CA  ASP A 160       1.843 -13.187   5.898  1.00 98.88           C  
+ATOM   1264  C   ASP A 160       1.117 -12.230   6.844  1.00 98.88           C  
+ATOM   1265  CB  ASP A 160       2.858 -14.028   6.678  1.00 98.88           C  
+ATOM   1266  O   ASP A 160      -0.019 -12.494   7.235  1.00 98.88           O  
+ATOM   1267  CG  ASP A 160       3.270 -15.298   5.936  1.00 98.88           C  
+ATOM   1268  OD1 ASP A 160       2.421 -15.851   5.186  1.00 98.88           O  
+ATOM   1269  OD2 ASP A 160       4.406 -15.746   6.203  1.00 98.88           O  
+ATOM   1270  N   ASP A 161       1.723 -11.088   7.160  1.00 98.94           N  
+ATOM   1271  CA  ASP A 161       1.098 -10.067   8.000  1.00 98.94           C  
+ATOM   1272  C   ASP A 161      -0.133  -9.465   7.317  1.00 98.94           C  
+ATOM   1273  CB  ASP A 161       2.116  -8.972   8.324  1.00 98.94           C  
+ATOM   1274  O   ASP A 161      -1.177  -9.301   7.950  1.00 98.94           O  
+ATOM   1275  CG  ASP A 161       3.246  -9.437   9.242  1.00 98.94           C  
+ATOM   1276  OD1 ASP A 161       3.295 -10.609   9.676  1.00 98.94           O  
+ATOM   1277  OD2 ASP A 161       4.113  -8.597   9.553  1.00 98.94           O  
+ATOM   1278  N   GLY A 162      -0.052  -9.195   6.010  1.00 98.94           N  
+ATOM   1279  CA  GLY A 162      -1.203  -8.744   5.235  1.00 98.94           C  
+ATOM   1280  C   GLY A 162      -2.315  -9.786   5.147  1.00 98.94           C  
+ATOM   1281  O   GLY A 162      -3.491  -9.441   5.266  1.00 98.94           O  
+ATOM   1282  N   TRP A 163      -1.963 -11.067   5.037  1.00 98.81           N  
+ATOM   1283  CA  TRP A 163      -2.927 -12.162   5.081  1.00 98.81           C  
+ATOM   1284  C   TRP A 163      -3.626 -12.277   6.441  1.00 98.81           C  
+ATOM   1285  CB  TRP A 163      -2.215 -13.470   4.737  1.00 98.81           C  
+ATOM   1286  O   TRP A 163      -4.850 -12.418   6.508  1.00 98.81           O  
+ATOM   1287  CG  TRP A 163      -3.119 -14.650   4.824  1.00 98.81           C  
+ATOM   1288  CD1 TRP A 163      -3.068 -15.635   5.748  1.00 98.81           C  
+ATOM   1289  CD2 TRP A 163      -4.306 -14.901   4.027  1.00 98.81           C  
+ATOM   1290  CE2 TRP A 163      -4.899 -16.114   4.487  1.00 98.81           C  
+ATOM   1291  CE3 TRP A 163      -4.951 -14.198   2.989  1.00 98.81           C  
+ATOM   1292  NE1 TRP A 163      -4.100 -16.526   5.529  1.00 98.81           N  
+ATOM   1293  CH2 TRP A 163      -6.748 -15.831   2.988  1.00 98.81           C  
+ATOM   1294  CZ2 TRP A 163      -6.107 -16.589   3.975  1.00 98.81           C  
+ATOM   1295  CZ3 TRP A 163      -6.170 -14.662   2.475  1.00 98.81           C  
+ATOM   1296  N   ILE A 164      -2.866 -12.189   7.535  1.00 98.88           N  
+ATOM   1297  CA  ILE A 164      -3.407 -12.180   8.899  1.00 98.88           C  
+ATOM   1298  C   ILE A 164      -4.351 -10.984   9.080  1.00 98.88           C  
+ATOM   1299  CB  ILE A 164      -2.246 -12.182   9.922  1.00 98.88           C  
+ATOM   1300  O   ILE A 164      -5.455 -11.150   9.603  1.00 98.88           O  
+ATOM   1301  CG1 ILE A 164      -1.542 -13.558   9.933  1.00 98.88           C  
+ATOM   1302  CG2 ILE A 164      -2.740 -11.856  11.340  1.00 98.88           C  
+ATOM   1303  CD1 ILE A 164      -0.192 -13.555  10.662  1.00 98.88           C  
+ATOM   1304  N   ALA A 165      -3.959  -9.800   8.603  1.00 98.94           N  
+ATOM   1305  CA  ALA A 165      -4.780  -8.598   8.683  1.00 98.94           C  
+ATOM   1306  C   ALA A 165      -6.079  -8.713   7.866  1.00 98.94           C  
+ATOM   1307  CB  ALA A 165      -3.930  -7.395   8.270  1.00 98.94           C  
+ATOM   1308  O   ALA A 165      -7.139  -8.339   8.368  1.00 98.94           O  
+ATOM   1309  N   LEU A 166      -6.034  -9.304   6.666  1.00 98.88           N  
+ATOM   1310  CA  LEU A 166      -7.227  -9.563   5.854  1.00 98.88           C  
+ATOM   1311  C   LEU A 166      -8.216 -10.495   6.569  1.00 98.88           C  
+ATOM   1312  CB  LEU A 166      -6.797 -10.130   4.487  1.00 98.88           C  
+ATOM   1313  O   LEU A 166      -9.405 -10.189   6.658  1.00 98.88           O  
+ATOM   1314  CG  LEU A 166      -7.986 -10.507   3.584  1.00 98.88           C  
+ATOM   1315  CD1 LEU A 166      -8.852  -9.298   3.246  1.00 98.88           C  
+ATOM   1316  CD2 LEU A 166      -7.501 -11.114   2.276  1.00 98.88           C  
+ATOM   1317  N   ASN A 167      -7.730 -11.604   7.133  1.00 98.75           N  
+ATOM   1318  CA  ASN A 167      -8.572 -12.523   7.905  1.00 98.75           C  
+ATOM   1319  C   ASN A 167      -9.170 -11.838   9.136  1.00 98.75           C  
+ATOM   1320  CB  ASN A 167      -7.754 -13.749   8.332  1.00 98.75           C  
+ATOM   1321  O   ASN A 167     -10.346 -12.031   9.455  1.00 98.75           O  
+ATOM   1322  CG  ASN A 167      -7.639 -14.738   7.201  1.00 98.75           C  
+ATOM   1323  ND2 ASN A 167      -6.642 -14.605   6.370  1.00 98.75           N  
+ATOM   1324  OD1 ASN A 167      -8.461 -15.620   7.042  1.00 98.75           O  
+ATOM   1325  N   TRP A 168      -8.376 -11.012   9.820  1.00 98.62           N  
+ATOM   1326  CA  TRP A 168      -8.858 -10.241  10.954  1.00 98.62           C  
+ATOM   1327  C   TRP A 168      -9.986  -9.298  10.539  1.00 98.62           C  
+ATOM   1328  CB  TRP A 168      -7.706  -9.504  11.622  1.00 98.62           C  
+ATOM   1329  O   TRP A 168     -11.049  -9.374  11.155  1.00 98.62           O  
+ATOM   1330  CG  TRP A 168      -8.106  -8.702  12.822  1.00 98.62           C  
+ATOM   1331  CD1 TRP A 168      -8.044  -9.115  14.108  1.00 98.62           C  
+ATOM   1332  CD2 TRP A 168      -8.592  -7.325  12.867  1.00 98.62           C  
+ATOM   1333  CE2 TRP A 168      -8.826  -6.975  14.229  1.00 98.62           C  
+ATOM   1334  CE3 TRP A 168      -8.821  -6.326  11.897  1.00 98.62           C  
+ATOM   1335  NE1 TRP A 168      -8.470  -8.100  14.942  1.00 98.62           N  
+ATOM   1336  CH2 TRP A 168      -9.522  -4.743  13.615  1.00 98.62           C  
+ATOM   1337  CZ2 TRP A 168      -9.284  -5.705  14.611  1.00 98.62           C  
+ATOM   1338  CZ3 TRP A 168      -9.289  -5.051  12.265  1.00 98.62           C  
+ATOM   1339  N   VAL A 169      -9.805  -8.513   9.470  1.00 98.69           N  
+ATOM   1340  CA  VAL A 169     -10.829  -7.606   8.924  1.00 98.69           C  
+ATOM   1341  C   VAL A 169     -12.110  -8.366   8.589  1.00 98.69           C  
+ATOM   1342  CB  VAL A 169     -10.291  -6.831   7.699  1.00 98.69           C  
+ATOM   1343  O   VAL A 169     -13.170  -8.018   9.105  1.00 98.69           O  
+ATOM   1344  CG1 VAL A 169     -11.400  -6.209   6.843  1.00 98.69           C  
+ATOM   1345  CG2 VAL A 169      -9.375  -5.696   8.167  1.00 98.69           C  
+ATOM   1346  N   ASN A 170     -12.009  -9.456   7.824  1.00 97.88           N  
+ATOM   1347  CA  ASN A 170     -13.162 -10.266   7.421  1.00 97.88           C  
+ATOM   1348  C   ASN A 170     -13.921 -10.878   8.622  1.00 97.88           C  
+ATOM   1349  CB  ASN A 170     -12.648 -11.346   6.455  1.00 97.88           C  
+ATOM   1350  O   ASN A 170     -15.120 -11.142   8.557  1.00 97.88           O  
+ATOM   1351  CG  ASN A 170     -13.788 -12.073   5.765  1.00 97.88           C  
+ATOM   1352  ND2 ASN A 170     -13.717 -13.377   5.647  1.00 97.88           N  
+ATOM   1353  OD1 ASN A 170     -14.757 -11.486   5.326  1.00 97.88           O  
+ATOM   1354  N   SER A 171     -13.232 -11.086   9.751  1.00 96.75           N  
+ATOM   1355  CA  SER A 171     -13.822 -11.650  10.973  1.00 96.75           C  
+ATOM   1356  C   SER A 171     -14.486 -10.626  11.907  1.00 96.75           C  
+ATOM   1357  CB  SER A 171     -12.773 -12.461  11.741  1.00 96.75           C  
+ATOM   1358  O   SER A 171     -15.203 -11.030  12.825  1.00 96.75           O  
+ATOM   1359  OG  SER A 171     -11.840 -11.614  12.395  1.00 96.75           O  
+ATOM   1360  N   ARG A 172     -14.250  -9.315  11.740  1.00 96.50           N  
+ATOM   1361  CA  ARG A 172     -14.784  -8.304  12.669  1.00 96.50           C  
+ATOM   1362  C   ARG A 172     -16.248  -8.006  12.361  1.00 96.50           C  
+ATOM   1363  CB  ARG A 172     -13.972  -7.001  12.628  1.00 96.50           C  
+ATOM   1364  O   ARG A 172     -16.576  -7.455  11.317  1.00 96.50           O  
+ATOM   1365  CG  ARG A 172     -12.519  -7.044  13.123  1.00 96.50           C  
+ATOM   1366  CD  ARG A 172     -12.323  -7.703  14.497  1.00 96.50           C  
+ATOM   1367  NE  ARG A 172     -11.947  -9.126  14.398  1.00 96.50           N  
+ATOM   1368  NH1 ARG A 172     -11.945  -9.583  16.648  1.00 96.50           N  
+ATOM   1369  NH2 ARG A 172     -11.354 -11.171  15.191  1.00 96.50           N  
+ATOM   1370  CZ  ARG A 172     -11.748  -9.950  15.410  1.00 96.50           C  
+ATOM   1371  N   SER A 173     -17.136  -8.299  13.310  1.00 95.06           N  
+ATOM   1372  CA  SER A 173     -18.574  -8.033  13.168  1.00 95.06           C  
+ATOM   1373  C   SER A 173     -18.896  -6.557  12.940  1.00 95.06           C  
+ATOM   1374  CB  SER A 173     -19.336  -8.524  14.402  1.00 95.06           C  
+ATOM   1375  O   SER A 173     -19.803  -6.249  12.179  1.00 95.06           O  
+ATOM   1376  OG  SER A 173     -18.741  -8.007  15.578  1.00 95.06           O  
+ATOM   1377  N   TRP A 174     -18.141  -5.648  13.554  1.00 95.25           N  
+ATOM   1378  CA  TRP A 174     -18.357  -4.208  13.427  1.00 95.25           C  
+ATOM   1379  C   TRP A 174     -17.866  -3.607  12.105  1.00 95.25           C  
+ATOM   1380  CB  TRP A 174     -17.723  -3.523  14.628  1.00 95.25           C  
+ATOM   1381  O   TRP A 174     -18.206  -2.471  11.795  1.00 95.25           O  
+ATOM   1382  CG  TRP A 174     -16.231  -3.615  14.709  1.00 95.25           C  
+ATOM   1383  CD1 TRP A 174     -15.524  -4.474  15.479  1.00 95.25           C  
+ATOM   1384  CD2 TRP A 174     -15.250  -2.741  14.079  1.00 95.25           C  
+ATOM   1385  CE2 TRP A 174     -13.950  -3.190  14.452  1.00 95.25           C  
+ATOM   1386  CE3 TRP A 174     -15.328  -1.585  13.272  1.00 95.25           C  
+ATOM   1387  NE1 TRP A 174     -14.173  -4.219  15.339  1.00 95.25           N  
+ATOM   1388  CH2 TRP A 174     -12.894  -1.459  13.145  1.00 95.25           C  
+ATOM   1389  CZ2 TRP A 174     -12.784  -2.570  13.993  1.00 95.25           C  
+ATOM   1390  CZ3 TRP A 174     -14.161  -0.943  12.819  1.00 95.25           C  
+ATOM   1391  N   LEU A 175     -17.107  -4.368  11.311  1.00 96.94           N  
+ATOM   1392  CA  LEU A 175     -16.729  -4.004   9.943  1.00 96.94           C  
+ATOM   1393  C   LEU A 175     -17.744  -4.502   8.903  1.00 96.94           C  
+ATOM   1394  CB  LEU A 175     -15.312  -4.539   9.659  1.00 96.94           C  
+ATOM   1395  O   LEU A 175     -17.594  -4.224   7.716  1.00 96.94           O  
+ATOM   1396  CG  LEU A 175     -14.194  -3.797  10.410  1.00 96.94           C  
+ATOM   1397  CD1 LEU A 175     -12.824  -4.407  10.107  1.00 96.94           C  
+ATOM   1398  CD2 LEU A 175     -14.110  -2.328   9.996  1.00 96.94           C  
+ATOM   1399  N   LYS A 176     -18.771  -5.249   9.323  1.00 96.06           N  
+ATOM   1400  CA  LYS A 176     -19.811  -5.740   8.421  1.00 96.06           C  
+ATOM   1401  C   LYS A 176     -20.806  -4.636   8.083  1.00 96.06           C  
+ATOM   1402  CB  LYS A 176     -20.529  -6.958   9.005  1.00 96.06           C  
+ATOM   1403  O   LYS A 176     -21.280  -3.918   8.961  1.00 96.06           O  
+ATOM   1404  CG  LYS A 176     -19.584  -8.157   9.128  1.00 96.06           C  
+ATOM   1405  CD  LYS A 176     -20.298  -9.332   9.794  1.00 96.06           C  
+ATOM   1406  CE  LYS A 176     -19.427 -10.580   9.649  1.00 96.06           C  
+ATOM   1407  NZ  LYS A 176     -20.234 -11.803   9.851  1.00 96.06           N  
+ATOM   1408  N   SER A 177     -21.163  -4.571   6.810  1.00 94.44           N  
+ATOM   1409  CA  SER A 177     -22.222  -3.723   6.282  1.00 94.44           C  
+ATOM   1410  C   SER A 177     -23.596  -4.217   6.731  1.00 94.44           C  
+ATOM   1411  CB  SER A 177     -22.111  -3.709   4.762  1.00 94.44           C  
+ATOM   1412  O   SER A 177     -23.847  -5.424   6.792  1.00 94.44           O  
+ATOM   1413  OG  SER A 177     -20.975  -2.936   4.425  1.00 94.44           O  
+ATOM   1414  N   LYS A 178     -24.502  -3.286   7.050  1.00 92.81           N  
+ATOM   1415  CA  LYS A 178     -25.847  -3.601   7.562  1.00 92.81           C  
+ATOM   1416  C   LYS A 178     -26.702  -4.319   6.512  1.00 92.81           C  
+ATOM   1417  CB  LYS A 178     -26.542  -2.312   8.047  1.00 92.81           C  
+ATOM   1418  O   LYS A 178     -27.458  -5.220   6.868  1.00 92.81           O  
+ATOM   1419  CG  LYS A 178     -25.899  -1.706   9.311  1.00 92.81           C  
+ATOM   1420  CD  LYS A 178     -26.580  -0.386   9.728  1.00 92.81           C  
+ATOM   1421  CE  LYS A 178     -25.921   0.220  10.985  1.00 92.81           C  
+ATOM   1422  NZ  LYS A 178     -26.415   1.595  11.294  1.00 92.81           N  
+ATOM   1423  N   LYS A 179     -26.547  -3.955   5.234  1.00 93.25           N  
+ATOM   1424  CA  LYS A 179     -27.355  -4.450   4.106  1.00 93.25           C  
+ATOM   1425  C   LYS A 179     -27.322  -5.970   3.949  1.00 93.25           C  
+ATOM   1426  CB  LYS A 179     -26.849  -3.760   2.832  1.00 93.25           C  
+ATOM   1427  O   LYS A 179     -28.351  -6.572   3.660  1.00 93.25           O  
+ATOM   1428  CG  LYS A 179     -27.692  -4.040   1.576  1.00 93.25           C  
+ATOM   1429  CD  LYS A 179     -27.004  -3.353   0.395  1.00 93.25           C  
+ATOM   1430  CE  LYS A 179     -27.702  -3.503  -0.958  1.00 93.25           C  
+ATOM   1431  NZ  LYS A 179     -26.926  -2.786  -2.010  1.00 93.25           N  
+ATOM   1432  N   ASP A 180     -26.155  -6.588   4.116  1.00 93.31           N  
+ATOM   1433  CA  ASP A 180     -25.939  -8.007   3.804  1.00 93.31           C  
+ATOM   1434  C   ASP A 180     -25.078  -8.759   4.831  1.00 93.31           C  
+ATOM   1435  CB  ASP A 180     -25.356  -8.119   2.389  1.00 93.31           C  
+ATOM   1436  O   ASP A 180     -24.799  -9.946   4.654  1.00 93.31           O  
+ATOM   1437  CG  ASP A 180     -23.997  -7.433   2.221  1.00 93.31           C  
+ATOM   1438  OD1 ASP A 180     -23.391  -6.993   3.227  1.00 93.31           O  
+ATOM   1439  OD2 ASP A 180     -23.555  -7.346   1.060  1.00 93.31           O  
+ATOM   1440  N   SER A 181     -24.680  -8.105   5.928  1.00 92.25           N  
+ATOM   1441  CA  SER A 181     -23.829  -8.688   6.974  1.00 92.25           C  
+ATOM   1442  C   SER A 181     -22.481  -9.223   6.460  1.00 92.25           C  
+ATOM   1443  CB  SER A 181     -24.607  -9.730   7.786  1.00 92.25           C  
+ATOM   1444  O   SER A 181     -21.899 -10.134   7.069  1.00 92.25           O  
+ATOM   1445  OG  SER A 181     -25.729  -9.134   8.403  1.00 92.25           O  
+ATOM   1446  N   LYS A 182     -21.961  -8.647   5.368  1.00 94.38           N  
+ATOM   1447  CA  LYS A 182     -20.628  -8.924   4.815  1.00 94.38           C  
+ATOM   1448  C   LYS A 182     -19.681  -7.752   5.036  1.00 94.38           C  
+ATOM   1449  CB  LYS A 182     -20.730  -9.216   3.316  1.00 94.38           C  
+ATOM   1450  O   LYS A 182     -20.110  -6.618   5.230  1.00 94.38           O  
+ATOM   1451  CG  LYS A 182     -21.575 -10.454   2.993  1.00 94.38           C  
+ATOM   1452  CD  LYS A 182     -21.569 -10.633   1.477  1.00 94.38           C  
+ATOM   1453  CE  LYS A 182     -22.385 -11.841   1.028  1.00 94.38           C  
+ATOM   1454  NZ  LYS A 182     -22.287 -11.954  -0.448  1.00 94.38           N  
+ATOM   1455  N   VAL A 183     -18.380  -8.022   5.005  1.00 96.88           N  
+ATOM   1456  CA  VAL A 183     -17.362  -6.967   4.961  1.00 96.88           C  
+ATOM   1457  C   VAL A 183     -17.060  -6.684   3.491  1.00 96.88           C  
+ATOM   1458  CB  VAL A 183     -16.095  -7.359   5.742  1.00 96.88           C  
+ATOM   1459  O   VAL A 183     -16.666  -7.590   2.766  1.00 96.88           O  
+ATOM   1460  CG1 VAL A 183     -15.174  -6.145   5.877  1.00 96.88           C  
+ATOM   1461  CG2 VAL A 183     -16.421  -7.844   7.163  1.00 96.88           C  
+ATOM   1462  N   HIS A 184     -17.265  -5.444   3.051  1.00 97.50           N  
+ATOM   1463  CA  HIS A 184     -17.029  -5.011   1.669  1.00 97.50           C  
+ATOM   1464  C   HIS A 184     -15.562  -4.590   1.531  1.00 97.50           C  
+ATOM   1465  CB  HIS A 184     -18.038  -3.901   1.323  1.00 97.50           C  
+ATOM   1466  O   HIS A 184     -15.218  -3.426   1.736  1.00 97.50           O  
+ATOM   1467  CG  HIS A 184     -19.441  -4.418   1.101  1.00 97.50           C  
+ATOM   1468  CD2 HIS A 184     -20.266  -4.986   2.031  1.00 97.50           C  
+ATOM   1469  ND1 HIS A 184     -20.082  -4.452  -0.131  1.00 97.50           N  
+ATOM   1470  CE1 HIS A 184     -21.273  -5.027   0.075  1.00 97.50           C  
+ATOM   1471  NE2 HIS A 184     -21.414  -5.363   1.367  1.00 97.50           N  
+ATOM   1472  N   ILE A 185     -14.676  -5.568   1.319  1.00 98.69           N  
+ATOM   1473  CA  ILE A 185     -13.223  -5.388   1.456  1.00 98.69           C  
+ATOM   1474  C   ILE A 185     -12.600  -4.888   0.153  1.00 98.69           C  
+ATOM   1475  CB  ILE A 185     -12.519  -6.680   1.938  1.00 98.69           C  
+ATOM   1476  O   ILE A 185     -12.758  -5.499  -0.897  1.00 98.69           O  
+ATOM   1477  CG1 ILE A 185     -13.102  -7.159   3.285  1.00 98.69           C  
+ATOM   1478  CG2 ILE A 185     -11.002  -6.435   2.087  1.00 98.69           C  
+ATOM   1479  CD1 ILE A 185     -12.651  -8.555   3.728  1.00 98.69           C  
+ATOM   1480  N   PHE A 186     -11.784  -3.846   0.254  1.00 98.88           N  
+ATOM   1481  CA  PHE A 186     -10.854  -3.409  -0.781  1.00 98.88           C  
+ATOM   1482  C   PHE A 186      -9.427  -3.503  -0.247  1.00 98.88           C  
+ATOM   1483  CB  PHE A 186     -11.190  -1.978  -1.202  1.00 98.88           C  
+ATOM   1484  O   PHE A 186      -9.176  -3.215   0.924  1.00 98.88           O  
+ATOM   1485  CG  PHE A 186     -12.481  -1.857  -1.983  1.00 98.88           C  
+ATOM   1486  CD1 PHE A 186     -12.440  -1.796  -3.388  1.00 98.88           C  
+ATOM   1487  CD2 PHE A 186     -13.720  -1.819  -1.313  1.00 98.88           C  
+ATOM   1488  CE1 PHE A 186     -13.631  -1.684  -4.123  1.00 98.88           C  
+ATOM   1489  CE2 PHE A 186     -14.912  -1.712  -2.050  1.00 98.88           C  
+ATOM   1490  CZ  PHE A 186     -14.862  -1.636  -3.451  1.00 98.88           C  
+ATOM   1491  N   LEU A 187      -8.469  -3.865  -1.096  1.00 98.94           N  
+ATOM   1492  CA  LEU A 187      -7.051  -3.735  -0.769  1.00 98.94           C  
+ATOM   1493  C   LEU A 187      -6.507  -2.476  -1.429  1.00 98.94           C  
+ATOM   1494  CB  LEU A 187      -6.251  -4.972  -1.193  1.00 98.94           C  
+ATOM   1495  O   LEU A 187      -6.810  -2.186  -2.584  1.00 98.94           O  
+ATOM   1496  CG  LEU A 187      -6.820  -6.312  -0.700  1.00 98.94           C  
+ATOM   1497  CD1 LEU A 187      -5.913  -7.446  -1.156  1.00 98.94           C  
+ATOM   1498  CD2 LEU A 187      -6.923  -6.394   0.819  1.00 98.94           C  
+ATOM   1499  N   ALA A 188      -5.667  -1.740  -0.717  1.00 98.94           N  
+ATOM   1500  CA  ALA A 188      -4.984  -0.589  -1.277  1.00 98.94           C  
+ATOM   1501  C   ALA A 188      -3.535  -0.547  -0.813  1.00 98.94           C  
+ATOM   1502  CB  ALA A 188      -5.749   0.686  -0.914  1.00 98.94           C  
+ATOM   1503  O   ALA A 188      -3.211  -0.942   0.304  1.00 98.94           O  
+ATOM   1504  N   GLY A 189      -2.652  -0.030  -1.651  1.00 98.81           N  
+ATOM   1505  CA  GLY A 189      -1.291   0.233  -1.221  1.00 98.81           C  
+ATOM   1506  C   GLY A 189      -0.470   0.923  -2.283  1.00 98.81           C  
+ATOM   1507  O   GLY A 189      -0.776   0.847  -3.476  1.00 98.81           O  
+ATOM   1508  N   ASP A 190       0.585   1.595  -1.839  1.00 98.50           N  
+ATOM   1509  CA  ASP A 190       1.476   2.324  -2.724  1.00 98.50           C  
+ATOM   1510  C   ASP A 190       2.882   1.725  -2.754  1.00 98.50           C  
+ATOM   1511  CB  ASP A 190       1.451   3.814  -2.390  1.00 98.50           C  
+ATOM   1512  O   ASP A 190       3.376   1.215  -1.750  1.00 98.50           O  
+ATOM   1513  CG  ASP A 190       2.353   4.163  -1.219  1.00 98.50           C  
+ATOM   1514  OD1 ASP A 190       2.012   3.735  -0.095  1.00 98.50           O  
+ATOM   1515  OD2 ASP A 190       3.359   4.866  -1.472  1.00 98.50           O  
+ATOM   1516  N   SER A 191       3.551   1.750  -3.908  1.00 98.25           N  
+ATOM   1517  CA  SER A 191       4.902   1.193  -4.044  1.00 98.25           C  
+ATOM   1518  C   SER A 191       4.973  -0.263  -3.543  1.00 98.25           C  
+ATOM   1519  CB  SER A 191       5.892   2.168  -3.390  1.00 98.25           C  
+ATOM   1520  O   SER A 191       4.233  -1.122  -4.030  1.00 98.25           O  
+ATOM   1521  OG  SER A 191       7.223   1.837  -3.740  1.00 98.25           O  
+ATOM   1522  N   SER A 192       5.836  -0.578  -2.570  1.00 98.69           N  
+ATOM   1523  CA  SER A 192       5.883  -1.906  -1.936  1.00 98.69           C  
+ATOM   1524  C   SER A 192       4.555  -2.330  -1.306  1.00 98.69           C  
+ATOM   1525  CB  SER A 192       6.973  -1.962  -0.867  1.00 98.69           C  
+ATOM   1526  O   SER A 192       4.245  -3.514  -1.291  1.00 98.69           O  
+ATOM   1527  OG  SER A 192       6.851  -0.909   0.084  1.00 98.69           O  
+ATOM   1528  N   GLY A 193       3.744  -1.387  -0.831  1.00 98.88           N  
+ATOM   1529  CA  GLY A 193       2.405  -1.662  -0.324  1.00 98.88           C  
+ATOM   1530  C   GLY A 193       1.461  -2.183  -1.407  1.00 98.88           C  
+ATOM   1531  O   GLY A 193       0.725  -3.136  -1.175  1.00 98.88           O  
+ATOM   1532  N   GLY A 194       1.545  -1.641  -2.626  1.00 98.81           N  
+ATOM   1533  CA  GLY A 194       0.796  -2.160  -3.776  1.00 98.81           C  
+ATOM   1534  C   GLY A 194       1.232  -3.579  -4.171  1.00 98.81           C  
+ATOM   1535  O   GLY A 194       0.413  -4.409  -4.557  1.00 98.81           O  
+ATOM   1536  N   ASN A 195       2.514  -3.900  -3.994  1.00 98.81           N  
+ATOM   1537  CA  ASN A 195       3.020  -5.262  -4.171  1.00 98.81           C  
+ATOM   1538  C   ASN A 195       2.484  -6.236  -3.101  1.00 98.81           C  
+ATOM   1539  CB  ASN A 195       4.548  -5.183  -4.177  1.00 98.81           C  
+ATOM   1540  O   ASN A 195       2.099  -7.362  -3.427  1.00 98.81           O  
+ATOM   1541  CG  ASN A 195       5.182  -6.541  -4.351  1.00 98.81           C  
+ATOM   1542  ND2 ASN A 195       5.729  -6.821  -5.502  1.00 98.81           N  
+ATOM   1543  OD1 ASN A 195       5.215  -7.331  -3.424  1.00 98.81           O  
+ATOM   1544  N   ILE A 196       2.409  -5.802  -1.836  1.00 98.94           N  
+ATOM   1545  CA  ILE A 196       1.782  -6.584  -0.758  1.00 98.94           C  
+ATOM   1546  C   ILE A 196       0.296  -6.800  -1.063  1.00 98.94           C  
+ATOM   1547  CB  ILE A 196       1.996  -5.919   0.624  1.00 98.94           C  
+ATOM   1548  O   ILE A 196      -0.171  -7.933  -0.985  1.00 98.94           O  
+ATOM   1549  CG1 ILE A 196       3.495  -5.881   0.998  1.00 98.94           C  
+ATOM   1550  CG2 ILE A 196       1.218  -6.677   1.718  1.00 98.94           C  
+ATOM   1551  CD1 ILE A 196       3.827  -4.894   2.123  1.00 98.94           C  
+ATOM   1552  N   ALA A 197      -0.425  -5.754  -1.481  1.00 98.88           N  
+ATOM   1553  CA  ALA A 197      -1.835  -5.842  -1.856  1.00 98.88           C  
+ATOM   1554  C   ALA A 197      -2.076  -6.882  -2.966  1.00 98.88           C  
+ATOM   1555  CB  ALA A 197      -2.329  -4.449  -2.267  1.00 98.88           C  
+ATOM   1556  O   ALA A 197      -2.975  -7.706  -2.829  1.00 98.88           O  
+ATOM   1557  N   HIS A 198      -1.235  -6.918  -4.008  1.00 98.44           N  
+ATOM   1558  CA  HIS A 198      -1.289  -7.965  -5.038  1.00 98.44           C  
+ATOM   1559  C   HIS A 198      -1.158  -9.383  -4.449  1.00 98.44           C  
+ATOM   1560  CB  HIS A 198      -0.191  -7.713  -6.086  1.00 98.44           C  
+ATOM   1561  O   HIS A 198      -1.973 -10.257  -4.744  1.00 98.44           O  
+ATOM   1562  CG  HIS A 198       0.031  -8.903  -6.986  1.00 98.44           C  
+ATOM   1563  CD2 HIS A 198       0.994  -9.867  -6.849  1.00 98.44           C  
+ATOM   1564  ND1 HIS A 198      -0.763  -9.281  -8.041  1.00 98.44           N  
+ATOM   1565  CE1 HIS A 198      -0.283 -10.429  -8.541  1.00 98.44           C  
+ATOM   1566  NE2 HIS A 198       0.791 -10.822  -7.845  1.00 98.44           N  
+ATOM   1567  N   ASN A 199      -0.157  -9.618  -3.596  1.00 98.31           N  
+ATOM   1568  CA  ASN A 199       0.081 -10.945  -3.018  1.00 98.31           C  
+ATOM   1569  C   ASN A 199      -1.046 -11.376  -2.063  1.00 98.31           C  
+ATOM   1570  CB  ASN A 199       1.445 -10.954  -2.310  1.00 98.31           C  
+ATOM   1571  O   ASN A 199      -1.441 -12.542  -2.046  1.00 98.31           O  
+ATOM   1572  CG  ASN A 199       2.585 -11.159  -3.286  1.00 98.31           C  
+ATOM   1573  ND2 ASN A 199       3.162 -10.103  -3.811  1.00 98.31           N  
+ATOM   1574  OD1 ASN A 199       2.961 -12.275  -3.594  1.00 98.31           O  
+ATOM   1575  N   VAL A 200      -1.596 -10.437  -1.288  1.00 98.75           N  
+ATOM   1576  CA  VAL A 200      -2.747 -10.692  -0.410  1.00 98.75           C  
+ATOM   1577  C   VAL A 200      -4.007 -10.979  -1.232  1.00 98.75           C  
+ATOM   1578  CB  VAL A 200      -2.943  -9.523   0.574  1.00 98.75           C  
+ATOM   1579  O   VAL A 200      -4.738 -11.908  -0.893  1.00 98.75           O  
+ATOM   1580  CG1 VAL A 200      -4.215  -9.666   1.416  1.00 98.75           C  
+ATOM   1581  CG2 VAL A 200      -1.768  -9.441   1.558  1.00 98.75           C  
+ATOM   1582  N   ALA A 201      -4.231 -10.257  -2.336  1.00 98.38           N  
+ATOM   1583  CA  ALA A 201      -5.341 -10.504  -3.259  1.00 98.38           C  
+ATOM   1584  C   ALA A 201      -5.289 -11.905  -3.875  1.00 98.38           C  
+ATOM   1585  CB  ALA A 201      -5.302  -9.463  -4.377  1.00 98.38           C  
+ATOM   1586  O   ALA A 201      -6.306 -12.593  -3.924  1.00 98.38           O  
+ATOM   1587  N   LEU A 202      -4.100 -12.351  -4.294  1.00 96.50           N  
+ATOM   1588  CA  LEU A 202      -3.906 -13.698  -4.826  1.00 96.50           C  
+ATOM   1589  C   LEU A 202      -4.333 -14.759  -3.803  1.00 96.50           C  
+ATOM   1590  CB  LEU A 202      -2.434 -13.849  -5.249  1.00 96.50           C  
+ATOM   1591  O   LEU A 202      -5.111 -15.657  -4.117  1.00 96.50           O  
+ATOM   1592  CG  LEU A 202      -2.059 -15.251  -5.764  1.00 96.50           C  
+ATOM   1593  CD1 LEU A 202      -2.981 -15.706  -6.892  1.00 96.50           C  
+ATOM   1594  CD2 LEU A 202      -0.618 -15.228  -6.271  1.00 96.50           C  
+ATOM   1595  N   ARG A 203      -3.888 -14.611  -2.551  1.00 96.81           N  
+ATOM   1596  CA  ARG A 203      -4.241 -15.539  -1.469  1.00 96.81           C  
+ATOM   1597  C   ARG A 203      -5.729 -15.486  -1.100  1.00 96.81           C  
+ATOM   1598  CB  ARG A 203      -3.324 -15.250  -0.276  1.00 96.81           C  
+ATOM   1599  O   ARG A 203      -6.292 -16.515  -0.737  1.00 96.81           O  
+ATOM   1600  CG  ARG A 203      -3.332 -16.384   0.757  1.00 96.81           C  
+ATOM   1601  CD  ARG A 203      -2.211 -16.129   1.770  1.00 96.81           C  
+ATOM   1602  NE  ARG A 203      -2.186 -17.137   2.846  1.00 96.81           N  
+ATOM   1603  NH1 ARG A 203      -0.316 -16.340   3.941  1.00 96.81           N  
+ATOM   1604  NH2 ARG A 203      -1.319 -18.199   4.666  1.00 96.81           N  
+ATOM   1605  CZ  ARG A 203      -1.280 -17.216   3.811  1.00 96.81           C  
+ATOM   1606  N   ALA A 204      -6.369 -14.320  -1.212  1.00 97.50           N  
+ATOM   1607  CA  ALA A 204      -7.809 -14.162  -0.994  1.00 97.50           C  
+ATOM   1608  C   ALA A 204      -8.639 -14.917  -2.043  1.00 97.50           C  
+ATOM   1609  CB  ALA A 204      -8.146 -12.667  -0.992  1.00 97.50           C  
+ATOM   1610  O   ALA A 204      -9.573 -15.640  -1.686  1.00 97.50           O  
+ATOM   1611  N   GLY A 205      -8.241 -14.820  -3.316  1.00 94.25           N  
+ATOM   1612  CA  GLY A 205      -8.858 -15.565  -4.413  1.00 94.25           C  
+ATOM   1613  C   GLY A 205      -8.763 -17.081  -4.239  1.00 94.25           C  
+ATOM   1614  O   GLY A 205      -9.719 -17.798  -4.521  1.00 94.25           O  
+ATOM   1615  N   GLU A 206      -7.640 -17.571  -3.707  1.00 92.69           N  
+ATOM   1616  CA  GLU A 206      -7.435 -18.995  -3.400  1.00 92.69           C  
+ATOM   1617  C   GLU A 206      -8.246 -19.469  -2.174  1.00 92.69           C  
+ATOM   1618  CB  GLU A 206      -5.930 -19.265  -3.181  1.00 92.69           C  
+ATOM   1619  O   GLU A 206      -8.589 -20.647  -2.079  1.00 92.69           O  
+ATOM   1620  CG  GLU A 206      -5.059 -19.137  -4.452  1.00 92.69           C  
+ATOM   1621  CD  GLU A 206      -3.544 -19.352  -4.196  1.00 92.69           C  
+ATOM   1622  OE1 GLU A 206      -2.781 -19.677  -5.145  1.00 92.69           O  
+ATOM   1623  OE2 GLU A 206      -3.076 -19.222  -3.042  1.00 92.69           O  
+ATOM   1624  N   SER A 207      -8.561 -18.579  -1.226  1.00 94.56           N  
+ATOM   1625  CA  SER A 207      -9.275 -18.932   0.009  1.00 94.56           C  
+ATOM   1626  C   SER A 207     -10.794 -18.763  -0.059  1.00 94.56           C  
+ATOM   1627  CB  SER A 207      -8.746 -18.087   1.164  1.00 94.56           C  
+ATOM   1628  O   SER A 207     -11.489 -19.198   0.858  1.00 94.56           O  
+ATOM   1629  OG  SER A 207      -9.282 -16.779   1.100  1.00 94.56           O  
+ATOM   1630  N   GLY A 208     -11.301 -18.037  -1.059  1.00 90.88           N  
+ATOM   1631  CA  GLY A 208     -12.703 -17.622  -1.148  1.00 90.88           C  
+ATOM   1632  C   GLY A 208     -13.087 -16.447  -0.237  1.00 90.88           C  
+ATOM   1633  O   GLY A 208     -14.272 -16.272   0.040  1.00 90.88           O  
+ATOM   1634  N   ILE A 209     -12.121 -15.655   0.257  1.00 95.88           N  
+ATOM   1635  CA  ILE A 209     -12.450 -14.378   0.919  1.00 95.88           C  
+ATOM   1636  C   ILE A 209     -12.769 -13.370  -0.182  1.00 95.88           C  
+ATOM   1637  CB  ILE A 209     -11.333 -13.869   1.864  1.00 95.88           C  
+ATOM   1638  O   ILE A 209     -11.937 -13.128  -1.053  1.00 95.88           O  
+ATOM   1639  CG1 ILE A 209     -11.231 -14.793   3.099  1.00 95.88           C  
+ATOM   1640  CG2 ILE A 209     -11.604 -12.417   2.320  1.00 95.88           C  
+ATOM   1641  CD1 ILE A 209     -10.105 -14.426   4.077  1.00 95.88           C  
+ATOM   1642  N   ASP A 210     -13.961 -12.784  -0.117  1.00 94.69           N  
+ATOM   1643  CA  ASP A 210     -14.445 -11.829  -1.109  1.00 94.69           C  
+ATOM   1644  C   ASP A 210     -13.737 -10.474  -0.948  1.00 94.69           C  
+ATOM   1645  CB  ASP A 210     -15.974 -11.731  -0.992  1.00 94.69           C  
+ATOM   1646  O   ASP A 210     -13.896  -9.780   0.060  1.00 94.69           O  
+ATOM   1647  CG  ASP A 210     -16.649 -11.073  -2.198  1.00 94.69           C  
+ATOM   1648  OD1 ASP A 210     -15.964 -10.903  -3.228  1.00 94.69           O  
+ATOM   1649  OD2 ASP A 210     -17.873 -10.828  -2.075  1.00 94.69           O  
+ATOM   1650  N   VAL A 211     -12.906 -10.131  -1.931  1.00 98.19           N  
+ATOM   1651  CA  VAL A 211     -12.251  -8.826  -2.060  1.00 98.19           C  
+ATOM   1652  C   VAL A 211     -12.838  -8.173  -3.305  1.00 98.19           C  
+ATOM   1653  CB  VAL A 211     -10.717  -8.960  -2.142  1.00 98.19           C  
+ATOM   1654  O   VAL A 211     -12.762  -8.732  -4.393  1.00 98.19           O  
+ATOM   1655  CG1 VAL A 211     -10.038  -7.608  -2.392  1.00 98.19           C  
+ATOM   1656  CG2 VAL A 211     -10.134  -9.531  -0.840  1.00 98.19           C  
+ATOM   1657  N   LEU A 212     -13.404  -6.981  -3.147  1.00 98.00           N  
+ATOM   1658  CA  LEU A 212     -14.153  -6.280  -4.192  1.00 98.00           C  
+ATOM   1659  C   LEU A 212     -13.255  -5.493  -5.150  1.00 98.00           C  
+ATOM   1660  CB  LEU A 212     -15.165  -5.338  -3.522  1.00 98.00           C  
+ATOM   1661  O   LEU A 212     -13.649  -5.180  -6.272  1.00 98.00           O  
+ATOM   1662  CG  LEU A 212     -16.160  -6.019  -2.566  1.00 98.00           C  
+ATOM   1663  CD1 LEU A 212     -17.118  -4.958  -2.035  1.00 98.00           C  
+ATOM   1664  CD2 LEU A 212     -16.970  -7.128  -3.234  1.00 98.00           C  
+ATOM   1665  N   GLY A 213     -12.035  -5.156  -4.727  1.00 98.62           N  
+ATOM   1666  CA  GLY A 213     -11.101  -4.445  -5.588  1.00 98.62           C  
+ATOM   1667  C   GLY A 213      -9.715  -4.226  -4.991  1.00 98.62           C  
+ATOM   1668  O   GLY A 213      -9.542  -4.160  -3.773  1.00 98.62           O  
+ATOM   1669  N   ASN A 214      -8.736  -4.056  -5.878  1.00 98.50           N  
+ATOM   1670  CA  ASN A 214      -7.368  -3.652  -5.564  1.00 98.50           C  
+ATOM   1671  C   ASN A 214      -7.080  -2.246  -6.092  1.00 98.50           C  
+ATOM   1672  CB  ASN A 214      -6.382  -4.625  -6.213  1.00 98.50           C  
+ATOM   1673  O   ASN A 214      -7.303  -1.964  -7.266  1.00 98.50           O  
+ATOM   1674  CG  ASN A 214      -6.289  -5.973  -5.546  1.00 98.50           C  
+ATOM   1675  ND2 ASN A 214      -5.769  -6.932  -6.270  1.00 98.50           N  
+ATOM   1676  OD1 ASN A 214      -6.611  -6.170  -4.390  1.00 98.50           O  
+ATOM   1677  N   ILE A 215      -6.496  -1.394  -5.256  1.00 98.88           N  
+ATOM   1678  CA  ILE A 215      -6.086  -0.030  -5.601  1.00 98.88           C  
+ATOM   1679  C   ILE A 215      -4.569   0.068  -5.448  1.00 98.88           C  
+ATOM   1680  CB  ILE A 215      -6.843   0.993  -4.733  1.00 98.88           C  
+ATOM   1681  O   ILE A 215      -4.038   0.169  -4.340  1.00 98.88           O  
+ATOM   1682  CG1 ILE A 215      -8.371   0.795  -4.872  1.00 98.88           C  
+ATOM   1683  CG2 ILE A 215      -6.416   2.422  -5.112  1.00 98.88           C  
+ATOM   1684  CD1 ILE A 215      -9.193   1.665  -3.926  1.00 98.88           C  
+ATOM   1685  N   LEU A 216      -3.860  -0.003  -6.570  1.00 98.88           N  
+ATOM   1686  CA  LEU A 216      -2.407  -0.135  -6.611  1.00 98.88           C  
+ATOM   1687  C   LEU A 216      -1.781   1.177  -7.092  1.00 98.88           C  
+ATOM   1688  CB  LEU A 216      -2.037  -1.340  -7.497  1.00 98.88           C  
+ATOM   1689  O   LEU A 216      -1.808   1.486  -8.284  1.00 98.88           O  
+ATOM   1690  CG  LEU A 216      -2.739  -2.665  -7.138  1.00 98.88           C  
+ATOM   1691  CD1 LEU A 216      -2.177  -3.790  -8.003  1.00 98.88           C  
+ATOM   1692  CD2 LEU A 216      -2.548  -3.066  -5.677  1.00 98.88           C  
+ATOM   1693  N   LEU A 217      -1.215   1.954  -6.166  1.00 98.75           N  
+ATOM   1694  CA  LEU A 217      -0.620   3.262  -6.454  1.00 98.75           C  
+ATOM   1695  C   LEU A 217       0.879   3.121  -6.722  1.00 98.75           C  
+ATOM   1696  CB  LEU A 217      -0.900   4.243  -5.301  1.00 98.75           C  
+ATOM   1697  O   LEU A 217       1.647   2.838  -5.806  1.00 98.75           O  
+ATOM   1698  CG  LEU A 217      -2.376   4.414  -4.910  1.00 98.75           C  
+ATOM   1699  CD1 LEU A 217      -2.487   5.412  -3.755  1.00 98.75           C  
+ATOM   1700  CD2 LEU A 217      -3.224   4.915  -6.076  1.00 98.75           C  
+ATOM   1701  N   ASN A 218       1.317   3.307  -7.968  1.00 97.81           N  
+ATOM   1702  CA  ASN A 218       2.715   3.124  -8.370  1.00 97.81           C  
+ATOM   1703  C   ASN A 218       3.323   1.821  -7.802  1.00 97.81           C  
+ATOM   1704  CB  ASN A 218       3.529   4.364  -7.961  1.00 97.81           C  
+ATOM   1705  O   ASN A 218       4.348   1.877  -7.112  1.00 97.81           O  
+ATOM   1706  CG  ASN A 218       3.239   5.598  -8.777  1.00 97.81           C  
+ATOM   1707  ND2 ASN A 218       4.284   6.275  -9.180  1.00 97.81           N  
+ATOM   1708  OD1 ASN A 218       2.117   5.990  -9.054  1.00 97.81           O  
+ATOM   1709  N   PRO A 219       2.681   0.650  -7.985  1.00 98.62           N  
+ATOM   1710  CA  PRO A 219       3.086  -0.561  -7.285  1.00 98.62           C  
+ATOM   1711  C   PRO A 219       4.523  -0.963  -7.637  1.00 98.62           C  
+ATOM   1712  CB  PRO A 219       2.061  -1.623  -7.673  1.00 98.62           C  
+ATOM   1713  O   PRO A 219       4.998  -0.773  -8.756  1.00 98.62           O  
+ATOM   1714  CG  PRO A 219       1.589  -1.161  -9.054  1.00 98.62           C  
+ATOM   1715  CD  PRO A 219       1.597   0.361  -8.917  1.00 98.62           C  
+ATOM   1716  N   MET A 220       5.244  -1.501  -6.657  1.00 98.44           N  
+ATOM   1717  CA  MET A 220       6.644  -1.870  -6.822  1.00 98.44           C  
+ATOM   1718  C   MET A 220       6.778  -3.282  -7.391  1.00 98.44           C  
+ATOM   1719  CB  MET A 220       7.399  -1.676  -5.505  1.00 98.44           C  
+ATOM   1720  O   MET A 220       6.556  -4.264  -6.687  1.00 98.44           O  
+ATOM   1721  CG  MET A 220       8.888  -2.005  -5.634  1.00 98.44           C  
+ATOM   1722  SD  MET A 220       9.832  -1.641  -4.133  1.00 98.44           S  
+ATOM   1723  CE  MET A 220      11.427  -2.384  -4.578  1.00 98.44           C  
+ATOM   1724  N   PHE A 221       7.224  -3.369  -8.639  1.00 98.69           N  
+ATOM   1725  CA  PHE A 221       7.624  -4.607  -9.307  1.00 98.69           C  
+ATOM   1726  C   PHE A 221       9.002  -4.426  -9.954  1.00 98.69           C  
+ATOM   1727  CB  PHE A 221       6.539  -5.018 -10.314  1.00 98.69           C  
+ATOM   1728  O   PHE A 221       9.597  -3.344  -9.889  1.00 98.69           O  
+ATOM   1729  CG  PHE A 221       5.182  -5.241  -9.675  1.00 98.69           C  
+ATOM   1730  CD1 PHE A 221       5.003  -6.283  -8.745  1.00 98.69           C  
+ATOM   1731  CD2 PHE A 221       4.103  -4.396  -9.991  1.00 98.69           C  
+ATOM   1732  CE1 PHE A 221       3.755  -6.472  -8.125  1.00 98.69           C  
+ATOM   1733  CE2 PHE A 221       2.850  -4.603  -9.389  1.00 98.69           C  
+ATOM   1734  CZ  PHE A 221       2.677  -5.632  -8.449  1.00 98.69           C  
+ATOM   1735  N   GLY A 222       9.540  -5.492 -10.525  1.00 98.19           N  
+ATOM   1736  CA  GLY A 222      10.822  -5.490 -11.209  1.00 98.19           C  
+ATOM   1737  C   GLY A 222      11.041  -6.782 -11.982  1.00 98.19           C  
+ATOM   1738  O   GLY A 222      10.137  -7.598 -12.111  1.00 98.19           O  
+ATOM   1739  N   GLY A 223      12.266  -6.949 -12.461  1.00 98.00           N  
+ATOM   1740  CA  GLY A 223      12.765  -8.144 -13.132  1.00 98.00           C  
+ATOM   1741  C   GLY A 223      14.257  -7.984 -13.399  1.00 98.00           C  
+ATOM   1742  O   GLY A 223      14.817  -6.913 -13.146  1.00 98.00           O  
+ATOM   1743  N   ASN A 224      14.934  -9.030 -13.863  1.00 97.00           N  
+ATOM   1744  CA  ASN A 224      16.383  -8.991 -14.068  1.00 97.00           C  
+ATOM   1745  C   ASN A 224      16.788  -8.031 -15.195  1.00 97.00           C  
+ATOM   1746  CB  ASN A 224      16.903 -10.413 -14.331  1.00 97.00           C  
+ATOM   1747  O   ASN A 224      17.745  -7.264 -15.033  1.00 97.00           O  
+ATOM   1748  CG  ASN A 224      17.124 -11.234 -13.071  1.00 97.00           C  
+ATOM   1749  ND2 ASN A 224      17.380 -12.507 -13.252  1.00 97.00           N  
+ATOM   1750  OD1 ASN A 224      17.159 -10.731 -11.952  1.00 97.00           O  
+ATOM   1751  N   GLU A 225      16.063  -8.050 -16.312  1.00 96.75           N  
+ATOM   1752  CA  GLU A 225      16.345  -7.192 -17.463  1.00 96.75           C  
+ATOM   1753  C   GLU A 225      15.974  -5.733 -17.179  1.00 96.75           C  
+ATOM   1754  CB  GLU A 225      15.635  -7.704 -18.721  1.00 96.75           C  
+ATOM   1755  O   GLU A 225      14.933  -5.437 -16.605  1.00 96.75           O  
+ATOM   1756  CG  GLU A 225      16.163  -9.085 -19.143  1.00 96.75           C  
+ATOM   1757  CD  GLU A 225      15.690  -9.517 -20.540  1.00 96.75           C  
+ATOM   1758  OE1 GLU A 225      16.228 -10.538 -21.024  1.00 96.75           O  
+ATOM   1759  OE2 GLU A 225      14.846  -8.808 -21.133  1.00 96.75           O  
+ATOM   1760  N   ARG A 226      16.829  -4.782 -17.569  1.00 97.81           N  
+ATOM   1761  CA  ARG A 226      16.524  -3.358 -17.378  1.00 97.81           C  
+ATOM   1762  C   ARG A 226      15.590  -2.860 -18.464  1.00 97.81           C  
+ATOM   1763  CB  ARG A 226      17.787  -2.495 -17.299  1.00 97.81           C  
+ATOM   1764  O   ARG A 226      15.917  -2.935 -19.650  1.00 97.81           O  
+ATOM   1765  CG  ARG A 226      18.688  -2.866 -16.121  1.00 97.81           C  
+ATOM   1766  CD  ARG A 226      17.979  -2.728 -14.772  1.00 97.81           C  
+ATOM   1767  NE  ARG A 226      18.895  -3.136 -13.709  1.00 97.81           N  
+ATOM   1768  NH1 ARG A 226      17.572  -2.646 -11.881  1.00 97.81           N  
+ATOM   1769  NH2 ARG A 226      19.591  -3.527 -11.627  1.00 97.81           N  
+ATOM   1770  CZ  ARG A 226      18.668  -3.094 -12.419  1.00 97.81           C  
+ATOM   1771  N   THR A 227      14.487  -2.276 -18.027  1.00 98.38           N  
+ATOM   1772  CA  THR A 227      13.520  -1.587 -18.884  1.00 98.38           C  
+ATOM   1773  C   THR A 227      14.049  -0.240 -19.384  1.00 98.38           C  
+ATOM   1774  CB  THR A 227      12.199  -1.398 -18.136  1.00 98.38           C  
+ATOM   1775  O   THR A 227      15.065   0.273 -18.903  1.00 98.38           O  
+ATOM   1776  CG2 THR A 227      11.533  -2.733 -17.808  1.00 98.38           C  
+ATOM   1777  OG1 THR A 227      12.415  -0.698 -16.930  1.00 98.38           O  
+ATOM   1778  N   GLU A 228      13.367   0.359 -20.362  1.00 98.12           N  
+ATOM   1779  CA  GLU A 228      13.755   1.670 -20.890  1.00 98.12           C  
+ATOM   1780  C   GLU A 228      13.517   2.783 -19.863  1.00 98.12           C  
+ATOM   1781  CB  GLU A 228      13.019   1.957 -22.209  1.00 98.12           C  
+ATOM   1782  O   GLU A 228      14.334   3.702 -19.761  1.00 98.12           O  
+ATOM   1783  CG  GLU A 228      13.460   1.031 -23.356  1.00 98.12           C  
+ATOM   1784  CD  GLU A 228      14.981   1.080 -23.577  1.00 98.12           C  
+ATOM   1785  OE1 GLU A 228      15.657   0.040 -23.361  1.00 98.12           O  
+ATOM   1786  OE2 GLU A 228      15.498   2.180 -23.862  1.00 98.12           O  
+ATOM   1787  N   SER A 229      12.466   2.692 -19.037  1.00 97.94           N  
+ATOM   1788  CA  SER A 229      12.258   3.618 -17.916  1.00 97.94           C  
+ATOM   1789  C   SER A 229      13.384   3.526 -16.887  1.00 97.94           C  
+ATOM   1790  CB  SER A 229      10.884   3.425 -17.257  1.00 97.94           C  
+ATOM   1791  O   SER A 229      13.899   4.561 -16.469  1.00 97.94           O  
+ATOM   1792  OG  SER A 229      10.745   2.206 -16.550  1.00 97.94           O  
+ATOM   1793  N   GLU A 230      13.840   2.318 -16.537  1.00 98.00           N  
+ATOM   1794  CA  GLU A 230      14.961   2.144 -15.604  1.00 98.00           C  
+ATOM   1795  C   GLU A 230      16.252   2.779 -16.133  1.00 98.00           C  
+ATOM   1796  CB  GLU A 230      15.208   0.657 -15.306  1.00 98.00           C  
+ATOM   1797  O   GLU A 230      16.963   3.423 -15.368  1.00 98.00           O  
+ATOM   1798  CG  GLU A 230      14.246   0.066 -14.268  1.00 98.00           C  
+ATOM   1799  CD  GLU A 230      14.501  -1.439 -14.131  1.00 98.00           C  
+ATOM   1800  OE1 GLU A 230      15.134  -1.869 -13.130  1.00 98.00           O  
+ATOM   1801  OE2 GLU A 230      14.116  -2.167 -15.076  1.00 98.00           O  
+ATOM   1802  N   LYS A 231      16.549   2.644 -17.433  1.00 97.12           N  
+ATOM   1803  CA  LYS A 231      17.750   3.236 -18.053  1.00 97.12           C  
+ATOM   1804  C   LYS A 231      17.647   4.757 -18.190  1.00 97.12           C  
+ATOM   1805  CB  LYS A 231      17.977   2.621 -19.442  1.00 97.12           C  
+ATOM   1806  O   LYS A 231      18.623   5.467 -17.970  1.00 97.12           O  
+ATOM   1807  CG  LYS A 231      18.302   1.120 -19.416  1.00 97.12           C  
+ATOM   1808  CD  LYS A 231      18.218   0.557 -20.842  1.00 97.12           C  
+ATOM   1809  CE  LYS A 231      18.091  -0.965 -20.818  1.00 97.12           C  
+ATOM   1810  NZ  LYS A 231      17.565  -1.467 -22.109  1.00 97.12           N  
+ATOM   1811  N   SER A 232      16.482   5.262 -18.594  1.00 94.94           N  
+ATOM   1812  CA  SER A 232      16.302   6.671 -18.972  1.00 94.94           C  
+ATOM   1813  C   SER A 232      15.945   7.598 -17.810  1.00 94.94           C  
+ATOM   1814  CB  SER A 232      15.256   6.794 -20.082  1.00 94.94           C  
+ATOM   1815  O   SER A 232      16.150   8.812 -17.930  1.00 94.94           O  
+ATOM   1816  OG  SER A 232      13.990   6.359 -19.635  1.00 94.94           O  
+ATOM   1817  N   LEU A 233      15.422   7.059 -16.703  1.00 95.00           N  
+ATOM   1818  CA  LEU A 233      14.981   7.823 -15.530  1.00 95.00           C  
+ATOM   1819  C   LEU A 233      15.845   7.600 -14.282  1.00 95.00           C  
+ATOM   1820  CB  LEU A 233      13.490   7.549 -15.241  1.00 95.00           C  
+ATOM   1821  O   LEU A 233      15.534   8.188 -13.243  1.00 95.00           O  
+ATOM   1822  CG  LEU A 233      12.524   7.910 -16.385  1.00 95.00           C  
+ATOM   1823  CD1 LEU A 233      11.122   7.410 -16.040  1.00 95.00           C  
+ATOM   1824  CD2 LEU A 233      12.458   9.427 -16.637  1.00 95.00           C  
+ATOM   1825  N   ASP A 234      16.922   6.808 -14.364  1.00 95.56           N  
+ATOM   1826  CA  ASP A 234      17.805   6.540 -13.222  1.00 95.56           C  
+ATOM   1827  C   ASP A 234      18.294   7.841 -12.568  1.00 95.56           C  
+ATOM   1828  CB  ASP A 234      18.992   5.634 -13.601  1.00 95.56           C  
+ATOM   1829  O   ASP A 234      18.950   8.675 -13.190  1.00 95.56           O  
+ATOM   1830  CG  ASP A 234      19.712   5.072 -12.364  1.00 95.56           C  
+ATOM   1831  OD1 ASP A 234      19.149   5.164 -11.244  1.00 95.56           O  
+ATOM   1832  OD2 ASP A 234      20.812   4.501 -12.507  1.00 95.56           O  
+ATOM   1833  N   GLY A 235      17.902   8.056 -11.310  1.00 89.19           N  
+ATOM   1834  CA  GLY A 235      18.277   9.229 -10.524  1.00 89.19           C  
+ATOM   1835  C   GLY A 235      17.564  10.540 -10.870  1.00 89.19           C  
+ATOM   1836  O   GLY A 235      17.722  11.495 -10.103  1.00 89.19           O  
+ATOM   1837  N   LYS A 236      16.746  10.602 -11.934  1.00 87.94           N  
+ATOM   1838  CA  LYS A 236      16.081  11.844 -12.384  1.00 87.94           C  
+ATOM   1839  C   LYS A 236      14.983  12.318 -11.432  1.00 87.94           C  
+ATOM   1840  CB  LYS A 236      15.517  11.686 -13.806  1.00 87.94           C  
+ATOM   1841  O   LYS A 236      14.948  13.488 -11.067  1.00 87.94           O  
+ATOM   1842  CG  LYS A 236      16.620  11.516 -14.861  1.00 87.94           C  
+ATOM   1843  CD  LYS A 236      16.051  11.696 -16.274  1.00 87.94           C  
+ATOM   1844  CE  LYS A 236      17.168  11.551 -17.307  1.00 87.94           C  
+ATOM   1845  NZ  LYS A 236      16.629  11.574 -18.684  1.00 87.94           N  
+ATOM   1846  N   TYR A 237      14.118  11.408 -10.982  1.00 85.81           N  
+ATOM   1847  CA  TYR A 237      12.978  11.713 -10.107  1.00 85.81           C  
+ATOM   1848  C   TYR A 237      13.087  10.952  -8.781  1.00 85.81           C  
+ATOM   1849  CB  TYR A 237      11.654  11.485 -10.850  1.00 85.81           C  
+ATOM   1850  O   TYR A 237      12.261  10.109  -8.440  1.00 85.81           O  
+ATOM   1851  CG  TYR A 237      11.497  12.354 -12.083  1.00 85.81           C  
+ATOM   1852  CD1 TYR A 237      11.144  13.711 -11.952  1.00 85.81           C  
+ATOM   1853  CD2 TYR A 237      11.717  11.806 -13.362  1.00 85.81           C  
+ATOM   1854  CE1 TYR A 237      10.984  14.511 -13.098  1.00 85.81           C  
+ATOM   1855  CE2 TYR A 237      11.591  12.613 -14.509  1.00 85.81           C  
+ATOM   1856  OH  TYR A 237      11.060  14.753 -15.474  1.00 85.81           O  
+ATOM   1857  CZ  TYR A 237      11.217  13.968 -14.378  1.00 85.81           C  
+ATOM   1858  N   PHE A 238      14.134  11.275  -8.015  1.00 86.44           N  
+ATOM   1859  CA  PHE A 238      14.406  10.818  -6.639  1.00 86.44           C  
+ATOM   1860  C   PHE A 238      14.895   9.375  -6.466  1.00 86.44           C  
+ATOM   1861  CB  PHE A 238      13.199  11.081  -5.722  1.00 86.44           C  
+ATOM   1862  O   PHE A 238      15.769   9.152  -5.626  1.00 86.44           O  
+ATOM   1863  CG  PHE A 238      12.533  12.424  -5.936  1.00 86.44           C  
+ATOM   1864  CD1 PHE A 238      13.156  13.593  -5.463  1.00 86.44           C  
+ATOM   1865  CD2 PHE A 238      11.307  12.508  -6.625  1.00 86.44           C  
+ATOM   1866  CE1 PHE A 238      12.565  14.843  -5.699  1.00 86.44           C  
+ATOM   1867  CE2 PHE A 238      10.718  13.758  -6.863  1.00 86.44           C  
+ATOM   1868  CZ  PHE A 238      11.352  14.923  -6.404  1.00 86.44           C  
+ATOM   1869  N   VAL A 239      14.371   8.425  -7.239  1.00 91.81           N  
+ATOM   1870  CA  VAL A 239      14.699   6.993  -7.135  1.00 91.81           C  
+ATOM   1871  C   VAL A 239      15.871   6.639  -8.052  1.00 91.81           C  
+ATOM   1872  CB  VAL A 239      13.460   6.121  -7.435  1.00 91.81           C  
+ATOM   1873  O   VAL A 239      15.979   7.178  -9.151  1.00 91.81           O  
+ATOM   1874  CG1 VAL A 239      13.727   4.631  -7.172  1.00 91.81           C  
+ATOM   1875  CG2 VAL A 239      12.271   6.528  -6.551  1.00 91.81           C  
+ATOM   1876  N   THR A 240      16.739   5.726  -7.608  1.00 95.25           N  
+ATOM   1877  CA  THR A 240      17.804   5.149  -8.442  1.00 95.25           C  
+ATOM   1878  C   THR A 240      17.645   3.640  -8.602  1.00 95.25           C  
+ATOM   1879  CB  THR A 240      19.216   5.465  -7.920  1.00 95.25           C  
+ATOM   1880  O   THR A 240      17.064   2.969  -7.738  1.00 95.25           O  
+ATOM   1881  CG2 THR A 240      19.459   6.947  -7.640  1.00 95.25           C  
+ATOM   1882  OG1 THR A 240      19.488   4.764  -6.728  1.00 95.25           O  
+ATOM   1883  N   VAL A 241      18.213   3.093  -9.676  1.00 97.31           N  
+ATOM   1884  CA  VAL A 241      18.337   1.651  -9.917  1.00 97.31           C  
+ATOM   1885  C   VAL A 241      19.086   0.984  -8.766  1.00 97.31           C  
+ATOM   1886  CB  VAL A 241      19.052   1.406 -11.262  1.00 97.31           C  
+ATOM   1887  O   VAL A 241      18.596   0.013  -8.191  1.00 97.31           O  
+ATOM   1888  CG1 VAL A 241      19.576  -0.027 -11.373  1.00 97.31           C  
+ATOM   1889  CG2 VAL A 241      18.094   1.674 -12.428  1.00 97.31           C  
+ATOM   1890  N   ARG A 242      20.218   1.565  -8.348  1.00 95.62           N  
+ATOM   1891  CA  ARG A 242      21.010   1.076  -7.208  1.00 95.62           C  
+ATOM   1892  C   ARG A 242      20.164   0.933  -5.944  1.00 95.62           C  
+ATOM   1893  CB  ARG A 242      22.183   2.038  -6.975  1.00 95.62           C  
+ATOM   1894  O   ARG A 242      20.331  -0.025  -5.189  1.00 95.62           O  
+ATOM   1895  CG  ARG A 242      23.058   1.598  -5.793  1.00 95.62           C  
+ATOM   1896  CD  ARG A 242      24.196   2.591  -5.569  1.00 95.62           C  
+ATOM   1897  NE  ARG A 242      25.010   2.195  -4.407  1.00 95.62           N  
+ATOM   1898  NH1 ARG A 242      26.523   3.913  -4.518  1.00 95.62           N  
+ATOM   1899  NH2 ARG A 242      26.735   2.354  -2.932  1.00 95.62           N  
+ATOM   1900  CZ  ARG A 242      26.080   2.822  -3.957  1.00 95.62           C  
+ATOM   1901  N   ASP A 243      19.266   1.883  -5.698  1.00 95.62           N  
+ATOM   1902  CA  ASP A 243      18.389   1.826  -4.538  1.00 95.62           C  
+ATOM   1903  C   ASP A 243      17.364   0.708  -4.659  1.00 95.62           C  
+ATOM   1904  CB  ASP A 243      17.699   3.175  -4.303  1.00 95.62           C  
+ATOM   1905  O   ASP A 243      17.189  -0.041  -3.700  1.00 95.62           O  
+ATOM   1906  CG  ASP A 243      18.691   4.273  -3.951  1.00 95.62           C  
+ATOM   1907  OD1 ASP A 243      19.687   3.947  -3.266  1.00 95.62           O  
+ATOM   1908  OD2 ASP A 243      18.422   5.437  -4.316  1.00 95.62           O  
+ATOM   1909  N   ARG A 244      16.717   0.565  -5.824  1.00 96.31           N  
+ATOM   1910  CA  ARG A 244      15.780  -0.540  -6.080  1.00 96.31           C  
+ATOM   1911  C   ARG A 244      16.446  -1.896  -5.884  1.00 96.31           C  
+ATOM   1912  CB  ARG A 244      15.199  -0.460  -7.503  1.00 96.31           C  
+ATOM   1913  O   ARG A 244      15.865  -2.742  -5.212  1.00 96.31           O  
+ATOM   1914  CG  ARG A 244      14.241   0.706  -7.778  1.00 96.31           C  
+ATOM   1915  CD  ARG A 244      13.044   0.756  -6.821  1.00 96.31           C  
+ATOM   1916  NE  ARG A 244      13.375   1.518  -5.608  1.00 96.31           N  
+ATOM   1917  NH1 ARG A 244      11.692   0.765  -4.239  1.00 96.31           N  
+ATOM   1918  NH2 ARG A 244      13.054   2.342  -3.529  1.00 96.31           N  
+ATOM   1919  CZ  ARG A 244      12.708   1.529  -4.476  1.00 96.31           C  
+ATOM   1920  N   ASP A 245      17.660  -2.070  -6.395  1.00 96.94           N  
+ATOM   1921  CA  ASP A 245      18.417  -3.314  -6.255  1.00 96.94           C  
+ATOM   1922  C   ASP A 245      18.719  -3.628  -4.790  1.00 96.94           C  
+ATOM   1923  CB  ASP A 245      19.734  -3.210  -7.025  1.00 96.94           C  
+ATOM   1924  O   ASP A 245      18.546  -4.763  -4.349  1.00 96.94           O  
+ATOM   1925  CG  ASP A 245      19.562  -3.099  -8.535  1.00 96.94           C  
+ATOM   1926  OD1 ASP A 245      18.454  -3.321  -9.082  1.00 96.94           O  
+ATOM   1927  OD2 ASP A 245      20.579  -2.818  -9.199  1.00 96.94           O  
+ATOM   1928  N   TRP A 246      19.111  -2.614  -4.010  1.00 97.06           N  
+ATOM   1929  CA  TRP A 246      19.338  -2.781  -2.577  1.00 97.06           C  
+ATOM   1930  C   TRP A 246      18.060  -3.234  -1.858  1.00 97.06           C  
+ATOM   1931  CB  TRP A 246      19.909  -1.483  -1.990  1.00 97.06           C  
+ATOM   1932  O   TRP A 246      18.109  -4.168  -1.063  1.00 97.06           O  
+ATOM   1933  CG  TRP A 246      20.400  -1.599  -0.578  1.00 97.06           C  
+ATOM   1934  CD1 TRP A 246      21.694  -1.718  -0.200  1.00 97.06           C  
+ATOM   1935  CD2 TRP A 246      19.619  -1.685   0.653  1.00 97.06           C  
+ATOM   1936  CE2 TRP A 246      20.517  -1.877   1.745  1.00 97.06           C  
+ATOM   1937  CE3 TRP A 246      18.240  -1.620   0.957  1.00 97.06           C  
+ATOM   1938  NE1 TRP A 246      21.771  -1.843   1.175  1.00 97.06           N  
+ATOM   1939  CH2 TRP A 246      18.686  -2.058   3.309  1.00 97.06           C  
+ATOM   1940  CZ2 TRP A 246      20.067  -2.051   3.058  1.00 97.06           C  
+ATOM   1941  CZ3 TRP A 246      17.776  -1.813   2.270  1.00 97.06           C  
+ATOM   1942  N   TYR A 247      16.907  -2.616  -2.148  1.00 97.81           N  
+ATOM   1943  CA  TYR A 247      15.643  -2.980  -1.495  1.00 97.81           C  
+ATOM   1944  C   TYR A 247      15.137  -4.361  -1.905  1.00 97.81           C  
+ATOM   1945  CB  TYR A 247      14.559  -1.937  -1.772  1.00 97.81           C  
+ATOM   1946  O   TYR A 247      14.642  -5.091  -1.051  1.00 97.81           O  
+ATOM   1947  CG  TYR A 247      14.823  -0.591  -1.138  1.00 97.81           C  
+ATOM   1948  CD1 TYR A 247      14.905  -0.469   0.261  1.00 97.81           C  
+ATOM   1949  CD2 TYR A 247      14.970   0.543  -1.949  1.00 97.81           C  
+ATOM   1950  CE1 TYR A 247      15.180   0.782   0.840  1.00 97.81           C  
+ATOM   1951  CE2 TYR A 247      15.252   1.794  -1.381  1.00 97.81           C  
+ATOM   1952  OH  TYR A 247      15.678   3.125   0.531  1.00 97.81           O  
+ATOM   1953  CZ  TYR A 247      15.368   1.912   0.013  1.00 97.81           C  
+ATOM   1954  N   TRP A 248      15.287  -4.742  -3.176  1.00 98.31           N  
+ATOM   1955  CA  TRP A 248      14.953  -6.098  -3.607  1.00 98.31           C  
+ATOM   1956  C   TRP A 248      15.846  -7.129  -2.926  1.00 98.31           C  
+ATOM   1957  CB  TRP A 248      14.997  -6.216  -5.133  1.00 98.31           C  
+ATOM   1958  O   TRP A 248      15.322  -8.092  -2.380  1.00 98.31           O  
+ATOM   1959  CG  TRP A 248      13.780  -5.677  -5.817  1.00 98.31           C  
+ATOM   1960  CD1 TRP A 248      13.758  -4.744  -6.796  1.00 98.31           C  
+ATOM   1961  CD2 TRP A 248      12.386  -6.047  -5.581  1.00 98.31           C  
+ATOM   1962  CE2 TRP A 248      11.566  -5.271  -6.453  1.00 98.31           C  
+ATOM   1963  CE3 TRP A 248      11.729  -6.962  -4.727  1.00 98.31           C  
+ATOM   1964  NE1 TRP A 248      12.452  -4.519  -7.194  1.00 98.31           N  
+ATOM   1965  CH2 TRP A 248       9.547  -6.264  -5.568  1.00 98.31           C  
+ATOM   1966  CZ2 TRP A 248      10.171  -5.383  -6.466  1.00 98.31           C  
+ATOM   1967  CZ3 TRP A 248      10.325  -7.064  -4.715  1.00 98.31           C  
+ATOM   1968  N   LYS A 249      17.160  -6.891  -2.849  1.00 97.50           N  
+ATOM   1969  CA  LYS A 249      18.079  -7.777  -2.121  1.00 97.50           C  
+ATOM   1970  C   LYS A 249      17.737  -7.890  -0.630  1.00 97.50           C  
+ATOM   1971  CB  LYS A 249      19.519  -7.292  -2.335  1.00 97.50           C  
+ATOM   1972  O   LYS A 249      17.895  -8.956  -0.056  1.00 97.50           O  
+ATOM   1973  CG  LYS A 249      20.523  -8.276  -1.721  1.00 97.50           C  
+ATOM   1974  CD  LYS A 249      21.973  -7.856  -1.969  1.00 97.50           C  
+ATOM   1975  CE  LYS A 249      22.872  -8.913  -1.318  1.00 97.50           C  
+ATOM   1976  NZ  LYS A 249      24.311  -8.587  -1.450  1.00 97.50           N  
+ATOM   1977  N   ALA A 250      17.286  -6.803  -0.002  1.00 97.62           N  
+ATOM   1978  CA  ALA A 250      16.892  -6.806   1.406  1.00 97.62           C  
+ATOM   1979  C   ALA A 250      15.580  -7.567   1.667  1.00 97.62           C  
+ATOM   1980  CB  ALA A 250      16.781  -5.348   1.871  1.00 97.62           C  
+ATOM   1981  O   ALA A 250      15.386  -8.081   2.766  1.00 97.62           O  
+ATOM   1982  N   PHE A 251      14.661  -7.590   0.696  1.00 98.69           N  
+ATOM   1983  CA  PHE A 251      13.343  -8.201   0.857  1.00 98.69           C  
+ATOM   1984  C   PHE A 251      13.274  -9.649   0.368  1.00 98.69           C  
+ATOM   1985  CB  PHE A 251      12.295  -7.333   0.151  1.00 98.69           C  
+ATOM   1986  O   PHE A 251      12.602 -10.458   0.998  1.00 98.69           O  
+ATOM   1987  CG  PHE A 251      10.893  -7.905   0.236  1.00 98.69           C  
+ATOM   1988  CD1 PHE A 251      10.263  -8.407  -0.916  1.00 98.69           C  
+ATOM   1989  CD2 PHE A 251      10.234  -7.982   1.478  1.00 98.69           C  
+ATOM   1990  CE1 PHE A 251       8.977  -8.963  -0.822  1.00 98.69           C  
+ATOM   1991  CE2 PHE A 251       8.952  -8.550   1.572  1.00 98.69           C  
+ATOM   1992  CZ  PHE A 251       8.320  -9.040   0.418  1.00 98.69           C  
+ATOM   1993  N   LEU A 252      13.913  -9.984  -0.752  1.00 98.75           N  
+ATOM   1994  CA  LEU A 252      13.849 -11.325  -1.333  1.00 98.75           C  
+ATOM   1995  C   LEU A 252      14.556 -12.365  -0.446  1.00 98.75           C  
+ATOM   1996  CB  LEU A 252      14.445 -11.291  -2.750  1.00 98.75           C  
+ATOM   1997  O   LEU A 252      15.431 -11.996   0.342  1.00 98.75           O  
+ATOM   1998  CG  LEU A 252      13.613 -10.480  -3.759  1.00 98.75           C  
+ATOM   1999  CD1 LEU A 252      14.368 -10.387  -5.081  1.00 98.75           C  
+ATOM   2000  CD2 LEU A 252      12.243 -11.112  -4.009  1.00 98.75           C  
+ATOM   2001  N   PRO A 253      14.201 -13.661  -0.564  1.00 98.19           N  
+ATOM   2002  CA  PRO A 253      14.959 -14.724   0.088  1.00 98.19           C  
+ATOM   2003  C   PRO A 253      16.447 -14.652  -0.269  1.00 98.19           C  
+ATOM   2004  CB  PRO A 253      14.348 -16.045  -0.385  1.00 98.19           C  
+ATOM   2005  O   PRO A 253      16.822 -14.244  -1.369  1.00 98.19           O  
+ATOM   2006  CG  PRO A 253      12.944 -15.648  -0.828  1.00 98.19           C  
+ATOM   2007  CD  PRO A 253      13.121 -14.229  -1.361  1.00 98.19           C  
+ATOM   2008  N   GLU A 254      17.311 -15.069   0.651  1.00 95.88           N  
+ATOM   2009  CA  GLU A 254      18.747 -15.080   0.385  1.00 95.88           C  
+ATOM   2010  C   GLU A 254      19.077 -15.981  -0.818  1.00 95.88           C  
+ATOM   2011  CB  GLU A 254      19.510 -15.489   1.650  1.00 95.88           C  
+ATOM   2012  O   GLU A 254      18.626 -17.121  -0.902  1.00 95.88           O  
+ATOM   2013  CG  GLU A 254      21.027 -15.360   1.451  1.00 95.88           C  
+ATOM   2014  CD  GLU A 254      21.829 -15.542   2.748  1.00 95.88           C  
+ATOM   2015  OE1 GLU A 254      23.015 -15.140   2.730  1.00 95.88           O  
+ATOM   2016  OE2 GLU A 254      21.267 -16.058   3.740  1.00 95.88           O  
+ATOM   2017  N   GLY A 255      19.862 -15.450  -1.760  1.00 95.50           N  
+ATOM   2018  CA  GLY A 255      20.242 -16.142  -2.995  1.00 95.50           C  
+ATOM   2019  C   GLY A 255      19.240 -16.014  -4.146  1.00 95.50           C  
+ATOM   2020  O   GLY A 255      19.580 -16.386  -5.267  1.00 95.50           O  
+ATOM   2021  N   GLU A 256      18.051 -15.457  -3.909  1.00 97.75           N  
+ATOM   2022  CA  GLU A 256      17.075 -15.200  -4.968  1.00 97.75           C  
+ATOM   2023  C   GLU A 256      17.405 -13.947  -5.787  1.00 97.75           C  
+ATOM   2024  CB  GLU A 256      15.652 -15.132  -4.390  1.00 97.75           C  
+ATOM   2025  O   GLU A 256      18.008 -12.983  -5.306  1.00 97.75           O  
+ATOM   2026  CG  GLU A 256      15.101 -16.527  -4.050  1.00 97.75           C  
+ATOM   2027  CD  GLU A 256      14.834 -17.372  -5.303  1.00 97.75           C  
+ATOM   2028  OE1 GLU A 256      14.581 -18.588  -5.189  1.00 97.75           O  
+ATOM   2029  OE2 GLU A 256      14.795 -16.796  -6.418  1.00 97.75           O  
+ATOM   2030  N   ASP A 257      16.977 -13.965  -7.048  1.00 97.06           N  
+ATOM   2031  CA  ASP A 257      17.114 -12.855  -7.985  1.00 97.06           C  
+ATOM   2032  C   ASP A 257      15.771 -12.159  -8.256  1.00 97.06           C  
+ATOM   2033  CB  ASP A 257      17.822 -13.339  -9.260  1.00 97.06           C  
+ATOM   2034  O   ASP A 257      14.733 -12.468  -7.665  1.00 97.06           O  
+ATOM   2035  CG  ASP A 257      16.960 -14.168 -10.221  1.00 97.06           C  
+ATOM   2036  OD1 ASP A 257      15.753 -14.385  -9.957  1.00 97.06           O  
+ATOM   2037  OD2 ASP A 257      17.504 -14.506 -11.291  1.00 97.06           O  
+ATOM   2038  N   ARG A 258      15.774 -11.188  -9.173  1.00 97.62           N  
+ATOM   2039  CA  ARG A 258      14.582 -10.385  -9.464  1.00 97.62           C  
+ATOM   2040  C   ARG A 258      13.571 -11.094 -10.369  1.00 97.62           C  
+ATOM   2041  CB  ARG A 258      14.986  -8.986  -9.948  1.00 97.62           C  
+ATOM   2042  O   ARG A 258      12.510 -10.532 -10.599  1.00 97.62           O  
+ATOM   2043  CG  ARG A 258      15.737  -8.229  -8.837  1.00 97.62           C  
+ATOM   2044  CD  ARG A 258      16.015  -6.774  -9.216  1.00 97.62           C  
+ATOM   2045  NE  ARG A 258      16.902  -6.678 -10.392  1.00 97.62           N  
+ATOM   2046  NH1 ARG A 258      15.892  -4.864 -11.355  1.00 97.62           N  
+ATOM   2047  NH2 ARG A 258      17.506  -5.948 -12.458  1.00 97.62           N  
+ATOM   2048  CZ  ARG A 258      16.777  -5.821 -11.386  1.00 97.62           C  
+ATOM   2049  N   GLU A 259      13.820 -12.337 -10.791  1.00 97.62           N  
+ATOM   2050  CA  GLU A 259      12.781 -13.193 -11.390  1.00 97.62           C  
+ATOM   2051  C   GLU A 259      11.919 -13.898 -10.339  1.00 97.62           C  
+ATOM   2052  CB  GLU A 259      13.381 -14.224 -12.364  1.00 97.62           C  
+ATOM   2053  O   GLU A 259      11.017 -14.668 -10.688  1.00 97.62           O  
+ATOM   2054  CG  GLU A 259      14.086 -13.574 -13.556  1.00 97.62           C  
+ATOM   2055  CD  GLU A 259      13.201 -12.551 -14.275  1.00 97.62           C  
+ATOM   2056  OE1 GLU A 259      13.713 -11.450 -14.582  1.00 97.62           O  
+ATOM   2057  OE2 GLU A 259      11.989 -12.824 -14.443  1.00 97.62           O  
+ATOM   2058  N   HIS A 260      12.191 -13.677  -9.049  1.00 98.50           N  
+ATOM   2059  CA  HIS A 260      11.384 -14.240  -7.978  1.00 98.50           C  
+ATOM   2060  C   HIS A 260       9.967 -13.650  -8.060  1.00 98.50           C  
+ATOM   2061  CB  HIS A 260      12.060 -13.977  -6.627  1.00 98.50           C  
+ATOM   2062  O   HIS A 260       9.848 -12.436  -8.243  1.00 98.50           O  
+ATOM   2063  CG  HIS A 260      11.320 -14.606  -5.475  1.00 98.50           C  
+ATOM   2064  CD2 HIS A 260      11.527 -15.848  -4.940  1.00 98.50           C  
+ATOM   2065  ND1 HIS A 260      10.271 -14.042  -4.791  1.00 98.50           N  
+ATOM   2066  CE1 HIS A 260       9.844 -14.924  -3.875  1.00 98.50           C  
+ATOM   2067  NE2 HIS A 260      10.579 -16.040  -3.928  1.00 98.50           N  
+ATOM   2068  N   PRO A 261       8.887 -14.435  -7.874  1.00 98.12           N  
+ATOM   2069  CA  PRO A 261       7.505 -13.961  -8.057  1.00 98.12           C  
+ATOM   2070  C   PRO A 261       7.120 -12.736  -7.219  1.00 98.12           C  
+ATOM   2071  CB  PRO A 261       6.623 -15.157  -7.698  1.00 98.12           C  
+ATOM   2072  O   PRO A 261       6.280 -11.938  -7.615  1.00 98.12           O  
+ATOM   2073  CG  PRO A 261       7.502 -16.338  -8.089  1.00 98.12           C  
+ATOM   2074  CD  PRO A 261       8.896 -15.880  -7.686  1.00 98.12           C  
+ATOM   2075  N   ALA A 262       7.768 -12.546  -6.068  1.00 98.25           N  
+ATOM   2076  CA  ALA A 262       7.610 -11.335  -5.262  1.00 98.25           C  
+ATOM   2077  C   ALA A 262       8.060 -10.053  -5.997  1.00 98.25           C  
+ATOM   2078  CB  ALA A 262       8.416 -11.520  -3.975  1.00 98.25           C  
+ATOM   2079  O   ALA A 262       7.486  -8.987  -5.788  1.00 98.25           O  
+ATOM   2080  N   CYS A 263       9.093 -10.151  -6.840  1.00 98.62           N  
+ATOM   2081  CA  CYS A 263       9.625  -9.057  -7.649  1.00 98.62           C  
+ATOM   2082  C   CYS A 263       8.945  -8.977  -9.017  1.00 98.62           C  
+ATOM   2083  CB  CYS A 263      11.144  -9.236  -7.772  1.00 98.62           C  
+ATOM   2084  O   CYS A 263       8.474  -7.903  -9.400  1.00 98.62           O  
+ATOM   2085  SG  CYS A 263      11.865  -7.760  -8.534  1.00 98.62           S  
+ATOM   2086  N   ASN A 264       8.845 -10.122  -9.693  1.00 98.31           N  
+ATOM   2087  CA  ASN A 264       8.268 -10.273 -11.021  1.00 98.31           C  
+ATOM   2088  C   ASN A 264       7.106 -11.289 -10.984  1.00 98.31           C  
+ATOM   2089  CB  ASN A 264       9.394 -10.688 -11.988  1.00 98.31           C  
+ATOM   2090  O   ASN A 264       7.326 -12.474 -11.249  1.00 98.31           O  
+ATOM   2091  CG  ASN A 264       8.943 -10.631 -13.439  1.00 98.31           C  
+ATOM   2092  ND2 ASN A 264       9.806 -10.942 -14.379  1.00 98.31           N  
+ATOM   2093  OD1 ASN A 264       7.795 -10.337 -13.749  1.00 98.31           O  
+ATOM   2094  N   PRO A 265       5.870 -10.868 -10.647  1.00 97.81           N  
+ATOM   2095  CA  PRO A 265       4.740 -11.787 -10.473  1.00 97.81           C  
+ATOM   2096  C   PRO A 265       4.374 -12.597 -11.718  1.00 97.81           C  
+ATOM   2097  CB  PRO A 265       3.557 -10.915 -10.052  1.00 97.81           C  
+ATOM   2098  O   PRO A 265       3.818 -13.683 -11.592  1.00 97.81           O  
+ATOM   2099  CG  PRO A 265       4.210  -9.699  -9.409  1.00 97.81           C  
+ATOM   2100  CD  PRO A 265       5.497  -9.529 -10.213  1.00 97.81           C  
+ATOM   2101  N   PHE A 266       4.696 -12.076 -12.906  1.00 96.75           N  
+ATOM   2102  CA  PHE A 266       4.422 -12.709 -14.197  1.00 96.75           C  
+ATOM   2103  C   PHE A 266       5.684 -13.291 -14.854  1.00 96.75           C  
+ATOM   2104  CB  PHE A 266       3.676 -11.721 -15.107  1.00 96.75           C  
+ATOM   2105  O   PHE A 266       5.687 -13.552 -16.058  1.00 96.75           O  
+ATOM   2106  CG  PHE A 266       2.321 -11.293 -14.580  1.00 96.75           C  
+ATOM   2107  CD1 PHE A 266       1.245 -12.201 -14.599  1.00 96.75           C  
+ATOM   2108  CD2 PHE A 266       2.129  -9.991 -14.081  1.00 96.75           C  
+ATOM   2109  CE1 PHE A 266      -0.018 -11.810 -14.123  1.00 96.75           C  
+ATOM   2110  CE2 PHE A 266       0.864  -9.602 -13.607  1.00 96.75           C  
+ATOM   2111  CZ  PHE A 266      -0.204 -10.511 -13.623  1.00 96.75           C  
+ATOM   2112  N   SER A 267       6.762 -13.500 -14.089  1.00 95.62           N  
+ATOM   2113  CA  SER A 267       7.899 -14.296 -14.561  1.00 95.62           C  
+ATOM   2114  C   SER A 267       7.473 -15.753 -14.802  1.00 95.62           C  
+ATOM   2115  CB  SER A 267       9.072 -14.234 -13.569  1.00 95.62           C  
+ATOM   2116  O   SER A 267       6.442 -16.188 -14.285  1.00 95.62           O  
+ATOM   2117  OG  SER A 267       8.929 -15.136 -12.484  1.00 95.62           O  
+ATOM   2118  N   PRO A 268       8.284 -16.574 -15.495  1.00 94.38           N  
+ATOM   2119  CA  PRO A 268       8.026 -18.012 -15.615  1.00 94.38           C  
+ATOM   2120  C   PRO A 268       7.926 -18.760 -14.274  1.00 94.38           C  
+ATOM   2121  CB  PRO A 268       9.192 -18.555 -16.447  1.00 94.38           C  
+ATOM   2122  O   PRO A 268       7.427 -19.881 -14.237  1.00 94.38           O  
+ATOM   2123  CG  PRO A 268       9.644 -17.348 -17.267  1.00 94.38           C  
+ATOM   2124  CD  PRO A 268       9.435 -16.187 -16.298  1.00 94.38           C  
+ATOM   2125  N   ARG A 269       8.425 -18.167 -13.177  1.00 95.38           N  
+ATOM   2126  CA  ARG A 269       8.325 -18.717 -11.814  1.00 95.38           C  
+ATOM   2127  C   ARG A 269       7.050 -18.273 -11.093  1.00 95.38           C  
+ATOM   2128  CB  ARG A 269       9.568 -18.329 -10.992  1.00 95.38           C  
+ATOM   2129  O   ARG A 269       6.733 -18.813 -10.034  1.00 95.38           O  
+ATOM   2130  CG  ARG A 269      10.879 -18.720 -11.691  1.00 95.38           C  
+ATOM   2131  CD  ARG A 269      12.072 -18.719 -10.733  1.00 95.38           C  
+ATOM   2132  NE  ARG A 269      12.479 -17.378 -10.252  1.00 95.38           N  
+ATOM   2133  NH1 ARG A 269      13.575 -18.148  -8.392  1.00 95.38           N  
+ATOM   2134  NH2 ARG A 269      13.811 -16.061  -8.887  1.00 95.38           N  
+ATOM   2135  CZ  ARG A 269      13.270 -17.188  -9.207  1.00 95.38           C  
+ATOM   2136  N   GLY A 270       6.365 -17.264 -11.626  1.00 93.88           N  
+ATOM   2137  CA  GLY A 270       5.106 -16.747 -11.118  1.00 93.88           C  
+ATOM   2138  C   GLY A 270       3.963 -17.741 -11.286  1.00 93.88           C  
+ATOM   2139  O   GLY A 270       3.978 -18.609 -12.160  1.00 93.88           O  
+ATOM   2140  N   LYS A 271       2.944 -17.618 -10.435  1.00 89.50           N  
+ATOM   2141  CA  LYS A 271       1.695 -18.362 -10.613  1.00 89.50           C  
+ATOM   2142  C   LYS A 271       0.862 -17.682 -11.698  1.00 89.50           C  
+ATOM   2143  CB  LYS A 271       0.910 -18.432  -9.300  1.00 89.50           C  
+ATOM   2144  O   LYS A 271       0.693 -16.465 -11.664  1.00 89.50           O  
+ATOM   2145  CG  LYS A 271       1.524 -19.369  -8.244  1.00 89.50           C  
+ATOM   2146  CD  LYS A 271       0.600 -19.343  -7.018  1.00 89.50           C  
+ATOM   2147  CE  LYS A 271       0.923 -20.362  -5.924  1.00 89.50           C  
+ATOM   2148  NZ  LYS A 271      -0.115 -20.274  -4.856  1.00 89.50           N  
+ATOM   2149  N   SER A 272       0.297 -18.466 -12.617  1.00 90.38           N  
+ATOM   2150  CA  SER A 272      -0.724 -17.943 -13.530  1.00 90.38           C  
+ATOM   2151  C   SER A 272      -1.941 -17.473 -12.735  1.00 90.38           C  
+ATOM   2152  CB  SER A 272      -1.149 -18.978 -14.576  1.00 90.38           C  
+ATOM   2153  O   SER A 272      -2.347 -18.122 -11.771  1.00 90.38           O  
+ATOM   2154  OG  SER A 272      -2.060 -18.363 -15.467  1.00 90.38           O  
+ATOM   2155  N   LEU A 273      -2.520 -16.351 -13.157  1.00 94.00           N  
+ATOM   2156  CA  LEU A 273      -3.787 -15.850 -12.626  1.00 94.00           C  
+ATOM   2157  C   LEU A 273      -4.978 -16.273 -13.493  1.00 94.00           C  
+ATOM   2158  CB  LEU A 273      -3.716 -14.326 -12.432  1.00 94.00           C  
+ATOM   2159  O   LEU A 273      -6.104 -15.897 -13.189  1.00 94.00           O  
+ATOM   2160  CG  LEU A 273      -2.599 -13.847 -11.494  1.00 94.00           C  
+ATOM   2161  CD1 LEU A 273      -2.638 -12.325 -11.430  1.00 94.00           C  
+ATOM   2162  CD2 LEU A 273      -2.758 -14.385 -10.079  1.00 94.00           C  
+ATOM   2163  N   GLU A 274      -4.768 -17.038 -14.565  1.00 94.50           N  
+ATOM   2164  CA  GLU A 274      -5.866 -17.532 -15.400  1.00 94.50           C  
+ATOM   2165  C   GLU A 274      -6.808 -18.425 -14.582  1.00 94.50           C  
+ATOM   2166  CB  GLU A 274      -5.328 -18.302 -16.610  1.00 94.50           C  
+ATOM   2167  O   GLU A 274      -6.381 -19.370 -13.919  1.00 94.50           O  
+ATOM   2168  CG  GLU A 274      -4.635 -17.384 -17.629  1.00 94.50           C  
+ATOM   2169  CD  GLU A 274      -4.001 -18.156 -18.797  1.00 94.50           C  
+ATOM   2170  OE1 GLU A 274      -3.300 -17.502 -19.600  1.00 94.50           O  
+ATOM   2171  OE2 GLU A 274      -4.174 -19.395 -18.863  1.00 94.50           O  
+ATOM   2172  N   GLY A 275      -8.105 -18.109 -14.607  1.00 91.38           N  
+ATOM   2173  CA  GLY A 275      -9.130 -18.845 -13.859  1.00 91.38           C  
+ATOM   2174  C   GLY A 275      -9.109 -18.635 -12.339  1.00 91.38           C  
+ATOM   2175  O   GLY A 275      -9.959 -19.192 -11.647  1.00 91.38           O  
+ATOM   2176  N   VAL A 276      -8.185 -17.828 -11.807  1.00 91.00           N  
+ATOM   2177  CA  VAL A 276      -8.175 -17.433 -10.392  1.00 91.00           C  
+ATOM   2178  C   VAL A 276      -9.214 -16.334 -10.170  1.00 91.00           C  
+ATOM   2179  CB  VAL A 276      -6.769 -16.984  -9.939  1.00 91.00           C  
+ATOM   2180  O   VAL A 276      -9.309 -15.396 -10.960  1.00 91.00           O  
+ATOM   2181  CG1 VAL A 276      -6.743 -16.573  -8.459  1.00 91.00           C  
+ATOM   2182  CG2 VAL A 276      -5.746 -18.114 -10.121  1.00 91.00           C  
+ATOM   2183  N   SER A 277      -9.972 -16.417  -9.074  1.00 93.25           N  
+ATOM   2184  CA  SER A 277     -10.877 -15.346  -8.638  1.00 93.25           C  
+ATOM   2185  C   SER A 277     -10.070 -14.175  -8.066  1.00 93.25           C  
+ATOM   2186  CB  SER A 277     -11.880 -15.898  -7.618  1.00 93.25           C  
+ATOM   2187  O   SER A 277      -9.901 -14.070  -6.853  1.00 93.25           O  
+ATOM   2188  OG  SER A 277     -12.807 -14.900  -7.249  1.00 93.25           O  
+ATOM   2189  N   PHE A 278      -9.520 -13.323  -8.929  1.00 96.56           N  
+ATOM   2190  CA  PHE A 278      -8.732 -12.159  -8.529  1.00 96.56           C  
+ATOM   2191  C   PHE A 278      -9.607 -10.892  -8.560  1.00 96.56           C  
+ATOM   2192  CB  PHE A 278      -7.500 -12.048  -9.435  1.00 96.56           C  
+ATOM   2193  O   PHE A 278     -10.375 -10.707  -9.504  1.00 96.56           O  
+ATOM   2194  CG  PHE A 278      -6.391 -11.166  -8.910  1.00 96.56           C  
+ATOM   2195  CD1 PHE A 278      -6.284  -9.831  -9.342  1.00 96.56           C  
+ATOM   2196  CD2 PHE A 278      -5.449 -11.687  -8.005  1.00 96.56           C  
+ATOM   2197  CE1 PHE A 278      -5.237  -9.022  -8.870  1.00 96.56           C  
+ATOM   2198  CE2 PHE A 278      -4.404 -10.875  -7.531  1.00 96.56           C  
+ATOM   2199  CZ  PHE A 278      -4.302  -9.541  -7.957  1.00 96.56           C  
+ATOM   2200  N   PRO A 279      -9.520 -10.003  -7.555  1.00 97.12           N  
+ATOM   2201  CA  PRO A 279     -10.315  -8.778  -7.534  1.00 97.12           C  
+ATOM   2202  C   PRO A 279      -9.957  -7.852  -8.702  1.00 97.12           C  
+ATOM   2203  CB  PRO A 279     -10.003  -8.121  -6.189  1.00 97.12           C  
+ATOM   2204  O   PRO A 279      -8.779  -7.709  -9.067  1.00 97.12           O  
+ATOM   2205  CG  PRO A 279      -8.605  -8.625  -5.885  1.00 97.12           C  
+ATOM   2206  CD  PRO A 279      -8.647 -10.057  -6.394  1.00 97.12           C  
+ATOM   2207  N   LYS A 280     -10.960  -7.139  -9.231  1.00 97.62           N  
+ATOM   2208  CA  LYS A 280     -10.724  -6.073 -10.212  1.00 97.62           C  
+ATOM   2209  C   LYS A 280      -9.691  -5.086  -9.675  1.00 97.62           C  
+ATOM   2210  CB  LYS A 280     -12.031  -5.380 -10.638  1.00 97.62           C  
+ATOM   2211  O   LYS A 280      -9.637  -4.801  -8.478  1.00 97.62           O  
+ATOM   2212  CG  LYS A 280     -12.725  -4.637  -9.489  1.00 97.62           C  
+ATOM   2213  CD  LYS A 280     -13.938  -3.838  -9.969  1.00 97.62           C  
+ATOM   2214  CE  LYS A 280     -14.431  -2.959  -8.816  1.00 97.62           C  
+ATOM   2215  NZ  LYS A 280     -15.634  -2.193  -9.203  1.00 97.62           N  
+ATOM   2216  N   SER A 281      -8.832  -4.579 -10.544  1.00 98.81           N  
+ATOM   2217  CA  SER A 281      -7.655  -3.821 -10.120  1.00 98.81           C  
+ATOM   2218  C   SER A 281      -7.588  -2.453 -10.790  1.00 98.81           C  
+ATOM   2219  CB  SER A 281      -6.396  -4.646 -10.396  1.00 98.81           C  
+ATOM   2220  O   SER A 281      -7.475  -2.362 -12.009  1.00 98.81           O  
+ATOM   2221  OG  SER A 281      -6.352  -5.795  -9.561  1.00 98.81           O  
+ATOM   2222  N   LEU A 282      -7.585  -1.386  -9.992  1.00 98.88           N  
+ATOM   2223  CA  LEU A 282      -7.156  -0.056 -10.412  1.00 98.88           C  
+ATOM   2224  C   LEU A 282      -5.635   0.044 -10.244  1.00 98.88           C  
+ATOM   2225  CB  LEU A 282      -7.916   1.009  -9.600  1.00 98.88           C  
+ATOM   2226  O   LEU A 282      -5.125   0.048  -9.124  1.00 98.88           O  
+ATOM   2227  CG  LEU A 282      -7.451   2.456  -9.857  1.00 98.88           C  
+ATOM   2228  CD1 LEU A 282      -7.630   2.889 -11.315  1.00 98.88           C  
+ATOM   2229  CD2 LEU A 282      -8.238   3.413  -8.965  1.00 98.88           C  
+ATOM   2230  N   VAL A 283      -4.906   0.137 -11.356  1.00 98.88           N  
+ATOM   2231  CA  VAL A 283      -3.445   0.298 -11.366  1.00 98.88           C  
+ATOM   2232  C   VAL A 283      -3.109   1.721 -11.785  1.00 98.88           C  
+ATOM   2233  CB  VAL A 283      -2.753  -0.742 -12.270  1.00 98.88           C  
+ATOM   2234  O   VAL A 283      -3.353   2.114 -12.925  1.00 98.88           O  
+ATOM   2235  CG1 VAL A 283      -1.226  -0.637 -12.149  1.00 98.88           C  
+ATOM   2236  CG2 VAL A 283      -3.147  -2.171 -11.879  1.00 98.88           C  
+ATOM   2237  N   VAL A 284      -2.539   2.500 -10.868  1.00 98.81           N  
+ATOM   2238  CA  VAL A 284      -2.117   3.882 -11.117  1.00 98.81           C  
+ATOM   2239  C   VAL A 284      -0.618   3.905 -11.386  1.00 98.81           C  
+ATOM   2240  CB  VAL A 284      -2.501   4.805  -9.951  1.00 98.81           C  
+ATOM   2241  O   VAL A 284       0.173   3.427 -10.574  1.00 98.81           O  
+ATOM   2242  CG1 VAL A 284      -2.102   6.257 -10.229  1.00 98.81           C  
+ATOM   2243  CG2 VAL A 284      -4.010   4.770  -9.675  1.00 98.81           C  
+ATOM   2244  N   VAL A 285      -0.230   4.470 -12.526  1.00 98.69           N  
+ATOM   2245  CA  VAL A 285       1.154   4.494 -13.014  1.00 98.69           C  
+ATOM   2246  C   VAL A 285       1.570   5.934 -13.270  1.00 98.69           C  
+ATOM   2247  CB  VAL A 285       1.288   3.633 -14.285  1.00 98.69           C  
+ATOM   2248  O   VAL A 285       0.946   6.633 -14.071  1.00 98.69           O  
+ATOM   2249  CG1 VAL A 285       2.692   3.691 -14.889  1.00 98.69           C  
+ATOM   2250  CG2 VAL A 285       0.943   2.175 -13.971  1.00 98.69           C  
+ATOM   2251  N   ALA A 286       2.646   6.379 -12.623  1.00 96.94           N  
+ATOM   2252  CA  ALA A 286       3.247   7.675 -12.919  1.00 96.94           C  
+ATOM   2253  C   ALA A 286       4.244   7.533 -14.079  1.00 96.94           C  
+ATOM   2254  CB  ALA A 286       3.866   8.252 -11.643  1.00 96.94           C  
+ATOM   2255  O   ALA A 286       5.165   6.721 -14.024  1.00 96.94           O  
+ATOM   2256  N   GLY A 287       4.078   8.330 -15.134  1.00 96.56           N  
+ATOM   2257  CA  GLY A 287       4.875   8.215 -16.358  1.00 96.56           C  
+ATOM   2258  C   GLY A 287       6.360   8.554 -16.194  1.00 96.56           C  
+ATOM   2259  O   GLY A 287       7.174   8.178 -17.040  1.00 96.56           O  
+ATOM   2260  N   LEU A 288       6.716   9.219 -15.093  1.00 95.56           N  
+ATOM   2261  CA  LEU A 288       8.085   9.568 -14.712  1.00 95.56           C  
+ATOM   2262  C   LEU A 288       8.594   8.732 -13.521  1.00 95.56           C  
+ATOM   2263  CB  LEU A 288       8.154  11.083 -14.463  1.00 95.56           C  
+ATOM   2264  O   LEU A 288       9.542   9.128 -12.841  1.00 95.56           O  
+ATOM   2265  CG  LEU A 288       7.662  11.957 -15.633  1.00 95.56           C  
+ATOM   2266  CD1 LEU A 288       7.614  13.418 -15.205  1.00 95.56           C  
+ATOM   2267  CD2 LEU A 288       8.568  11.837 -16.858  1.00 95.56           C  
+ATOM   2268  N   ASP A 289       7.960   7.588 -13.246  1.00 96.19           N  
+ATOM   2269  CA  ASP A 289       8.463   6.578 -12.313  1.00 96.19           C  
+ATOM   2270  C   ASP A 289       9.571   5.738 -12.967  1.00 96.19           C  
+ATOM   2271  CB  ASP A 289       7.302   5.701 -11.813  1.00 96.19           C  
+ATOM   2272  O   ASP A 289       9.437   5.263 -14.095  1.00 96.19           O  
+ATOM   2273  CG  ASP A 289       7.621   4.910 -10.542  1.00 96.19           C  
+ATOM   2274  OD1 ASP A 289       8.816   4.619 -10.298  1.00 96.19           O  
+ATOM   2275  OD2 ASP A 289       6.660   4.655  -9.774  1.00 96.19           O  
+ATOM   2276  N   LEU A 290      10.662   5.510 -12.235  1.00 96.75           N  
+ATOM   2277  CA  LEU A 290      11.785   4.679 -12.672  1.00 96.75           C  
+ATOM   2278  C   LEU A 290      11.338   3.254 -13.028  1.00 96.75           C  
+ATOM   2279  CB  LEU A 290      12.835   4.716 -11.547  1.00 96.75           C  
+ATOM   2280  O   LEU A 290      11.846   2.669 -13.983  1.00 96.75           O  
+ATOM   2281  CG  LEU A 290      14.120   3.920 -11.846  1.00 96.75           C  
+ATOM   2282  CD1 LEU A 290      15.306   4.616 -11.190  1.00 96.75           C  
+ATOM   2283  CD2 LEU A 290      14.076   2.504 -11.267  1.00 96.75           C  
+ATOM   2284  N   ILE A 291      10.362   2.712 -12.292  1.00 97.25           N  
+ATOM   2285  CA  ILE A 291       9.821   1.359 -12.504  1.00 97.25           C  
+ATOM   2286  C   ILE A 291       8.505   1.353 -13.302  1.00 97.25           C  
+ATOM   2287  CB  ILE A 291       9.709   0.589 -11.170  1.00 97.25           C  
+ATOM   2288  O   ILE A 291       7.783   0.356 -13.279  1.00 97.25           O  
+ATOM   2289  CG1 ILE A 291       8.811   1.345 -10.173  1.00 97.25           C  
+ATOM   2290  CG2 ILE A 291      11.099   0.329 -10.563  1.00 97.25           C  
+ATOM   2291  CD1 ILE A 291       8.269   0.445  -9.067  1.00 97.25           C  
+ATOM   2292  N   ARG A 292       8.183   2.445 -14.012  1.00 98.00           N  
+ATOM   2293  CA  ARG A 292       6.959   2.576 -14.822  1.00 98.00           C  
+ATOM   2294  C   ARG A 292       6.745   1.384 -15.748  1.00 98.00           C  
+ATOM   2295  CB  ARG A 292       7.046   3.861 -15.655  1.00 98.00           C  
+ATOM   2296  O   ARG A 292       5.648   0.838 -15.790  1.00 98.00           O  
+ATOM   2297  CG  ARG A 292       5.845   4.053 -16.602  1.00 98.00           C  
+ATOM   2298  CD  ARG A 292       6.172   5.043 -17.708  1.00 98.00           C  
+ATOM   2299  NE  ARG A 292       7.211   4.532 -18.616  1.00 98.00           N  
+ATOM   2300  NH1 ARG A 292       8.059   6.560 -19.302  1.00 98.00           N  
+ATOM   2301  NH2 ARG A 292       8.884   4.619 -20.091  1.00 98.00           N  
+ATOM   2302  CZ  ARG A 292       8.040   5.253 -19.334  1.00 98.00           C  
+ATOM   2303  N   ASP A 293       7.770   0.972 -16.488  1.00 98.75           N  
+ATOM   2304  CA  ASP A 293       7.613  -0.089 -17.486  1.00 98.75           C  
+ATOM   2305  C   ASP A 293       7.258  -1.436 -16.826  1.00 98.75           C  
+ATOM   2306  CB  ASP A 293       8.884  -0.181 -18.336  1.00 98.75           C  
+ATOM   2307  O   ASP A 293       6.437  -2.180 -17.358  1.00 98.75           O  
+ATOM   2308  CG  ASP A 293       9.152   1.037 -19.240  1.00 98.75           C  
+ATOM   2309  OD1 ASP A 293       8.295   1.953 -19.349  1.00 98.75           O  
+ATOM   2310  OD2 ASP A 293      10.260   1.095 -19.819  1.00 98.75           O  
+ATOM   2311  N   TRP A 294       7.762  -1.706 -15.615  1.00 98.56           N  
+ATOM   2312  CA  TRP A 294       7.362  -2.873 -14.819  1.00 98.56           C  
+ATOM   2313  C   TRP A 294       5.926  -2.778 -14.291  1.00 98.56           C  
+ATOM   2314  CB  TRP A 294       8.355  -3.086 -13.669  1.00 98.56           C  
+ATOM   2315  O   TRP A 294       5.224  -3.785 -14.226  1.00 98.56           O  
+ATOM   2316  CG  TRP A 294       9.675  -3.630 -14.107  1.00 98.56           C  
+ATOM   2317  CD1 TRP A 294      10.879  -3.020 -14.014  1.00 98.56           C  
+ATOM   2318  CD2 TRP A 294       9.920  -4.909 -14.763  1.00 98.56           C  
+ATOM   2319  CE2 TRP A 294      11.313  -5.012 -15.039  1.00 98.56           C  
+ATOM   2320  CE3 TRP A 294       9.095  -5.986 -15.158  1.00 98.56           C  
+ATOM   2321  NE1 TRP A 294      11.855  -3.839 -14.553  1.00 98.56           N  
+ATOM   2322  CH2 TRP A 294      11.008  -7.178 -16.089  1.00 98.56           C  
+ATOM   2323  CZ2 TRP A 294      11.851  -6.130 -15.687  1.00 98.56           C  
+ATOM   2324  CZ3 TRP A 294       9.633  -7.108 -15.815  1.00 98.56           C  
+ATOM   2325  N   GLN A 295       5.451  -1.577 -13.954  1.00 98.69           N  
+ATOM   2326  CA  GLN A 295       4.053  -1.350 -13.567  1.00 98.69           C  
+ATOM   2327  C   GLN A 295       3.096  -1.562 -14.751  1.00 98.69           C  
+ATOM   2328  CB  GLN A 295       3.888   0.075 -13.021  1.00 98.69           C  
+ATOM   2329  O   GLN A 295       2.040  -2.179 -14.597  1.00 98.69           O  
+ATOM   2330  CG  GLN A 295       4.664   0.330 -11.723  1.00 98.69           C  
+ATOM   2331  CD  GLN A 295       4.868   1.806 -11.386  1.00 98.69           C  
+ATOM   2332  NE2 GLN A 295       5.419   2.092 -10.230  1.00 98.69           N  
+ATOM   2333  OE1 GLN A 295       4.540   2.719 -12.125  1.00 98.69           O  
+ATOM   2334  N   LEU A 296       3.476  -1.089 -15.943  1.00 98.75           N  
+ATOM   2335  CA  LEU A 296       2.730  -1.318 -17.182  1.00 98.75           C  
+ATOM   2336  C   LEU A 296       2.728  -2.804 -17.563  1.00 98.75           C  
+ATOM   2337  CB  LEU A 296       3.327  -0.462 -18.314  1.00 98.75           C  
+ATOM   2338  O   LEU A 296       1.675  -3.344 -17.898  1.00 98.75           O  
+ATOM   2339  CG  LEU A 296       3.165   1.059 -18.139  1.00 98.75           C  
+ATOM   2340  CD1 LEU A 296       3.909   1.792 -19.257  1.00 98.75           C  
+ATOM   2341  CD2 LEU A 296       1.701   1.498 -18.181  1.00 98.75           C  
+ATOM   2342  N   ALA A 297       3.875  -3.481 -17.448  1.00 98.44           N  
+ATOM   2343  CA  ALA A 297       3.982  -4.921 -17.670  1.00 98.44           C  
+ATOM   2344  C   ALA A 297       3.099  -5.721 -16.699  1.00 98.44           C  
+ATOM   2345  CB  ALA A 297       5.453  -5.334 -17.550  1.00 98.44           C  
+ATOM   2346  O   ALA A 297       2.454  -6.679 -17.115  1.00 98.44           O  
+ATOM   2347  N   TYR A 298       3.005  -5.300 -15.434  1.00 98.62           N  
+ATOM   2348  CA  TYR A 298       2.090  -5.899 -14.463  1.00 98.62           C  
+ATOM   2349  C   TYR A 298       0.616  -5.748 -14.879  1.00 98.62           C  
+ATOM   2350  CB  TYR A 298       2.353  -5.288 -13.082  1.00 98.62           C  
+ATOM   2351  O   TYR A 298      -0.131  -6.726 -14.861  1.00 98.62           O  
+ATOM   2352  CG  TYR A 298       1.328  -5.699 -12.051  1.00 98.62           C  
+ATOM   2353  CD1 TYR A 298       0.189  -4.901 -11.833  1.00 98.62           C  
+ATOM   2354  CD2 TYR A 298       1.489  -6.907 -11.352  1.00 98.62           C  
+ATOM   2355  CE1 TYR A 298      -0.800  -5.322 -10.928  1.00 98.62           C  
+ATOM   2356  CE2 TYR A 298       0.504  -7.328 -10.443  1.00 98.62           C  
+ATOM   2357  OH  TYR A 298      -1.621  -6.937  -9.368  1.00 98.62           O  
+ATOM   2358  CZ  TYR A 298      -0.647  -6.538 -10.230  1.00 98.62           C  
+ATOM   2359  N   ALA A 299       0.191  -4.548 -15.292  1.00 98.69           N  
+ATOM   2360  CA  ALA A 299      -1.180  -4.328 -15.759  1.00 98.69           C  
+ATOM   2361  C   ALA A 299      -1.503  -5.166 -17.012  1.00 98.69           C  
+ATOM   2362  CB  ALA A 299      -1.383  -2.829 -16.007  1.00 98.69           C  
+ATOM   2363  O   ALA A 299      -2.577  -5.760 -17.103  1.00 98.69           O  
+ATOM   2364  N   GLU A 300      -0.559  -5.272 -17.949  1.00 98.38           N  
+ATOM   2365  CA  GLU A 300      -0.698  -6.134 -19.125  1.00 98.38           C  
+ATOM   2366  C   GLU A 300      -0.724  -7.625 -18.748  1.00 98.38           C  
+ATOM   2367  CB  GLU A 300       0.437  -5.811 -20.109  1.00 98.38           C  
+ATOM   2368  O   GLU A 300      -1.476  -8.395 -19.341  1.00 98.38           O  
+ATOM   2369  CG  GLU A 300       0.364  -6.586 -21.434  1.00 98.38           C  
+ATOM   2370  CD  GLU A 300      -0.915  -6.350 -22.261  1.00 98.38           C  
+ATOM   2371  OE1 GLU A 300      -1.167  -7.200 -23.145  1.00 98.38           O  
+ATOM   2372  OE2 GLU A 300      -1.620  -5.332 -22.061  1.00 98.38           O  
+ATOM   2373  N   GLY A 301       0.044  -8.039 -17.736  1.00 98.06           N  
+ATOM   2374  CA  GLY A 301       0.012  -9.397 -17.190  1.00 98.06           C  
+ATOM   2375  C   GLY A 301      -1.358  -9.769 -16.619  1.00 98.06           C  
+ATOM   2376  O   GLY A 301      -1.890 -10.824 -16.961  1.00 98.06           O  
+ATOM   2377  N   LEU A 302      -1.979  -8.874 -15.836  1.00 98.44           N  
+ATOM   2378  CA  LEU A 302      -3.354  -9.059 -15.347  1.00 98.44           C  
+ATOM   2379  C   LEU A 302      -4.341  -9.224 -16.508  1.00 98.44           C  
+ATOM   2380  CB  LEU A 302      -3.786  -7.861 -14.477  1.00 98.44           C  
+ATOM   2381  O   LEU A 302      -5.133 -10.166 -16.521  1.00 98.44           O  
+ATOM   2382  CG  LEU A 302      -3.171  -7.781 -13.072  1.00 98.44           C  
+ATOM   2383  CD1 LEU A 302      -3.648  -6.485 -12.412  1.00 98.44           C  
+ATOM   2384  CD2 LEU A 302      -3.618  -8.935 -12.175  1.00 98.44           C  
+ATOM   2385  N   LYS A 303      -4.251  -8.345 -17.512  1.00 98.12           N  
+ATOM   2386  CA  LYS A 303      -5.118  -8.387 -18.694  1.00 98.12           C  
+ATOM   2387  C   LYS A 303      -4.964  -9.693 -19.478  1.00 98.12           C  
+ATOM   2388  CB  LYS A 303      -4.800  -7.170 -19.565  1.00 98.12           C  
+ATOM   2389  O   LYS A 303      -5.965 -10.290 -19.865  1.00 98.12           O  
+ATOM   2390  CG  LYS A 303      -5.742  -7.062 -20.768  1.00 98.12           C  
+ATOM   2391  CD  LYS A 303      -5.316  -5.859 -21.602  1.00 98.12           C  
+ATOM   2392  CE  LYS A 303      -6.159  -5.766 -22.870  1.00 98.12           C  
+ATOM   2393  NZ  LYS A 303      -5.594  -4.718 -23.746  1.00 98.12           N  
+ATOM   2394  N   LYS A 304      -3.728 -10.150 -19.705  1.00 97.56           N  
+ATOM   2395  CA  LYS A 304      -3.441 -11.418 -20.400  1.00 97.56           C  
+ATOM   2396  C   LYS A 304      -3.979 -12.627 -19.644  1.00 97.56           C  
+ATOM   2397  CB  LYS A 304      -1.934 -11.571 -20.620  1.00 97.56           C  
+ATOM   2398  O   LYS A 304      -4.468 -13.548 -20.281  1.00 97.56           O  
+ATOM   2399  CG  LYS A 304      -1.456 -10.699 -21.784  1.00 97.56           C  
+ATOM   2400  CD  LYS A 304       0.062 -10.807 -21.915  1.00 97.56           C  
+ATOM   2401  CE  LYS A 304       0.517  -9.967 -23.106  1.00 97.56           C  
+ATOM   2402  NZ  LYS A 304       1.994  -9.907 -23.177  1.00 97.56           N  
+ATOM   2403  N   ALA A 305      -3.956 -12.584 -18.314  1.00 96.94           N  
+ATOM   2404  CA  ALA A 305      -4.539 -13.619 -17.468  1.00 96.94           C  
+ATOM   2405  C   ALA A 305      -6.081 -13.562 -17.376  1.00 96.94           C  
+ATOM   2406  CB  ALA A 305      -3.869 -13.535 -16.096  1.00 96.94           C  
+ATOM   2407  O   ALA A 305      -6.676 -14.309 -16.601  1.00 96.94           O  
+ATOM   2408  N   GLY A 306      -6.736 -12.673 -18.133  1.00 97.56           N  
+ATOM   2409  CA  GLY A 306      -8.193 -12.528 -18.156  1.00 97.56           C  
+ATOM   2410  C   GLY A 306      -8.779 -11.783 -16.955  1.00 97.56           C  
+ATOM   2411  O   GLY A 306      -9.986 -11.854 -16.742  1.00 97.56           O  
+ATOM   2412  N   GLN A 307      -7.952 -11.080 -16.175  1.00 97.88           N  
+ATOM   2413  CA  GLN A 307      -8.393 -10.339 -14.993  1.00 97.88           C  
+ATOM   2414  C   GLN A 307      -8.855  -8.922 -15.347  1.00 97.88           C  
+ATOM   2415  CB  GLN A 307      -7.285 -10.327 -13.922  1.00 97.88           C  
+ATOM   2416  O   GLN A 307      -8.332  -8.284 -16.267  1.00 97.88           O  
+ATOM   2417  CG  GLN A 307      -6.847 -11.742 -13.502  1.00 97.88           C  
+ATOM   2418  CD  GLN A 307      -7.997 -12.566 -12.929  1.00 97.88           C  
+ATOM   2419  NE2 GLN A 307      -7.920 -13.870 -12.920  1.00 97.88           N  
+ATOM   2420  OE1 GLN A 307      -8.984 -12.050 -12.439  1.00 97.88           O  
+ATOM   2421  N   GLU A 308      -9.821  -8.399 -14.590  1.00 98.06           N  
+ATOM   2422  CA  GLU A 308     -10.324  -7.040 -14.786  1.00 98.06           C  
+ATOM   2423  C   GLU A 308      -9.312  -6.010 -14.270  1.00 98.06           C  
+ATOM   2424  CB  GLU A 308     -11.703  -6.874 -14.137  1.00 98.06           C  
+ATOM   2425  O   GLU A 308      -9.007  -5.935 -13.077  1.00 98.06           O  
+ATOM   2426  CG  GLU A 308     -12.326  -5.520 -14.517  1.00 98.06           C  
+ATOM   2427  CD  GLU A 308     -13.691  -5.280 -13.859  1.00 98.06           C  
+ATOM   2428  OE1 GLU A 308     -13.979  -4.093 -13.576  1.00 98.06           O  
+ATOM   2429  OE2 GLU A 308     -14.424  -6.266 -13.643  1.00 98.06           O  
+ATOM   2430  N   VAL A 309      -8.789  -5.184 -15.176  1.00 98.62           N  
+ATOM   2431  CA  VAL A 309      -7.804  -4.153 -14.847  1.00 98.62           C  
+ATOM   2432  C   VAL A 309      -8.173  -2.818 -15.481  1.00 98.62           C  
+ATOM   2433  CB  VAL A 309      -6.373  -4.624 -15.175  1.00 98.62           C  
+ATOM   2434  O   VAL A 309      -8.324  -2.698 -16.698  1.00 98.62           O  
+ATOM   2435  CG1 VAL A 309      -6.136  -4.950 -16.654  1.00 98.62           C  
+ATOM   2436  CG2 VAL A 309      -5.330  -3.600 -14.714  1.00 98.62           C  
+ATOM   2437  N   LYS A 310      -8.269  -1.784 -14.644  1.00 98.56           N  
+ATOM   2438  CA  LYS A 310      -8.337  -0.386 -15.061  1.00 98.56           C  
+ATOM   2439  C   LYS A 310      -6.963   0.245 -14.866  1.00 98.56           C  
+ATOM   2440  CB  LYS A 310      -9.460   0.332 -14.298  1.00 98.56           C  
+ATOM   2441  O   LYS A 310      -6.499   0.419 -13.743  1.00 98.56           O  
+ATOM   2442  CG  LYS A 310      -9.674   1.749 -14.853  1.00 98.56           C  
+ATOM   2443  CD  LYS A 310     -10.932   2.404 -14.270  1.00 98.56           C  
+ATOM   2444  CE  LYS A 310     -11.115   3.799 -14.880  1.00 98.56           C  
+ATOM   2445  NZ  LYS A 310     -12.321   4.475 -14.353  1.00 98.56           N  
+ATOM   2446  N   LEU A 311      -6.302   0.584 -15.969  1.00 98.69           N  
+ATOM   2447  CA  LEU A 311      -4.999   1.246 -15.955  1.00 98.69           C  
+ATOM   2448  C   LEU A 311      -5.174   2.766 -16.037  1.00 98.69           C  
+ATOM   2449  CB  LEU A 311      -4.141   0.687 -17.103  1.00 98.69           C  
+ATOM   2450  O   LEU A 311      -5.712   3.280 -17.016  1.00 98.69           O  
+ATOM   2451  CG  LEU A 311      -2.771   1.373 -17.264  1.00 98.69           C  
+ATOM   2452  CD1 LEU A 311      -1.866   1.183 -16.048  1.00 98.69           C  
+ATOM   2453  CD2 LEU A 311      -2.057   0.804 -18.486  1.00 98.69           C  
+ATOM   2454  N   MET A 312      -4.654   3.485 -15.045  1.00 98.44           N  
+ATOM   2455  CA  MET A 312      -4.547   4.941 -15.052  1.00 98.44           C  
+ATOM   2456  C   MET A 312      -3.078   5.344 -15.205  1.00 98.44           C  
+ATOM   2457  CB  MET A 312      -5.208   5.513 -13.793  1.00 98.44           C  
+ATOM   2458  O   MET A 312      -2.350   5.498 -14.225  1.00 98.44           O  
+ATOM   2459  CG  MET A 312      -5.371   7.031 -13.899  1.00 98.44           C  
+ATOM   2460  SD  MET A 312      -6.260   7.726 -12.488  1.00 98.44           S  
+ATOM   2461  CE  MET A 312      -6.875   9.268 -13.203  1.00 98.44           C  
+ATOM   2462  N   HIS A 313      -2.634   5.500 -16.453  1.00 98.19           N  
+ATOM   2463  CA  HIS A 313      -1.277   5.943 -16.770  1.00 98.19           C  
+ATOM   2464  C   HIS A 313      -1.224   7.469 -16.926  1.00 98.19           C  
+ATOM   2465  CB  HIS A 313      -0.764   5.200 -18.009  1.00 98.19           C  
+ATOM   2466  O   HIS A 313      -1.841   8.042 -17.822  1.00 98.19           O  
+ATOM   2467  CG  HIS A 313       0.645   5.570 -18.414  1.00 98.19           C  
+ATOM   2468  CD2 HIS A 313       1.666   5.995 -17.603  1.00 98.19           C  
+ATOM   2469  ND1 HIS A 313       1.141   5.545 -19.696  1.00 98.19           N  
+ATOM   2470  CE1 HIS A 313       2.419   5.951 -19.661  1.00 98.19           C  
+ATOM   2471  NE2 HIS A 313       2.785   6.242 -18.402  1.00 98.19           N  
+ATOM   2472  N   LEU A 314      -0.479   8.132 -16.044  1.00 97.56           N  
+ATOM   2473  CA  LEU A 314      -0.350   9.585 -15.988  1.00 97.56           C  
+ATOM   2474  C   LEU A 314       1.043   9.991 -16.468  1.00 97.56           C  
+ATOM   2475  CB  LEU A 314      -0.643  10.050 -14.551  1.00 97.56           C  
+ATOM   2476  O   LEU A 314       1.963  10.131 -15.665  1.00 97.56           O  
+ATOM   2477  CG  LEU A 314      -2.066   9.719 -14.074  1.00 97.56           C  
+ATOM   2478  CD1 LEU A 314      -2.166   9.927 -12.573  1.00 97.56           C  
+ATOM   2479  CD2 LEU A 314      -3.121  10.596 -14.752  1.00 97.56           C  
+ATOM   2480  N   GLU A 315       1.194  10.193 -17.778  1.00 94.38           N  
+ATOM   2481  CA  GLU A 315       2.490  10.363 -18.465  1.00 94.38           C  
+ATOM   2482  C   GLU A 315       3.422  11.416 -17.839  1.00 94.38           C  
+ATOM   2483  CB  GLU A 315       2.242  10.774 -19.922  1.00 94.38           C  
+ATOM   2484  O   GLU A 315       4.639  11.251 -17.840  1.00 94.38           O  
+ATOM   2485  CG  GLU A 315       1.570   9.672 -20.752  1.00 94.38           C  
+ATOM   2486  CD  GLU A 315       1.369  10.086 -22.218  1.00 94.38           C  
+ATOM   2487  OE1 GLU A 315       0.961   9.201 -23.000  1.00 94.38           O  
+ATOM   2488  OE2 GLU A 315       1.604  11.277 -22.537  1.00 94.38           O  
+ATOM   2489  N   LYS A 316       2.855  12.495 -17.285  1.00 92.62           N  
+ATOM   2490  CA  LYS A 316       3.602  13.620 -16.694  1.00 92.62           C  
+ATOM   2491  C   LYS A 316       3.696  13.576 -15.165  1.00 92.62           C  
+ATOM   2492  CB  LYS A 316       3.010  14.951 -17.183  1.00 92.62           C  
+ATOM   2493  O   LYS A 316       4.306  14.461 -14.571  1.00 92.62           O  
+ATOM   2494  CG  LYS A 316       3.123  15.106 -18.706  1.00 92.62           C  
+ATOM   2495  CD  LYS A 316       2.616  16.480 -19.152  1.00 92.62           C  
+ATOM   2496  CE  LYS A 316       2.752  16.589 -20.673  1.00 92.62           C  
+ATOM   2497  NZ  LYS A 316       2.283  17.903 -21.175  1.00 92.62           N  
+ATOM   2498  N   ALA A 317       3.101  12.576 -14.519  1.00 94.06           N  
+ATOM   2499  CA  ALA A 317       3.159  12.439 -13.071  1.00 94.06           C  
+ATOM   2500  C   ALA A 317       4.516  11.876 -12.627  1.00 94.06           C  
+ATOM   2501  CB  ALA A 317       1.988  11.581 -12.587  1.00 94.06           C  
+ATOM   2502  O   ALA A 317       5.074  10.986 -13.271  1.00 94.06           O  
+ATOM   2503  N   THR A 318       5.017  12.362 -11.491  1.00 92.25           N  
+ATOM   2504  CA  THR A 318       6.223  11.857 -10.816  1.00 92.25           C  
+ATOM   2505  C   THR A 318       5.870  10.993  -9.614  1.00 92.25           C  
+ATOM   2506  CB  THR A 318       7.127  13.012 -10.372  1.00 92.25           C  
+ATOM   2507  O   THR A 318       4.757  11.061  -9.103  1.00 92.25           O  
+ATOM   2508  CG2 THR A 318       7.622  13.791 -11.588  1.00 92.25           C  
+ATOM   2509  OG1 THR A 318       6.425  13.895  -9.522  1.00 92.25           O  
+ATOM   2510  N   VAL A 319       6.813  10.180  -9.134  1.00 90.31           N  
+ATOM   2511  CA  VAL A 319       6.617   9.343  -7.936  1.00 90.31           C  
+ATOM   2512  C   VAL A 319       6.104  10.182  -6.759  1.00 90.31           C  
+ATOM   2513  CB  VAL A 319       7.924   8.625  -7.543  1.00 90.31           C  
+ATOM   2514  O   VAL A 319       6.616  11.267  -6.495  1.00 90.31           O  
+ATOM   2515  CG1 VAL A 319       7.711   7.657  -6.370  1.00 90.31           C  
+ATOM   2516  CG2 VAL A 319       8.475   7.816  -8.722  1.00 90.31           C  
+ATOM   2517  N   GLY A 320       5.091   9.681  -6.046  1.00 88.12           N  
+ATOM   2518  CA  GLY A 320       4.547  10.329  -4.849  1.00 88.12           C  
+ATOM   2519  C   GLY A 320       3.619  11.522  -5.108  1.00 88.12           C  
+ATOM   2520  O   GLY A 320       3.239  12.201  -4.153  1.00 88.12           O  
+ATOM   2521  N   PHE A 321       3.208  11.780  -6.358  1.00 93.50           N  
+ATOM   2522  CA  PHE A 321       2.285  12.875  -6.697  1.00 93.50           C  
+ATOM   2523  C   PHE A 321       0.958  12.842  -5.913  1.00 93.50           C  
+ATOM   2524  CB  PHE A 321       1.994  12.862  -8.205  1.00 93.50           C  
+ATOM   2525  O   PHE A 321       0.360  13.893  -5.702  1.00 93.50           O  
+ATOM   2526  CG  PHE A 321       1.085  11.732  -8.655  1.00 93.50           C  
+ATOM   2527  CD1 PHE A 321       1.609  10.470  -8.995  1.00 93.50           C  
+ATOM   2528  CD2 PHE A 321      -0.306  11.931  -8.692  1.00 93.50           C  
+ATOM   2529  CE1 PHE A 321       0.771   9.435  -9.422  1.00 93.50           C  
+ATOM   2530  CE2 PHE A 321      -1.153  10.884  -9.089  1.00 93.50           C  
+ATOM   2531  CZ  PHE A 321      -0.607   9.646  -9.471  1.00 93.50           C  
+ATOM   2532  N   TYR A 322       0.517  11.660  -5.465  1.00 93.94           N  
+ATOM   2533  CA  TYR A 322      -0.705  11.436  -4.681  1.00 93.94           C  
+ATOM   2534  C   TYR A 322      -0.544  11.706  -3.169  1.00 93.94           C  
+ATOM   2535  CB  TYR A 322      -1.225  10.016  -4.947  1.00 93.94           C  
+ATOM   2536  O   TYR A 322      -1.528  11.676  -2.428  1.00 93.94           O  
+ATOM   2537  CG  TYR A 322      -0.170   8.934  -4.816  1.00 93.94           C  
+ATOM   2538  CD1 TYR A 322       0.427   8.413  -5.978  1.00 93.94           C  
+ATOM   2539  CD2 TYR A 322       0.239   8.474  -3.549  1.00 93.94           C  
+ATOM   2540  CE1 TYR A 322       1.455   7.460  -5.876  1.00 93.94           C  
+ATOM   2541  CE2 TYR A 322       1.264   7.511  -3.444  1.00 93.94           C  
+ATOM   2542  OH  TYR A 322       2.917   6.135  -4.518  1.00 93.94           O  
+ATOM   2543  CZ  TYR A 322       1.883   7.011  -4.610  1.00 93.94           C  
+ATOM   2544  N   LEU A 323       0.671  11.982  -2.678  1.00 91.56           N  
+ATOM   2545  CA  LEU A 323       0.905  12.250  -1.252  1.00 91.56           C  
+ATOM   2546  C   LEU A 323       0.413  13.647  -0.839  1.00 91.56           C  
+ATOM   2547  CB  LEU A 323       2.399  12.060  -0.924  1.00 91.56           C  
+ATOM   2548  O   LEU A 323      -0.091  13.822   0.274  1.00 91.56           O  
+ATOM   2549  CG  LEU A 323       2.934  10.627  -1.133  1.00 91.56           C  
+ATOM   2550  CD1 LEU A 323       4.440  10.603  -0.868  1.00 91.56           C  
+ATOM   2551  CD2 LEU A 323       2.266   9.615  -0.199  1.00 91.56           C  
+ATOM   2552  N   LEU A 324       0.508  14.633  -1.740  1.00 90.94           N  
+ATOM   2553  CA  LEU A 324       0.133  16.027  -1.486  1.00 90.94           C  
+ATOM   2554  C   LEU A 324      -0.942  16.519  -2.475  1.00 90.94           C  
+ATOM   2555  CB  LEU A 324       1.378  16.930  -1.557  1.00 90.94           C  
+ATOM   2556  O   LEU A 324      -0.789  16.306  -3.676  1.00 90.94           O  
+ATOM   2557  CG  LEU A 324       2.471  16.647  -0.508  1.00 90.94           C  
+ATOM   2558  CD1 LEU A 324       3.623  17.632  -0.715  1.00 90.94           C  
+ATOM   2559  CD2 LEU A 324       1.966  16.813   0.927  1.00 90.94           C  
+ATOM   2560  N   PRO A 325      -1.981  17.240  -2.005  1.00 91.56           N  
+ATOM   2561  CA  PRO A 325      -3.067  17.763  -2.836  1.00 91.56           C  
+ATOM   2562  C   PRO A 325      -2.651  19.036  -3.587  1.00 91.56           C  
+ATOM   2563  CB  PRO A 325      -4.227  17.996  -1.856  1.00 91.56           C  
+ATOM   2564  O   PRO A 325      -3.161  20.119  -3.316  1.00 91.56           O  
+ATOM   2565  CG  PRO A 325      -3.503  18.431  -0.587  1.00 91.56           C  
+ATOM   2566  CD  PRO A 325      -2.242  17.568  -0.607  1.00 91.56           C  
+ATOM   2567  N   ASN A 326      -1.644  18.948  -4.458  1.00 90.25           N  
+ATOM   2568  CA  ASN A 326      -1.006  20.131  -5.051  1.00 90.25           C  
+ATOM   2569  C   ASN A 326      -0.832  20.079  -6.576  1.00 90.25           C  
+ATOM   2570  CB  ASN A 326       0.309  20.412  -4.304  1.00 90.25           C  
+ATOM   2571  O   ASN A 326      -0.081  20.879  -7.131  1.00 90.25           O  
+ATOM   2572  CG  ASN A 326       1.398  19.377  -4.531  1.00 90.25           C  
+ATOM   2573  ND2 ASN A 326       2.482  19.497  -3.801  1.00 90.25           N  
+ATOM   2574  OD1 ASN A 326       1.316  18.461  -5.336  1.00 90.25           O  
+ATOM   2575  N   ASN A 327      -1.486  19.136  -7.255  1.00 89.62           N  
+ATOM   2576  CA  ASN A 327      -1.446  19.008  -8.710  1.00 89.62           C  
+ATOM   2577  C   ASN A 327      -2.709  18.301  -9.238  1.00 89.62           C  
+ATOM   2578  CB  ASN A 327      -0.145  18.282  -9.107  1.00 89.62           C  
+ATOM   2579  O   ASN A 327      -3.399  17.609  -8.496  1.00 89.62           O  
+ATOM   2580  CG  ASN A 327      -0.079  16.869  -8.564  1.00 89.62           C  
+ATOM   2581  ND2 ASN A 327       0.659  16.604  -7.512  1.00 89.62           N  
+ATOM   2582  OD1 ASN A 327      -0.737  15.988  -9.076  1.00 89.62           O  
+ATOM   2583  N   ASN A 328      -3.009  18.439 -10.532  1.00 93.25           N  
+ATOM   2584  CA  ASN A 328      -4.204  17.819 -11.122  1.00 93.25           C  
+ATOM   2585  C   ASN A 328      -4.131  16.284 -11.155  1.00 93.25           C  
+ATOM   2586  CB  ASN A 328      -4.435  18.388 -12.532  1.00 93.25           C  
+ATOM   2587  O   ASN A 328      -5.163  15.630 -11.053  1.00 93.25           O  
+ATOM   2588  CG  ASN A 328      -4.882  19.839 -12.522  1.00 93.25           C  
+ATOM   2589  ND2 ASN A 328      -4.763  20.529 -13.630  1.00 93.25           N  
+ATOM   2590  OD1 ASN A 328      -5.318  20.385 -11.528  1.00 93.25           O  
+ATOM   2591  N   HIS A 329      -2.932  15.692 -11.253  1.00 95.25           N  
+ATOM   2592  CA  HIS A 329      -2.781  14.233 -11.208  1.00 95.25           C  
+ATOM   2593  C   HIS A 329      -3.297  13.662  -9.885  1.00 95.25           C  
+ATOM   2594  CB  HIS A 329      -1.315  13.830 -11.445  1.00 95.25           C  
+ATOM   2595  O   HIS A 329      -3.992  12.650  -9.891  1.00 95.25           O  
+ATOM   2596  CG  HIS A 329      -0.779  14.255 -12.782  1.00 95.25           C  
+ATOM   2597  CD2 HIS A 329       0.390  14.924 -13.028  1.00 95.25           C  
+ATOM   2598  ND1 HIS A 329      -1.370  14.005 -13.997  1.00 95.25           N  
+ATOM   2599  CE1 HIS A 329      -0.580  14.509 -14.955  1.00 95.25           C  
+ATOM   2600  NE2 HIS A 329       0.503  15.085 -14.414  1.00 95.25           N  
+ATOM   2601  N   PHE A 330      -3.016  14.344  -8.772  1.00 96.06           N  
+ATOM   2602  CA  PHE A 330      -3.548  14.006  -7.460  1.00 96.06           C  
+ATOM   2603  C   PHE A 330      -5.079  13.968  -7.479  1.00 96.06           C  
+ATOM   2604  CB  PHE A 330      -3.021  15.014  -6.428  1.00 96.06           C  
+ATOM   2605  O   PHE A 330      -5.654  12.958  -7.090  1.00 96.06           O  
+ATOM   2606  CG  PHE A 330      -3.701  14.917  -5.087  1.00 96.06           C  
+ATOM   2607  CD1 PHE A 330      -4.952  15.530  -4.882  1.00 96.06           C  
+ATOM   2608  CD2 PHE A 330      -3.076  14.233  -4.038  1.00 96.06           C  
+ATOM   2609  CE1 PHE A 330      -5.607  15.397  -3.655  1.00 96.06           C  
+ATOM   2610  CE2 PHE A 330      -3.714  14.121  -2.800  1.00 96.06           C  
+ATOM   2611  CZ  PHE A 330      -4.983  14.694  -2.620  1.00 96.06           C  
+ATOM   2612  N   HIS A 331      -5.733  15.037  -7.944  1.00 96.69           N  
+ATOM   2613  CA  HIS A 331      -7.196  15.130  -7.904  1.00 96.69           C  
+ATOM   2614  C   HIS A 331      -7.841  14.063  -8.786  1.00 96.69           C  
+ATOM   2615  CB  HIS A 331      -7.649  16.546  -8.278  1.00 96.69           C  
+ATOM   2616  O   HIS A 331      -8.695  13.328  -8.307  1.00 96.69           O  
+ATOM   2617  CG  HIS A 331      -7.305  17.548  -7.206  1.00 96.69           C  
+ATOM   2618  CD2 HIS A 331      -6.347  18.524  -7.258  1.00 96.69           C  
+ATOM   2619  ND1 HIS A 331      -7.888  17.615  -5.963  1.00 96.69           N  
+ATOM   2620  CE1 HIS A 331      -7.294  18.606  -5.279  1.00 96.69           C  
+ATOM   2621  NE2 HIS A 331      -6.333  19.179  -6.020  1.00 96.69           N  
+ATOM   2622  N   ASN A 332      -7.325  13.875 -10.003  1.00 97.38           N  
+ATOM   2623  CA  ASN A 332      -7.815  12.846 -10.916  1.00 97.38           C  
+ATOM   2624  C   ASN A 332      -7.741  11.440 -10.298  1.00 97.38           C  
+ATOM   2625  CB  ASN A 332      -6.995  12.896 -12.217  1.00 97.38           C  
+ATOM   2626  O   ASN A 332      -8.672  10.657 -10.448  1.00 97.38           O  
+ATOM   2627  CG  ASN A 332      -7.165  14.165 -13.033  1.00 97.38           C  
+ATOM   2628  ND2 ASN A 332      -6.486  14.252 -14.153  1.00 97.38           N  
+ATOM   2629  OD1 ASN A 332      -7.893  15.087 -12.725  1.00 97.38           O  
+ATOM   2630  N   VAL A 333      -6.649  11.109  -9.599  1.00 98.31           N  
+ATOM   2631  CA  VAL A 333      -6.508   9.801  -8.937  1.00 98.31           C  
+ATOM   2632  C   VAL A 333      -7.427   9.670  -7.734  1.00 98.31           C  
+ATOM   2633  CB  VAL A 333      -5.050   9.543  -8.531  1.00 98.31           C  
+ATOM   2634  O   VAL A 333      -7.991   8.604  -7.529  1.00 98.31           O  
+ATOM   2635  CG1 VAL A 333      -4.847   8.351  -7.587  1.00 98.31           C  
+ATOM   2636  CG2 VAL A 333      -4.270   9.248  -9.803  1.00 98.31           C  
+ATOM   2637  N   MET A 334      -7.593  10.724  -6.938  1.00 98.12           N  
+ATOM   2638  CA  MET A 334      -8.494  10.692  -5.783  1.00 98.12           C  
+ATOM   2639  C   MET A 334      -9.958  10.524  -6.208  1.00 98.12           C  
+ATOM   2640  CB  MET A 334      -8.297  11.960  -4.943  1.00 98.12           C  
+ATOM   2641  O   MET A 334     -10.677   9.735  -5.593  1.00 98.12           O  
+ATOM   2642  CG  MET A 334      -6.930  11.984  -4.242  1.00 98.12           C  
+ATOM   2643  SD  MET A 334      -6.587  10.634  -3.076  1.00 98.12           S  
+ATOM   2644  CE  MET A 334      -4.778  10.700  -3.065  1.00 98.12           C  
+ATOM   2645  N   ASP A 335     -10.373  11.201  -7.280  1.00 97.81           N  
+ATOM   2646  CA  ASP A 335     -11.709  11.054  -7.864  1.00 97.81           C  
+ATOM   2647  C   ASP A 335     -11.910   9.640  -8.426  1.00 97.81           C  
+ATOM   2648  CB  ASP A 335     -11.919  12.108  -8.967  1.00 97.81           C  
+ATOM   2649  O   ASP A 335     -12.942   9.014  -8.188  1.00 97.81           O  
+ATOM   2650  CG  ASP A 335     -11.985  13.554  -8.453  1.00 97.81           C  
+ATOM   2651  OD1 ASP A 335     -12.199  13.756  -7.234  1.00 97.81           O  
+ATOM   2652  OD2 ASP A 335     -11.829  14.465  -9.297  1.00 97.81           O  
+ATOM   2653  N   GLU A 336     -10.897   9.095  -9.104  1.00 98.19           N  
+ATOM   2654  CA  GLU A 336     -10.933   7.736  -9.647  1.00 98.19           C  
+ATOM   2655  C   GLU A 336     -10.983   6.667  -8.544  1.00 98.19           C  
+ATOM   2656  CB  GLU A 336      -9.725   7.558 -10.578  1.00 98.19           C  
+ATOM   2657  O   GLU A 336     -11.762   5.725  -8.640  1.00 98.19           O  
+ATOM   2658  CG  GLU A 336      -9.685   6.205 -11.287  1.00 98.19           C  
+ATOM   2659  CD  GLU A 336     -10.942   5.927 -12.110  1.00 98.19           C  
+ATOM   2660  OE1 GLU A 336     -11.542   4.848 -11.916  1.00 98.19           O  
+ATOM   2661  OE2 GLU A 336     -11.267   6.682 -13.058  1.00 98.19           O  
+ATOM   2662  N   ILE A 337     -10.222   6.830  -7.458  1.00 98.69           N  
+ATOM   2663  CA  ILE A 337     -10.295   5.959  -6.274  1.00 98.69           C  
+ATOM   2664  C   ILE A 337     -11.704   5.988  -5.672  1.00 98.69           C  
+ATOM   2665  CB  ILE A 337      -9.231   6.392  -5.240  1.00 98.69           C  
+ATOM   2666  O   ILE A 337     -12.262   4.934  -5.371  1.00 98.69           O  
+ATOM   2667  CG1 ILE A 337      -7.825   5.973  -5.720  1.00 98.69           C  
+ATOM   2668  CG2 ILE A 337      -9.500   5.831  -3.831  1.00 98.69           C  
+ATOM   2669  CD1 ILE A 337      -6.700   6.605  -4.888  1.00 98.69           C  
+ATOM   2670  N   SER A 338     -12.285   7.182  -5.513  1.00 98.12           N  
+ATOM   2671  CA  SER A 338     -13.640   7.339  -4.977  1.00 98.12           C  
+ATOM   2672  C   SER A 338     -14.670   6.646  -5.874  1.00 98.12           C  
+ATOM   2673  CB  SER A 338     -13.965   8.828  -4.811  1.00 98.12           C  
+ATOM   2674  O   SER A 338     -15.474   5.854  -5.385  1.00 98.12           O  
+ATOM   2675  OG  SER A 338     -15.212   9.004  -4.162  1.00 98.12           O  
+ATOM   2676  N   ALA A 339     -14.605   6.861  -7.191  1.00 98.06           N  
+ATOM   2677  CA  ALA A 339     -15.488   6.202  -8.151  1.00 98.06           C  
+ATOM   2678  C   ALA A 339     -15.322   4.672  -8.140  1.00 98.06           C  
+ATOM   2679  CB  ALA A 339     -15.212   6.785  -9.541  1.00 98.06           C  
+ATOM   2680  O   ALA A 339     -16.314   3.948  -8.168  1.00 98.06           O  
+ATOM   2681  N   PHE A 340     -14.084   4.178  -8.061  1.00 98.19           N  
+ATOM   2682  CA  PHE A 340     -13.775   2.750  -8.057  1.00 98.19           C  
+ATOM   2683  C   PHE A 340     -14.339   2.025  -6.829  1.00 98.19           C  
+ATOM   2684  CB  PHE A 340     -12.255   2.579  -8.150  1.00 98.19           C  
+ATOM   2685  O   PHE A 340     -14.894   0.936  -6.974  1.00 98.19           O  
+ATOM   2686  CG  PHE A 340     -11.802   1.136  -8.201  1.00 98.19           C  
+ATOM   2687  CD1 PHE A 340     -11.423   0.471  -7.020  1.00 98.19           C  
+ATOM   2688  CD2 PHE A 340     -11.757   0.459  -9.434  1.00 98.19           C  
+ATOM   2689  CE1 PHE A 340     -10.963  -0.854  -7.078  1.00 98.19           C  
+ATOM   2690  CE2 PHE A 340     -11.304  -0.871  -9.491  1.00 98.19           C  
+ATOM   2691  CZ  PHE A 340     -10.892  -1.516  -8.313  1.00 98.19           C  
+ATOM   2692  N   VAL A 341     -14.220   2.629  -5.640  1.00 97.75           N  
+ATOM   2693  CA  VAL A 341     -14.763   2.066  -4.392  1.00 97.75           C  
+ATOM   2694  C   VAL A 341     -16.288   2.163  -4.370  1.00 97.75           C  
+ATOM   2695  CB  VAL A 341     -14.136   2.746  -3.158  1.00 97.75           C  
+ATOM   2696  O   VAL A 341     -16.961   1.176  -4.087  1.00 97.75           O  
+ATOM   2697  CG1 VAL A 341     -14.769   2.273  -1.843  1.00 97.75           C  
+ATOM   2698  CG2 VAL A 341     -12.638   2.428  -3.072  1.00 97.75           C  
+ATOM   2699  N   ASN A 342     -16.843   3.327  -4.717  1.00 92.56           N  
+ATOM   2700  CA  ASN A 342     -18.284   3.572  -4.624  1.00 92.56           C  
+ATOM   2701  C   ASN A 342     -19.106   2.829  -5.689  1.00 92.56           C  
+ATOM   2702  CB  ASN A 342     -18.545   5.084  -4.689  1.00 92.56           C  
+ATOM   2703  O   ASN A 342     -20.304   2.661  -5.503  1.00 92.56           O  
+ATOM   2704  CG  ASN A 342     -18.017   5.848  -3.486  1.00 92.56           C  
+ATOM   2705  ND2 ASN A 342     -17.674   7.102  -3.660  1.00 92.56           N  
+ATOM   2706  OD1 ASN A 342     -17.914   5.362  -2.373  1.00 92.56           O  
+ATOM   2707  N   ALA A 343     -18.501   2.370  -6.790  1.00 89.19           N  
+ATOM   2708  CA  ALA A 343     -19.205   1.592  -7.814  1.00 89.19           C  
+ATOM   2709  C   ALA A 343     -19.745   0.236  -7.308  1.00 89.19           C  
+ATOM   2710  CB  ALA A 343     -18.260   1.403  -9.007  1.00 89.19           C  
+ATOM   2711  O   ALA A 343     -20.637  -0.323  -7.940  1.00 89.19           O  
+ATOM   2712  N   GLU A 344     -19.223  -0.279  -6.190  1.00 72.75           N  
+ATOM   2713  CA  GLU A 344     -19.643  -1.553  -5.579  1.00 72.75           C  
+ATOM   2714  C   GLU A 344     -20.539  -1.375  -4.341  1.00 72.75           C  
+ATOM   2715  CB  GLU A 344     -18.394  -2.350  -5.172  1.00 72.75           C  
+ATOM   2716  O   GLU A 344     -20.982  -2.366  -3.758  1.00 72.75           O  
+ATOM   2717  CG  GLU A 344     -17.454  -2.705  -6.331  1.00 72.75           C  
+ATOM   2718  CD  GLU A 344     -18.124  -3.610  -7.375  1.00 72.75           C  
+ATOM   2719  OE1 GLU A 344     -17.918  -3.320  -8.579  1.00 72.75           O  
+ATOM   2720  OE2 GLU A 344     -18.780  -4.593  -6.984  1.00 72.75           O  
+ATOM   2721  N   CYS A 345     -20.762  -0.134  -3.891  1.00 77.69           N  
+ATOM   2722  CA  CYS A 345     -21.385   0.171  -2.598  1.00 77.69           C  
+ATOM   2723  C   CYS A 345     -22.812   0.717  -2.711  1.00 77.69           C  
+ATOM   2724  CB  CYS A 345     -20.455   1.081  -1.786  1.00 77.69           C  
+ATOM   2725  O   CYS A 345     -23.129   1.417  -3.697  1.00 77.69           O  
+ATOM   2726  SG  CYS A 345     -18.951   0.150  -1.371  1.00 77.69           S  
+ATOM   2727  OXT CYS A 345     -23.580   0.410  -1.770  1.00 77.69           O  
+TER    2728      CYS A 345                                                      
+ENDMDL                                                                          
+END                                                                             
\ No newline at end of file
diff --git a/assets/test_structure/README.md b/assets/test_structure/README.md
new file mode 100644
index 0000000..0452a06
--- /dev/null
+++ b/assets/test_structure/README.md
@@ -0,0 +1,14 @@
+# Test structure fixture
+
+`GID1A_AFDB.pdb` is the AlphaFold DB predicted model for GID1A (UniProt **Q9MAA7**),
+downloaded from https://alphafold.ebi.ac.uk/entry/Q9MAA7.
+
+It is bundled only so the `test` profile can exercise the interactive variant effect
+inspection tool end-to-end without structure prediction.
+
+**Licence:** AlphaFold DB structures are distributed under **CC-BY-4.0**
+(© EMBL-EBI & DeepMind). If reused, attribute AlphaFold DB / UniProt Q9MAA7 accordingly.
+
+> TODO (release): move this fixture to the `deepmutscan` branch of `nf-core/test-datasets`
+> (e.g. `deepmutscan/testdata/GID1A_AFDB.pdb`) with the same attribution, then reference it in
+> `conf/test.config` via `params.pipelines_testdata_base_path` and remove this committed copy.
diff --git a/assets/variant_effect_inspection_tool/3Dmol-min.js b/assets/variant_effect_inspection_tool/3Dmol-min.js
new file mode 100644
index 0000000..c63bd14
--- /dev/null
+++ b/assets/variant_effect_inspection_tool/3Dmol-min.js
@@ -0,0 +1,2 @@
+/*! For license information please see 3Dmol-min.js.LICENSE.txt */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports["3Dmol"]=e():t["3Dmol"]=e()}(this,(()=>(()=>{var __webpack_modules__={620:function(){"use strict";!function(t){if(t.TextEncoder&&t.TextDecoder)return!1;function e(t="utf-8"){if("utf-8"!==t)throw new RangeError(`Failed to construct 'TextEncoder': The encoding label provided ('${t}') is invalid.`)}function i(t="utf-8",e={fatal:!1}){if("utf-8"!==t)throw new RangeError(`Failed to construct 'TextDecoder': The encoding label provided ('${t}') is invalid.`);if(e.fatal)throw new Error("Failed to construct 'TextDecoder': the 'fatal' option is unsupported.")}Object.defineProperty(e.prototype,"encoding",{value:"utf-8"}),e.prototype.encode=function(t,e={stream:!1}){if(e.stream)throw new Error("Failed to encode: the 'stream' option is unsupported.");let i=0;const r=t.length;let s=0,n=Math.max(32,r+(r>>1)+7),a=new 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a[t.gradient](e,i):new a[t.gradient](e,i,t.colors):new a[t.gradient](e,i,t.mid)}if("string"==typeof t.gradient&&t.gradient.startsWith("linear_")){let e=t.gradient.split("_");e.shift();let i=void 0===t.min?-1:t.min,r=void 0===t.max?1:t.max;return new CustomLinear(i,r,e)}return t}class RWB extends GradientType{constructor(t,e,i){super(),this.gradient="RWB",this.mult=1,this.mid=i,this.min=t,this.max=e,void 0===e&&Array.isArray(t)&&t.length>=2?(this.max=t[1],this.min=t[0]):t&&e&&!Array.isArray(t)&&(this.min=t,this.max=e)}range(){return void 0!==this.min&&void 0!==this.max?[this.min,this.max]:null}valueToHex(t,e){var i,r;if(t=this.mult*t,e?(i=e[0],r=e[1]):(i=this.min,r=this.max),void 0===t)return 16777215;var n=s(i,r,t);i=n.lo;var a,o=((r=n.hi)+i)/2;return(t=n.val)<(o=e&&void 0!==e[2]?e[2]:void 0!==this.mid?this.mid:(i+r)/2)?16711680+256*(a=Math.floor(255*Math.sqrt((t-i)/(o-i))))+a:t>o?65536*(a=Math.floor(255*Math.sqrt(1-(t-o)/(r-o))))+256*a+255:16777215}}class ROYGB extends GradientType{constructor(t,e){super(),this.gradient="ROYGB",this.mult=1,this.min=t,this.max=e,void 0===e&&Array.isArray(t)&&t.length>=2?(this.max=t[1],this.min=t[0]):t&&e&&!Array.isArray(t)&&(this.min=t,this.max=e)}valueToHex(t,e){var i,r;if(t=this.mult*t,e?(i=e[0],r=e[1]):(i=this.min,r=this.max),void 0===t)return 16777215;var n=s(i,r,t),a=((i=n.lo)+(r=n.hi))/2,o=(i+a)/2,l=(a+r)/2;return(t=n.val)<o?16711680+256*Math.floor(255*Math.sqrt((t-i)/(o-i)))+0:t<a?65536*Math.floor(255*Math.sqrt(1-(t-o)/(a-o)))+65280+0:t<l?65280+1*Math.floor(255*Math.sqrt((t-a)/(l-a))):0+256*Math.floor(255*Math.sqrt(1-(t-l)/(r-l)))+255}range(){return void 0!==this.min&&void 0!==this.max?[this.min,this.max]:null}}class Sinebow extends GradientType{constructor(t,e){super(),this.gradient="Sinebow",this.mult=1,this.min=t,this.max=e,void 0===e&&Array.isArray(t)&&t.length>=2&&(this.max=t[1],this.min=t[0]),e<t&&(this.mult=-1,this.min*=-1,this.max*=-1)}valueToHex(t,e){var i,r;if(t=this.mult*t,e?(i=e[0],r=e[1]):(i=this.min,r=this.max),void 0===t)return 16777215;var s=Gradient.normalizeValue(i,r,t);i=s.lo,r=s.hi;var n=5*(((t=s.val)-i)/(r-i))/6+.5,a=Math.sin(Math.PI*n);a*=255*a;var o=Math.sin(Math.PI*(n+1/3));o*=255*o;var l=Math.sin(Math.PI*(n+2/3));return l*=255*l,65536*Math.floor(a)+256*Math.floor(l)+1*Math.floor(o)}range(){return void 0!==this.min&&void 0!==this.max?[this.min,this.max]:null}}class CustomLinear extends GradientType{constructor(t,e,i){var s;if(super(),this.gradient="linear",this.colors=new Array,Array.isArray(t)&&t.length>=2?(this.max=t[1],this.min=t[0],s=e):(this.min=t,this.max=e,s=i),s)for(let t of s)this.colors.push(r.CC.color(t));else this.colors.push(r.CC.color(0))}range(){return void 0!==this.min&&void 0!==this.max?[this.min,this.max]:null}valueToHex(t,e){var i,n;if(e?(i=e[0],n=e[1]):(i=this.min,n=this.max),void 0===t)return 16777215;var a=s(i,n,t);i=a.lo,n=a.hi,t=a.val;let o=this.colors.length,l=(n-i)/o,h=Math.min(Math.floor((t-i)/l),o-1),c=Math.min(h+1,o-1),d=(t-i-h*l)/l,u=this.colors[h],f=this.colors[c];return new r.Color(u.r+d*(f.r-u.r),u.g+d*(f.g-u.g),u.b+d*(f.b-u.b)).getHex()}}const a={rwb:RWB,RWB,roygb:ROYGB,ROYGB,sinebow:Sinebow,linear:CustomLinear};class Gradient extends GradientType{valueToHex(t,e){return 0}range(){return null}}Gradient.RWB=RWB,Gradient.ROYGB=ROYGB,Gradient.Sinebow=Sinebow,Gradient.CustomLinear=CustomLinear,Gradient.builtinGradients=a,Gradient.normalizeValue=s,Gradient.getGradient=n},848:(t,e,i)=>{"use strict";i.r(e),i.d(e,{VolumeData:()=>VolumeData});var r=i(864),s=i(529),n=i(797),a=i(865),o=i(75);class VolumeData{constructor(t,e,i){if(this.unit={x:1,y:1,z:1},this.origin={x:0,y:0,z:0},this.size={x:0,y:0,z:0},this.data=new Float32Array([]),this.matrix=null,this.inversematrix=null,this.isbinary=new Set(["ccp4","CCP4"]),this.getCoordinates=function(t){var e=t/(this.size.y*this.size.z),i=t%(this.size.y*this.size.z),r=t%this.size.z;return e*=this.unit.x,i*=this.unit.y,r*=this.unit.z,{x:e+=this.origin.x,y:i+=this.origin.y,z:r+=this.origin.z}},this.vasp=function(t){var e=t.replace(/^\s+/,"").split(/[\n\r]/),i=(0,n.VASP)(t)[0].length;if(0==i)return console.warn("No good formating of CHG or CHGCAR file, not atomic information provided in the file."),void(this.data=[]);var r,a=1.889725992,o=parseFloat(e[1]);r=e[2].replace(/^\s+/,"").split(/\s+/);var l=new s.Vector3(parseFloat(r[0]),parseFloat(r[1]),parseFloat(r[2])).multiplyScalar(o*a);r=e[3].replace(/^\s+/,"").split(/\s+/);var h=new s.Vector3(parseFloat(r[0]),parseFloat(r[1]),parseFloat(r[2])).multiplyScalar(o*a);r=e[4].replace(/^\s+/,"").split(/\s+/);var c=new s.Vector3(parseFloat(r[0]),parseFloat(r[1]),parseFloat(r[2])).multiplyScalar(o*a),d=l.x*(h.y*c.z-c.y*h.z)-h.x*(l.y*c.z-c.y*l.z)+c.x*(l.y*h.z-h.y*l.z),u=1/(d=Math.abs(d)/Math.pow(a,3));e.splice(0,8+i+1);var f=e[0].replace(/^\s+/,"").replace(/\s+/g," ").split(" "),p=Math.abs(parseFloat(f[0])),g=Math.abs(parseFloat(f[1])),m=Math.abs(parseFloat(f[2])),v=this.origin=new s.Vector3(0,0,0);this.size={x:p,y:g,z:m},this.unit=new s.Vector3(l.x,h.y,c.z),l=l.multiplyScalar(1/(a*p)),h=h.multiplyScalar(1/(a*g)),c=c.multiplyScalar(1/(a*m)),0==l.y&&0==l.z&&0==h.x&&0==h.z&&0==c.x&&0==c.y||(this.matrix=new s.Matrix4(l.x,h.x,c.x,0,l.y,h.y,c.y,0,l.z,h.z,c.z,0,0,0,0,1),this.matrix=this.matrix.multiplyMatrices(this.matrix,(new s.Matrix4).makeTranslation(v.x,v.y,v.z)),this.origin=new s.Vector3(0,0,0),this.unit=new s.Vector3(1,1,1)),e.splice(0,1);var _=e.join(" "),y=(_=_.replace(/^\s+/,"")).split(/[\s\r]+/);y.splice(p*g*m+1);for(var b=Float32Array.from(y,parseFloat),x=0;x<b.length;x++)b[x]=b[x]*u*.036749309;this.data=b},this.dx=function(t){var e,i=t.split(/[\n\r]+/),r=/gridpositions\s+counts\s+(\d+)\s+(\d+)\s+(\d+)/,n=/^origin\s+(\S+)\s+(\S+)\s+(\S+)/,a=/^delta\s+(\S+)\s+(\S+)\s+(\S+)/,o=/data 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s.Matrix4).getInverse(this.matrix));var r=new s.Vector3(t,e,i);t=(r=r.applyMatrix4(this.inversematrix)).x,e=r.y,i=r.z}else t-=this.origin.x,e-=this.origin.y,i-=this.origin.z,t/=this.unit.x,e/=this.unit.y,i/=this.unit.z;return t=Math.round(t),e=Math.round(e),i=Math.round(i),t<0||t>=this.size.x||e<0||e>=this.size.y||i<0||i>=this.size.z?-1:t*this.size.y*this.size.z+e*this.size.z+i}getVal(t,e,i){let r=this.getIndex(t,e,i);return r<0?0:this.data[r]}cube(t){var e=t.split(/\r?\n/);if(!(e.length<6)){var i=(0,a.CUBE)(t,{}).modelData[0].cryst,r=e[2].replace(/^\s+/,"").replace(/\s+/g," ").split(" "),s=parseFloat(r[0]),n=Math.abs(s);this.origin=i.origin,this.size=i.size,this.unit=i.unit,this.matrix=i.matrix4;var o=6;s<0&&o++;var l=e.splice(n+o).join(" "),h=(l=l.replace(/^\s+/,"")).split(/[\s\r]+/);this.data=Float32Array.from(h,parseFloat)}}ccp4(t){var e={};t=new Int8Array(t);var i=new Int32Array(t.buffer,0,56),r=new Float32Array(t.buffer,0,56),n=new DataView(t.buffer);if(e.MAP=String.fromCharCode(n.getUint8(208),n.getUint8(209),n.getUint8(210),n.getUint8(211)),e.MACHST=[n.getUint8(212),n.getUint8(213)],17===e.MACHST[0]&&17===e.MACHST[1])for(var a=t.byteLength,o=0;o<a;o+=4)n.setFloat32(o,n.getFloat32(o),!0);e.NX=i[0],e.NY=i[1],e.NZ=i[2],e.MODE=i[3],e.NXSTART=i[4],e.NYSTART=i[5],e.NZSTART=i[6],e.MX=i[7],e.MY=i[8],e.MZ=i[9],e.xlen=r[10],e.ylen=r[11],e.zlen=r[12],e.alpha=r[13],e.beta=r[14],e.gamma=r[15],e.MAPC=i[16],e.MAPR=i[17],e.MAPS=i[18],e.DMIN=r[19],e.DMAX=r[20],e.DMEAN=r[21],e.ISPG=i[22],e.NSYMBT=i[23],e.LSKFLG=i[24],e.originX=r[49],e.originY=r[50],e.originZ=r[51],e.ARMS=r[54];var l=e,h=[l.xlen,0,0],c=[l.ylen*Math.cos(Math.PI/180*l.gamma),l.ylen*Math.sin(Math.PI/180*l.gamma),0],d=[l.zlen*Math.cos(Math.PI/180*l.beta),l.zlen*(Math.cos(Math.PI/180*l.alpha)-Math.cos(Math.PI/180*l.gamma)*Math.cos(Math.PI/180*l.beta))/Math.sin(Math.PI/180*l.gamma),0];d[2]=Math.sqrt(l.zlen*l.zlen*Math.sin(Math.PI/180*l.beta)*Math.sin(Math.PI/180*l.beta)-d[1]*d[1]);var u=[0,h,c,d],f=[0,l.MX,l.MY,l.MZ],p=[0,l.MAPC,l.MAPR,l.MAPS];this.matrix=new s.Matrix4,this.matrix.set(u[p[1]][0]/f[p[1]],u[p[2]][0]/f[p[2]],u[p[3]][0]/f[p[3]],0,u[p[1]][1]/f[p[1]],u[p[2]][1]/f[p[2]],u[p[3]][1]/f[p[3]],0,u[p[1]][2]/f[p[1]],u[p[2]][2]/f[p[2]],u[p[3]][2]/f[p[3]],0,0,0,0,1),this.matrix=this.matrix.multiplyMatrices(this.matrix,(new s.Matrix4).makeTranslation(l.NXSTART+l.originX,l.NYSTART+l.originY,l.NZSTART+l.originZ)),this.origin=new s.Vector3(0,0,0),this.unit=new s.Vector3(1,1,1),this.size={x:e.NX,y:e.NY,z:e.NZ},this.dimensionorder=[e.MAPC,e.MAPR,e.MAPS];var g=new Float32Array(t.buffer,1024+e.NSYMBT),m=e.NX,v=e.NY,_=e.NZ;this.data=new Float32Array(m*v*_);for(let t=0;t<m;t++)for(let e=0;e<v;e++)for(let i=0;i<_;i++)this.data[(t*v+e)*_+i]=g[(i*v+e)*m+t]}}},638:(t,e,i)=>{"use strict";var r;i.r(e),i.d(e,{BackSide:()=>n,Camera:()=>Camera,ClampToEdgeWrapping:()=>c,Coloring:()=>r,Cylinder:()=>M.Cylinder,DoubleSide:()=>a,EventDispatcher:()=>EventDispatcher,FloatType:()=>g,Fog:()=>Fog,FrontSide:()=>s,Geometry:()=>Geometry,GeometryGroup:()=>GeometryGroup,GeometryIDCount:()=>A,ImposterMaterial:()=>ImposterMaterial,InstancedMaterial:()=>InstancedMaterial,Light:()=>Light,Line:()=>Line,LineBasicMaterial:()=>LineBasicMaterial,LineStyle:()=>N,LinearFilter:()=>d,LinearMipMapLinearFilter:()=>f,Material:()=>Material,MaterialIdCount:()=>x,Matrix3:()=>l.Matrix3,Matrix4:()=>l.Matrix4,Mesh:()=>Mesh,MeshDoubleLambertMaterial:()=>MeshDoubleLambertMaterial,MeshLambertMaterial:()=>MeshLambertMaterial,MeshOutlineMaterial:()=>MeshOutlineMaterial,NearestFilter:()=>u,Object3D:()=>Object3D,Object3DIDCount:()=>C,Projector:()=>Projector,Quaternion:()=>l.Quaternion,R32Format:()=>_,RFormat:()=>v,RGBAFormat:()=>m,Ray:()=>l.Ray,Raycaster:()=>Raycaster,Renderer:()=>Renderer,Scene:()=>Scene,ShaderLib:()=>ct,ShaderUtils:()=>ut,Shading:()=>o,Sphere:()=>M.Sphere,SphereImposterMaterial:()=>SphereImposterMaterial,SphereImposterOutlineMaterial:()=>SphereImposterOutlineMaterial,Sprite:()=>Sprite,SpriteAlignment:()=>h,SpriteMaterial:()=>SpriteMaterial,SpritePlugin:()=>SpritePlugin,StickImposterMaterial:()=>StickImposterMaterial,StickImposterOutlineMaterial:()=>StickImposterOutlineMaterial,Texture:()=>Texture,TextureIdCount:()=>B,TextureOperations:()=>y,Triangle:()=>M.Triangle,UVMapping:()=>UVMapping,UnsignedByteType:()=>p,Vector2:()=>l.Vector2,Vector3:()=>l.Vector3,VolumetricMaterial:()=>VolumetricMaterial,basic:()=>V,clamp:()=>l.clamp,clone:()=>dt,conversionMatrix3:()=>l.conversionMatrix3,degToRad:()=>l.degToRad,instanced:()=>H,intersectObject:()=>U,lambert:()=>q,lambertdouble:()=>Z,outline:()=>K,screen:()=>Q,screenaa:()=>$,sphereimposter:()=>tt,sphereimposteroutline:()=>it,sprite:()=>rt,stickimposter:()=>at,stickimposteroutline:()=>ot,volumetric:()=>ht}),function(t){t[t.NoColors=0]="NoColors",t[t.FaceColors=1]="FaceColors",t[t.VertexColors=2]="VertexColors"}(r||(r={}));const s=0,n=1,a=2;var o;!function(t){t[t.NoShading=0]="NoShading",t[t.FlatShading=1]="FlatShading",t[t.SmoothShading=2]="SmoothShading"}(o||(o={}));var l=i(529);const h={topLeft:new l.Vector2(1,-1),topCenter:new l.Vector2(0,-1),topRight:new l.Vector2(-1,-1),centerLeft:new l.Vector2(1,0),center:new l.Vector2(0,0),centerRight:new l.Vector2(-1,0),bottomLeft:new l.Vector2(1,1),bottomCenter:new l.Vector2(0,1),bottomRight:new l.Vector2(-1,1)},c=1001,d=1006,u=1007,f=1008,p=1009,g=1010,m=1021,v=1022,_=1023;var y;!function(t){t[t.MultiplyOperation=0]="MultiplyOperation",t[t.MixOperation=1]="MixOperation",t[t.AddOperation=2]="AddOperation"}(y||(y={}));class EventDispatcher{constructor(){this.listeners={}}dispatchEvent(t){var e=this.listeners[t.type];if(void 0!==e){t.target=this;for(var i=0,r=e.length;i<r;i++)e[i].call(this,t)}}removeEventListener(t,e){if(e){var i=this.listeners[t].indexOf(e);-1!==i&&this.listeners[t].splice(i,1)}else this.listeners[t]=[]}addEventListener(t,e){void 0===this.listeners[t]&&(this.listeners[t]=[]),-1===this.listeners[t].indexOf(e)&&this.listeners[t].push(e)}}var b=i(222);class Material extends EventDispatcher{constructor(){super(...arguments),this.id=x++,this.name="",this.side=s,this.opacity=1,this.transparent=!1,this.depthTest=!0,this.depthWrite=!0,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.alphaTest=0,this.visible=!0,this.needsUpdate=!0,this.outline=!1,this.wireframe=!1,this.shaded=!1}setValues(t={}){if(void 0!==t)for(var e in t){var i=t[e];if(void 0!==i){if(e in this){var r=this[e];r instanceof b.Color&&i instanceof b.Color?r.copy(i):r instanceof b.Color?r.set(i):r instanceof l.Vector3&&i instanceof l.Vector3?r.copy(i):this[e]=i}}else console.warn("$3Dmol.Material: '"+e+"' parameter is undefined.")}}clone(t=new Material){return t.name=this.name,t.side=this.side,t.opacity=this.opacity,t.transparent=this.transparent,t.depthTest=this.depthTest,t.depthWrite=this.depthWrite,t.polygonOffset=this.polygonOffset,t.polygonOffsetFactor=this.polygonOffsetFactor,t.polygonOffsetUnits=this.polygonOffsetUnits,t.alphaTest=this.alphaTest,t.overdraw=this.overdraw,t.visible=this.visible,t}makeShaded(t){this.shaded=!0,this.uniforms&&(this.uniforms.shading={type:"i",value:t},this.uniforms.vWidth={type:"f",value:1},this.uniforms.vHeight={type:"f",value:1})}dispose(){this.dispatchEvent({type:"dispose"})}}let x=0;class LineBasicMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.vertexColors=!1,this.fog=!0,this.shaderID="basic",this.setValues(t)}clone(t=new LineBasicMaterial){return super.clone.call(this,t),t.color.copy(this.color),t}}const w=65535;class GeometryGroup{constructor(t=0){this.vertexArray=null,this.colorArray=null,this.normalArray=null,this.radiusArray=null,this.faceArray=null,this.lineArray=null,this.atomArray=Array(),this.vertices=0,this.faceidx=0,this.lineidx=0,this.__inittedArrays=!1,this.id=t}setColor(t){var e=this.vertexArray,i=this.colorArray;if(!e)throw new Error("vertex array not initialized");if(!i)throw new Error("color array not initialized");let r=b.CC.color(t);for(var s=0;s<e.length;s+=3)i[s]=r.r,i[s+1]=r.g,i[s+2]=r.b}setColors(t){var e=this.vertexArray,i=this.colorArray;if(!e)throw new Error("vertex array not initialized");if(!i)throw new Error("color array not initialized");if(e.length==i.length)for(var r=0;r<e.length;r+=3){var s=t(e[r],e[r+1],e[r+2]);s instanceof b.Color||(s=b.CC.color(s)),i[r]=s.r,i[r+1]=s.g,i[r+2]=s.b}else console.log("Cannot re-color geometry group due to mismatched lengths.")}getNumVertices(){return this.vertices}getVertices(){return this.vertexArray}getCentroid(){if(!this.vertexArray)throw new Error("vertex array not initialized");for(var t,e,i,r,s=new l.Vector3,n=0;n<this.vertices;++n)t=3*n,e=this.vertexArray[t],i=this.vertexArray[t+1],r=this.vertexArray[t+2],s.x+=e,s.y+=i,s.z+=r;return s.divideScalar(this.vertices),s}setNormals(){var t=this.faceArray,e=this.vertexArray,i=this.normalArray;if(this.vertices&&this.faceidx){if(!t)throw new Error("face array not initialized");if(!e)throw new Error("vertex array not initialized");if(!i)throw new Error("normal array not initialized");for(var r,s,n,a,o,h,c,d=0;d<t.length/3;++d)r=3*t[3*d],s=3*t[3*d+1],n=3*t[3*d+2],a=new l.Vector3(e[r],e[r+1],e[r+2]),o=new l.Vector3(e[s],e[s+1],e[s+2]),h=new l.Vector3(e[n],e[n+1],e[n+2]),a.subVectors(a,o),h.subVectors(h,o),h.cross(a),(c=h).normalize(),i[r]+=c.x,i[s]+=c.x,i[n]+=c.x,i[r+1]+=c.y,i[s+1]+=c.y,i[n+1]+=c.y,i[r+2]+=c.z,i[s+2]+=c.z,i[n+2]+=c.z}}setLineIndices(){if(this.faceidx&&(!this.lineArray||this.lineArray.length!=2*this.faceidx||this.lineidx!=2*this.faceidx)){var t=this.faceArray,e=this.lineArray=new Uint16Array(2*this.faceidx);if(this.lineidx=2*this.faceidx,!t)throw new Error("face array not initialized");for(var i=0;i<this.faceidx/3;++i){var r=3*i,s=2*r,n=t[r],a=t[r+1],o=t[r+2];e[s]=n,e[s+1]=a,e[s+2]=n,e[s+3]=o,e[s+4]=a,e[s+5]=o}}}vrml(t,e){var i,r,s,n,a,o,l,h,c,d,u,f,p,g,m,v,_,y,b="";if(b+=t+"Shape {\n"+t+" appearance Appearance {\n"+t+"  material Material {\n"+t+"   diffuseColor "+(null===(i=null==e?void 0:e.color)||void 0===i?void 0:i.r)+" "+(null===(r=null==e?void 0:e.color)||void 0===r?void 0:r.g)+" "+(null===(s=null==e?void 0:e.color)||void 0===s?void 0:s.b)+"\n",e.wireframe&&this.colorArray){let e=this.colorArray;b+=t+"    emissiveColor "+e[0]+" "+e[1]+" "+e[2]+"\n"}(null==e?void 0:e.transparent)&&(b+=t+"   transparency "+(1-e.opacity)+"\n"),b+=t+"  }\n",b+=t+" }\n";var x=t;if(t+=" ",e instanceof LineBasicMaterial||e.wireframe){let i,r,s;b+=t+"geometry IndexedLineSet {\n"+t+" colorPerVertex TRUE\n"+t+" coord Coordinate {\n"+t+"  point [\n";for(let e=0;e<this.vertices;++e){let l=3*e;i=null===(n=this.vertexArray)||void 0===n?void 0:n[l],r=null===(a=this.vertexArray)||void 0===a?void 0:a[l+1],s=null===(o=this.vertexArray)||void 0===o?void 0:o[l+2],b+=t+"   "+i+" "+r+" "+s+",\n"}if(b+=t+"  ]\n",b+=t+" }\n",this.colorArray&&!e.wireframe){b+=t+" color Color {\n"+t+"  color [\n";for(let e=0;e<this.vertices;++e){let n=3*e;i=this.colorArray[n],r=this.colorArray[n+1],s=this.colorArray[n+2],b+=t+"   "+i+" "+r+" "+s+",\n"}b+=t+"  ]\n",b+=t+" }\n"}if(b+=t+" coordIndex [\n",e.wireframe&&this.faceArray)for(let e=0;e<this.faceidx;e+=3)i=null===(l=this.faceArray)||void 0===l?void 0:l[e],r=null===(h=this.faceArray)||void 0===h?void 0:h[e+1],s=null===(c=this.faceArray)||void 0===c?void 0:c[e+2],b+=t+"  "+i+", "+r+", "+s+", -1,\n";else for(let e=0;e<this.vertices-1;e+=2)b+=t+"  "+e+", "+(e+1)+", -1,\n";b+=t+" ]\n",b+=t+"}\n"}else{let e,i,r;b+=t+"geometry IndexedFaceSet {\n"+t+" colorPerVertex TRUE\n"+t+" normalPerVertex TRUE\n"+t+" solid FALSE\n",b+=t+" coord Coordinate {\n"+t+"  point [\n";for(let s=0;s<this.vertices;++s){let n=3*s;e=null===(d=this.vertexArray)||void 0===d?void 0:d[n],i=null===(u=this.vertexArray)||void 0===u?void 0:u[n+1],r=null===(f=this.vertexArray)||void 0===f?void 0:f[n+2],b+=t+"   "+e+" "+i+" "+r+",\n"}b+=t+"  ]\n",b+=t+" }\n",b+=t+" normal Normal {\n"+t+"  vector [\n";for(let s=0;s<this.vertices;++s){let n=3*s;e=null===(p=this.normalArray)||void 0===p?void 0:p[n],i=null===(g=this.normalArray)||void 0===g?void 0:g[n+1],r=null===(m=this.normalArray)||void 0===m?void 0:m[n+2],b+=t+"   "+e+" "+i+" "+r+",\n"}if(b+=t+"  ]\n",b+=t+" }\n",this.colorArray){b+=t+" color Color {\n"+t+"  color [\n";for(let s=0;s<this.vertices;++s){let n=3*s;e=this.colorArray[n],i=this.colorArray[n+1],r=this.colorArray[n+2],b+=t+"   "+e+" "+i+" "+r+",\n"}b+=t+"  ]\n",b+=t+" }\n"}b+=t+" coordIndex [\n";for(let s=0;s<this.faceidx;s+=3)e=null===(v=this.faceArray)||void 0===v?void 0:v[s],i=null===(_=this.faceArray)||void 0===_?void 0:_[s+1],r=null===(y=this.faceArray)||void 0===y?void 0:y[s+2],b+=t+"  "+e+", "+i+", "+r+", -1,\n";b+=t+" ]\n",b+=t+"}\n"}return b+=x+"}"}truncateArrayBuffers(t=!0,e=!1){var i=this.vertexArray,r=this.colorArray,s=this.normalArray,n=this.faceArray,a=this.lineArray,o=this.radiusArray;this.vertexArray=(null==i?void 0:i.subarray(0,3*this.vertices))||null,this.colorArray=(null==r?void 0:r.subarray(0,3*this.vertices))||null,t?(this.normalArray=(null==s?void 0:s.subarray(0,3*this.vertices))||null,this.faceArray=(null==n?void 0:n.subarray(0,this.faceidx))||null,this.lineidx>0?this.lineArray=(null==a?void 0:a.subarray(0,this.lineidx))||null:this.lineArray=new Uint16Array(0)):(this.normalArray=new Float32Array(0),this.faceArray=new Uint16Array(0),this.lineArray=new Uint16Array(0)),o&&(this.radiusArray=o.subarray(0,this.vertices)),e&&(this.normalArray&&(this.normalArray=new Float32Array(this.normalArray)),this.faceArray&&(this.faceArray=new Uint16Array(this.faceArray)),this.lineArray&&(this.lineArray=new Uint16Array(this.lineArray)),this.vertexArray&&(this.vertexArray=new Float32Array(this.vertexArray)),this.colorArray&&(this.colorArray=new Float32Array(this.colorArray)),this.radiusArray&&(this.radiusArray=new Float32Array(this.radiusArray))),this.__inittedArrays=!0}}class Geometry extends EventDispatcher{constructor(t=!1,e=!1,i=!1){super(),this.name="",this.hasTangents=!1,this.dynamic=!0,this.verticesNeedUpdate=!1,this.elementsNeedUpdate=!1,this.normalsNeedUpdate=!1,this.colorsNeedUpdate=!1,this.buffersNeedUpdate=!1,this.imposter=!1,this.instanced=!1,this.geometryGroups=[],this.groups=0,this.id=A++,this.mesh=t,this.radii=e,this.offset=i}updateGeoGroup(t=0){var e,i=this.groups>0?this.geometryGroups[this.groups-1]:null;return(!i||i.vertices+t>((null===(e=null==i?void 0:i.vertexArray)||void 0===e?void 0:e.length)||0)/3)&&(i=this.addGeoGroup()),i}vrml(t,e){for(var i="",r=this.geometryGroups.length,s=0;s<r;s++){i+=this.geometryGroups[s].vrml(t,e)+",\n"}return i}addGeoGroup(){var t=new GeometryGroup(this.geometryGroups.length);return this.geometryGroups.push(t),this.groups=this.geometryGroups.length,t.vertexArray=new Float32Array(196605),t.colorArray=new Float32Array(196605),this.mesh&&(t.normalArray=new Float32Array(196605),t.faceArray=new Uint16Array(393210),t.lineArray=new Uint16Array(393210)),this.radii&&(t.radiusArray=new Float32Array(w)),t.useOffset=this.offset,t}setUpNormals(...t){for(var e=0;e<this.groups;e++){this.geometryGroups[e].setNormals(...t)}}setColors(...t){for(var e=this.geometryGroups.length,i=0;i<e;i++){this.geometryGroups[i].setColors(...t)}}setColor(...t){for(var e=this.geometryGroups.length,i=0;i<e;i++){this.geometryGroups[i].setColor(...t)}}setUpWireframe(...t){for(var e=0;e<this.groups;e++){this.geometryGroups[e].setLineIndices(...t)}}initTypedArrays(){for(var t=0;t<this.groups;t++){var e=this.geometryGroups[t];!0!==e.__inittedArrays&&e.truncateArrayBuffers(this.mesh,!1)}}dispose(){this.dispatchEvent({type:"dispose"})}get vertices(){for(var t=0,e=0;e<this.groups;e++)t+=this.geometryGroups[e].vertices;return t}}let A=0,C=0;class Object3D{constructor(){this.id=C++,this.name="",this.children=[],this.position=new l.Vector3,this.rotation=new l.Vector3,this.matrix=new l.Matrix4,this.matrixWorld=new l.Matrix4,this.quaternion=new l.Quaternion,this.eulerOrder="XYZ",this.up=new l.Vector3(0,1,0),this.scale=new l.Vector3(1,1,1),this.matrixAutoUpdate=!0,this.matrixWorldNeedsUpdate=!0,this.rotationAutoUpdate=!0,this.useQuaternion=!1,this.visible=!0}lookAt(t){this.matrix.lookAt(t,this.position,this.up),this.rotationAutoUpdate&&(!0===this.useQuaternion?console.error("Unimplemented math operation."):this.rotation instanceof l.Vector3&&this.rotation.setEulerFromRotationMatrix(this.matrix,this.eulerOrder))}add(t){if(t!==this){t.parent=this,this.children.push(t);for(var e=this;void 0!==e.parent;)e=e.parent;void 0!==e&&e instanceof Scene&&e.__addObject(t)}else console.error("Can't add $3Dmol.Object3D to itself")}remove(t){var e=this.children.indexOf(t);if(-1!==e){t.parent=void 0,this.children.splice(e,1);for(var i=this;void 0!==i.parent;)i=i.parent;void 0!==i&&i instanceof Scene&&i.__removeObject(t)}}vrml(t){t||(t=" ");var e=2*Math.atan2(this.quaternion.lengthxyz(),this.quaternion.w),i=0,r=0,s=0;if(0!=e){let t=Math.sin(e/2);i=this.quaternion.x/t,r=this.quaternion.y/t,s=this.quaternion.z/t}var n=t+"Transform {\n"+t+" center "+this.position.x+" "+this.position.y+" "+this.position.z+"\n"+t+" rotation "+i+" "+r+" "+s+" "+e+"\n"+t+" children [\n";this.geometry&&(n+=this.geometry.vrml(t,this.material));for(var a=0;a<this.children.length;a++)n+=this.children[a].vrml(t+" ")+",\n";return n+=" ]\n",n+="}"}updateMatrix(){this.matrix.setPosition(this.position),!1===this.useQuaternion&&this.rotation instanceof l.Vector3?this.matrix.setRotationFromEuler(this.rotation,this.eulerOrder):this.matrix.setRotationFromQuaternion(this.quaternion),1===this.scale.x&&1===this.scale.y&&1===this.scale.z||this.matrix.scale(this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){!0===this.matrixAutoUpdate&&this.updateMatrix(),!0!==this.matrixWorldNeedsUpdate&&!0!==t||(void 0===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix)),this.matrixWorldNeedsUpdate=!1;for(var e=0;e<this.children.length;e++)this.children[e].updateMatrixWorld(!0)}clone(t){void 0===t&&(t=new Object3D),t.name=this.name,t.up.copy(this.up),t.position.copy(this.position),t.rotation instanceof l.Vector3&&this.rotation instanceof l.Vector3?t.rotation.copy(this.rotation):t.rotation=this.rotation,t.eulerOrder=this.eulerOrder,t.scale.copy(this.scale),t.rotationAutoUpdate=this.rotationAutoUpdate,t.matrix.copy(this.matrix),t.matrixWorld.copy(this.matrixWorld),t.quaternion.copy(this.quaternion),t.matrixAutoUpdate=this.matrixAutoUpdate,t.matrixWorldNeedsUpdate=this.matrixWorldNeedsUpdate,t.useQuaternion=this.useQuaternion,t.visible=this.visible;for(var e=0;e<this.children.length;e++){var i=this.children[e];t.add(i.clone())}return t}setVisible(t){this.visible=t;for(var e=0;e<this.children.length;e++){this.children[e].setVisible(t)}}}class Scene extends Object3D{constructor(){super(...arguments),this.fog=null,this.overrideMaterial=null,this.matrixAutoUpdate=!1,this.__objects=[],this.__lights=[],this.__objectsAdded=[],this.__objectsRemoved=[]}__addObject(t){if(t instanceof Light)-1===this.__lights.indexOf(t)&&this.__lights.push(t),t.target&&void 0===t.target.parent&&this.add(t.target);else if(-1===this.__objects.indexOf(t)){this.__objects.push(t),this.__objectsAdded.push(t);var e=this.__objectsRemoved.indexOf(t);-1!==e&&this.__objectsRemoved.splice(e,1)}for(var i=0;i<t.children.length;i++)this.__addObject(t.children[i])}__removeObject(t){var e;t instanceof Light?-1!==(e=this.__lights.indexOf(t))&&this.__lights.splice(e,1):-1!==(e=this.__objects.indexOf(t))&&(this.__objects.splice(e,1),this.__objectsRemoved.push(t),-1!==this.__objectsAdded.indexOf(t)&&this.__objectsAdded.splice(e,1));for(var i=0;i<t.children.length;i++)this.__removeObject(t.children[i])}}class Light extends Object3D{constructor(t,e=1){super(),this.position=new l.Vector3(0,1,0),this.target=new Object3D,this.castShadow=!1,this.onlyShadow=!1,this.color=new b.Color(t),this.intensity=e}}const S=new l.Matrix4;class Projector{static unprojectVector(t,e){return e.projectionMatrixInverse.getInverse(e.projectionMatrix),S.multiplyMatrices(e.matrixWorld,e.projectionMatrixInverse),t.applyProjection(S)}static projectVector(t,e){return e.matrixWorldInverse.getInverse(e.matrixWorld),S.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),t.applyProjection(S)}projectVector(t,e){return Projector.projectVector(t,e)}unprojectVector(t,e){return Projector.unprojectVector(t,e)}}var M=i(99);const z=(t,e)=>t.distance-e.distance,T=new l.Matrix4;class Raycaster{constructor(t,e,i,r){this.precision=1e-4,this.linePrecision=.2,this.ray=new l.Ray(t,e),this.ray.direction.lengthSq()>0&&this.ray.direction.normalize(),this.near=r||0,this.far=i||1/0}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e.ortho?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld)):(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,t.z),e.projectionMatrixInverse.getInverse(e.projectionMatrix),T.multiplyMatrices(e.matrixWorld,e.projectionMatrixInverse),this.ray.direction.applyProjection(T),this.ray.direction.sub(this.ray.origin).normalize())}intersectObjects(t,e){for(var i=[],r=0,s=e.length;r<s;r++)U(t,e[r],this,i);return i.sort(z),i}}const E=t=>Math.min(Math.max(t,-1),1);var L=new M.Sphere,F=new M.Cylinder,I=new M.Triangle,O=new l.Vector3,D=new l.Vector3,k=new l.Vector3,R=new l.Vector3,P=new l.Vector3;function U(t,e,i,r){if(P.getPositionFromMatrix(t.matrixWorld),void 0===e.intersectionShape)return r;var s,n,a,o,l,h,c,d,u,f,p,g,m,v,_=e.intersectionShape,y=i.linePrecision,b=(y*=t.matrixWorld.getMaxScaleOnAxis())*y;if(void 0!==e.boundingSphere&&e.boundingSphere instanceof M.Sphere&&(L.copy(e.boundingSphere),L.applyMatrix4(t.matrixWorld),!i.ray.isIntersectionSphere(L)))return r;for(s=0,n=_.triangle.length;s<n;s++)if(_.triangle[s]instanceof M.Triangle){if(I.copy(_.triangle[s]),I.applyMatrix4(t.matrixWorld),a=I.getNormal(),(o=i.ray.direction.dot(a))>=0)continue;if(O.subVectors(I.a,i.ray.origin),(c=a.dot(O)/o)<0)continue;D.copy(i.ray.direction).multiplyScalar(c).add(i.ray.origin),D.sub(I.a),k.copy(I.b).sub(I.a),R.copy(I.c).sub(I.a);var x=k.dot(R),w=k.lengthSq(),A=R.lengthSq();if((g=(w*D.dot(R)-x*D.dot(k))/(w*A-x*x))<0||g>1)continue;if((p=(D.dot(k)-g*x)/w)<0||p>1||p+g>1)continue;r.push({clickable:e,distance:c})}for(s=0,n=_.cylinder.length;s<n;s++)if(_.cylinder[s]instanceof M.Cylinder){if(F.copy(_.cylinder[s]),F.applyMatrix4(t.matrixWorld),O.subVectors(F.c1,i.ray.origin),l=O.dot(F.direction),h=O.dot(i.ray.direction),0===(u=1-(o=E(i.ray.direction.dot(F.direction)))*o))continue;m=(o*h-l)/u,v=(h-o*l)/u,D.copy(F.direction).multiplyScalar(m).add(F.c1),k.copy(i.ray.direction).multiplyScalar(v).add(i.ray.origin),d=R.subVectors(D,k).lengthSq();var C=F.radius*F.radius;if(d<=C){if((p=o*(g=c=(f=(o*l-h)*(o*l-h)-u*(O.lengthSq()-l*l-C))<=0?Math.sqrt(d):(h-o*l-Math.sqrt(f))/u)-l)<0||p*p>F.lengthSq()||g<0)continue;r.push({clickable:e,distance:c})}}for(s=0,n=_.line.length;s<n;s+=2){D.copy(_.line[s]),D.applyMatrix4(t.matrixWorld),k.copy(_.line[s+1]),k.applyMatrix4(t.matrixWorld),R.subVectors(k,D);var S=R.lengthSq();R.normalize(),O.subVectors(D,i.ray.origin);var z=O.dot(R);h=O.dot(i.ray.direction),0!==(u=1-(o=E(i.ray.direction.dot(R)))*o)&&(m=(o*h-z)/u,v=(h-o*z)/u,D.add(R.multiplyScalar(m)),k.copy(i.ray.direction).multiplyScalar(v).add(i.ray.origin),(d=R.subVectors(k,D).lengthSq())<b&&m*m<S&&r.push({clickable:e,distance:v}))}for(s=0,n=_.sphere.length;s<n;s++)if(_.sphere[s]instanceof M.Sphere&&(L.copy(_.sphere[s]),L.applyMatrix4(t.matrixWorld),i.ray.isIntersectionSphere(L))){D.subVectors(L.center,i.ray.origin);var T=D.dot(i.ray.direction);if(f=T*T-(D.lengthSq()-L.radius*L.radius),T<0)return r;c=f<=0?T:T-Math.sqrt(f),r.push({clickable:e,distance:c})}return r}class UVMapping{}class Texture extends EventDispatcher{constructor(t,e){super(),this.id=B++,this.name="",this.image=t,this.mapping=new UVMapping,this.wrapS=c,this.wrapT=c,this.anisotropy=1,e?(this.format=v,this.type=g,this.premultiplyAlpha=!1,this.flipY=!1,this.unpackAlignment=1,this.magFilter=u,this.minFilter=u):(this.format=m,this.type=p,this.offset=new l.Vector2(0,0),this.repeat=new l.Vector2(1,1),this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.magFilter=d,this.minFilter=f),this.needsUpdate=!1,this.onUpdate=null}clone(t=new Texture){return t.image=this.image,t.mapping=this.mapping,t.wrapS=this.wrapS,t.wrapT=this.wrapT,t.magFilter=this.magFilter,t.minFilter=this.minFilter,t.anisotropy=this.anisotropy,t.format=this.format,t.type=this.type,t.offset.copy(this.offset),t.repeat.copy(this.repeat),t.premultiplyAlpha=this.premultiplyAlpha,t.flipY=this.flipY,t.unpackAlignment=this.unpackAlignment,t}dispose(){this.dispatchEvent({type:"dispose"})}}let B=0;class ImposterMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.ambient=new b.Color(1048575),this.emissive=new b.Color(0),this.imposter=!0,this.wrapAround=!1,this.wrapRGB=new l.Vector3(1,1,1),this.map=void 0,this.lightMap=null,this.specularMap=null,this.envMap=null,this.reflectivity=1,this.refractionRatio=.98,this.fog=!0,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.shading=o.SmoothShading,this.shaderID=null,this.vertexColors=r.NoColors,this.skinning=!1,this.setValues(t)}clone(t=new ImposterMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.ambient.copy(this.ambient),t.emissive.copy(this.emissive),t.wrapAround=this.wrapAround,t.wrapRGB.copy(this.wrapRGB),t.map=this.map,t.lightMap=this.lightMap,t.specularMap=this.specularMap,t.envMap=this.envMap,t.combine=this.combine,t.reflectivity=this.reflectivity,t.refractionRatio=this.refractionRatio,t.fog=this.fog,t.shading=this.shading,t.shaderID=this.shaderID,t.vertexColors=this.vertexColors,t.skinning=this.skinning,t.morphTargets=this.morphTargets,t.morphNormals=this.morphNormals,t}}class InstancedMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.ambient=new b.Color(1048575),this.emissive=new b.Color(0),this.wrapAround=!1,this.wrapRGB=new l.Vector3(1,1,1),this.map=void 0,this.lightMap=null,this.specularMap=null,this.envMap=null,this.reflectivity=1,this.refractionRatio=.98,this.fog=!0,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.shading=o.SmoothShading,this.shaderID="instanced",this.vertexColors=r.NoColors,this.skinning=!1,this.sphere=null,this.setValues(t)}clone(t=new InstancedMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.ambient.copy(this.ambient),t.emissive.copy(this.emissive),t.wrapAround=this.wrapAround,t.wrapRGB.copy(this.wrapRGB),t.map=this.map,t.lightMap=this.lightMap,t.specularMap=this.specularMap,t.envMap=this.envMap,t.combine=this.combine,t.reflectivity=this.reflectivity,t.refractionRatio=this.refractionRatio,t.fog=this.fog,t.shading=this.shading,t.shaderID=this.shaderID,t.vertexColors=this.vertexColors,t.skinning=this.skinning,t.morphTargets=this.morphTargets,t.morphNormals=this.morphNormals,t.sphere=this.sphere,t}}class MeshLambertMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.ambient=new b.Color(1048575),this.emissive=new b.Color(0),this.wrapAround=!1,this.wrapRGB=new l.Vector3(1,1,1),this.map=void 0,this.lightMap=null,this.specularMap=null,this.envMap=null,this.reflectivity=1,this.refractionRatio=.98,this.fog=!0,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.shading=o.SmoothShading,this.shaderID="lambert",this.vertexColors=r.NoColors,this.skinning=!1,this.setValues(t)}clone(t=new MeshLambertMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.ambient.copy(this.ambient),t.emissive.copy(this.emissive),t.wrapAround=this.wrapAround,t.wrapRGB.copy(this.wrapRGB),t.map=this.map,t.lightMap=this.lightMap,t.specularMap=this.specularMap,t.envMap=this.envMap,t.combine=this.combine,t.reflectivity=this.reflectivity,t.refractionRatio=this.refractionRatio,t.fog=this.fog,t.shading=this.shading,t.shaderID=this.shaderID,t.vertexColors=this.vertexColors,t.skinning=this.skinning,t.morphTargets=this.morphTargets,t.morphNormals=this.morphNormals,t}}class MeshDoubleLambertMaterial extends MeshLambertMaterial{constructor(t){super(t),this.shaderID="lambertdouble",this.side=a,this.outline=!1}clone(t=new MeshDoubleLambertMaterial){return super.clone.call(this,t),t}}class MeshOutlineMaterial extends Material{constructor(t){super(),t=t||{},this.fog=!0,this.shaderID="outline",this.wireframe=!1,this.outlineColor=b.CC.color(t.color||new b.Color(0,0,0)),this.outlineWidth=t.width||.1,this.outlinePushback=t.pushback||1,this.outlineMaxPixels=t.maxpixels||0}clone(t=new MeshOutlineMaterial){return super.clone.call(this,t),t.fog=this.fog,t.shaderID=this.shaderID,t.wireframe=this.wireframe,t.outlineColor=this.outlineColor,t.outlineWidth=this.outlineWidth,t.outlinePushback=this.outlinePushback,t.outlineMaxPixels=this.outlineMaxPixels,t}}class SphereImposterMaterial extends ImposterMaterial{constructor(t){super(t),this.shaderID="sphereimposter",this.setValues(t)}clone(t=new SphereImposterMaterial){return super.clone.call(this,t),t}}class SphereImposterOutlineMaterial extends ImposterMaterial{constructor(t){super(t),t=t||{},this.shaderID="sphereimposteroutline",this.outlineColor=b.CC.color(t.color||new b.Color(0,0,0)),this.outlineWidth=t.width||.1,this.outlinePushback=t.pushback||1,this.outlineMaxPixels=t.maxpixels||0,this.setValues(t)}clone(t=new SphereImposterOutlineMaterial){return super.clone.call(this,t),t.outlineColor=this.outlineColor,t.outlineWidth=this.outlineWidth,t.outlinePushback=this.outlinePushback,t.outlineMaxPixels=this.outlineMaxPixels,t}}class SpriteMaterial extends Material{constructor(t){super(),this.color=new b.Color(16777215),this.map=new Texture,this.useScreenCoordinates=!0,this.fog=!1,this.uvOffset=new l.Vector2(0,0),this.uvScale=new l.Vector2(1,1),this.depthTest=!this.useScreenCoordinates,this.sizeAttenuation=!this.useScreenCoordinates,this.screenOffset=this.screenOffset,this.scaleByViewPort=!this.sizeAttenuation,this.alignment=h.center.clone(),this.setValues(t),void 0===(t=t||{}).depthTest&&(this.depthTest=!this.useScreenCoordinates),void 0===t.sizeAttenuation&&(this.sizeAttenuation=!this.useScreenCoordinates),void 0===t.scaleByViewPort&&(this.scaleByViewPort=!this.sizeAttenuation)}clone(t=new SpriteMaterial){return super.clone.call(this,t),t.color.copy(this.color),t.map=this.map,t.useScreenCoordinates=this.useScreenCoordinates,t.screenOffset=this.screenOffset,t.sizeAttenuation=this.sizeAttenuation,t.scaleByViewport=this.scaleByViewPort,t.alignment.copy(this.alignment),t.uvOffset.copy(this.uvOffset),t}}class StickImposterMaterial extends ImposterMaterial{constructor(t){super(t),this.shaderID="stickimposter",this.setValues(t)}clone(t=new StickImposterMaterial){return super.clone.call(this,t),t}}class StickImposterOutlineMaterial extends ImposterMaterial{constructor(t={}){super(t),this.shaderID="stickimposteroutline",this.outlineColor=new b.Color(0,0,0),this.outlineWidth=.1,this.outlinePushback=1,this.outlineMaxPixels=0,t.color&&(this.outlineColor=b.CC.color(t.color)),t.width&&(this.outlineWidth=t.width),t.pushback&&(this.outlinePushback=t.pushback),t.maxpixels&&(this.outlineMaxPixels=t.maxpixels),this.setValues(t)}clone(t=new StickImposterOutlineMaterial){return super.clone.call(this,t),t.outlineColor=this.outlineColor,t.outlineWidth=this.outlineWidth,t.outlinePushback=this.outlinePushback,t.outlineMaxPixels=this.outlineMaxPixels,t}}class VolumetricMaterial extends Material{constructor(t){super(),this.transparent=!1,this.volumetric=!0,this.color=new b.Color(16777215),this.transferfn=null,this.map=void 0,this.extent=[],this.maxdepth=100,this.unit=0,this.texmatrix=null,this.transfermin=-1,this.transfermax=1,this.subsamples=5,this.shaderID="volumetric",this.side=s,this.setValues(t)}clone(t=new VolumetricMaterial){return super.clone.call(this,t),t.transparent=this.transparent,t.volumetric=this.volumetric,t.color=this.color,t.transferfn=this.transferfn,t.map=this.map,t.extent=this.extent,t.maxdepth=this.maxdepth,t.unit=this.unit,t.texmatrix=this.texmatrix,t.transfermin=this.transfermin,t.transfermax=this.transfermax,t.subsamples=this.subsamples,t.shaderID=this.shaderID,t.side=this.side,t}}var N;!function(t){t[t.LineStrip=0]="LineStrip",t[t.LinePieces=1]="LinePieces"}(N||(N={}));class Line extends Object3D{constructor(t,e=new LineBasicMaterial({color:16777215*Math.random()}),i=N.LineStrip){super(),this.geometry=t,this.material=e,this.type=i}clone(t=new Line(this.geometry,this.material,this.type)){return super.clone.call(this,t),t}}class Mesh extends Object3D{constructor(t,e){super(),this.geometry=t,this.material=e}clone(t){return void 0===t&&(t=new Mesh(this.geometry,this.material)),super.clone.call(this,t),t}}class Sprite extends Object3D{constructor(t=new SpriteMaterial){super(),this.material=t,this.rotation3d=this.rotation,this.rotation=0}updateMatrix(){this.matrix.setPosition(this.position),this.rotation3d.set(0,0,this.rotation),this.matrix.setRotationFromEuler(this.rotation3d),1===this.scale.x&&1===this.scale.y||this.matrix.scale(this.scale),this.matrixWorldNeedsUpdate=!0}clone(t=new Sprite(this.material)){return Object3D.prototype.clone.call(this,t),t}}const G={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3}},V={vertexShader:"uniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\n\nattribute vec3 position;\nattribute vec3 color;\n\nvarying vec3 vColor;\n\nvoid main() {\n\n    vColor = color;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    gl_Position = projectionMatrix * mvPosition;\n\n}".replace("#define GLSLIFY 1",""),fragmentShader:"uniform mat4 viewMatrix;\nuniform float opacity;\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\nvarying vec3 vColor;\n//DEFINEFRAGCOLOR\nvoid main() {\n    gl_FragColor = vec4( vColor, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n}".replace("#define GLSLIFY 1",""),uniforms:G},j={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},H={fragmentShader:"uniform mat4 viewMatrix;\nuniform float opacity;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\nvarying vec3 vLightFront;\nvarying vec3 vColor;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    gl_FragColor = vec4( vec3 ( 1.0 ), opacity );\n\n    #ifndef WIREFRAME\n    gl_FragColor.xyz *= vLightFront;\n    #endif\n\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    vColor *= shadowFactor;\n#endif\n    gl_FragColor = gl_FragColor * vec4( vColor, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n\n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 offset;\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\nattribute float radius;\n\nvarying vec3 vColor;\nvarying vec3 vLightFront;\n\nvoid main() {\n\n    vColor = color;\n\n    vec3 objectNormal = normal;\n    vec3 transformedNormal = normalMatrix * objectNormal;\n    vec4 mvPosition = modelViewMatrix * vec4( position * radius + offset, 1.0 );\n\n    vLightFront = vec3( 0.0 );\n\n    transformedNormal = normalize( transformedNormal );\n\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vec3 dirVector = normalize( lDirection.xyz );\n    float dotProduct = dot( transformedNormal, dirVector );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n\n    vLightFront += directionalLightColor[ 0 ] * directionalLightWeighting;\n\n    gl_Position = projectionMatrix * mvPosition;\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:j},W={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},q={fragmentShader:"uniform mat4 viewMatrix;\nuniform float opacity;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\nvarying vec3 vLightFront;\nvarying vec3 vColor;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    gl_FragColor = vec4( vec3 ( 1.0 ), opacity );\n\n    #ifndef WIREFRAME\n    gl_FragColor.xyz *= vLightFront;\n    #endif\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    vColor *= shadowFactor;\n#endif\n    gl_FragColor = gl_FragColor * vec4( vColor, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n\n}".replace("#define GLSLIFY 1",""),vertexShader:"\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec3 vColor;\nvarying vec3 vLightFront;\n\nvoid main() {\n\n    vColor = color;\n\n    vec3 objectNormal = normal;\n    vec3 transformedNormal = normalMatrix * objectNormal;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n\n    vLightFront = vec3( 0.0 );\n\n    transformedNormal = normalize( transformedNormal );\n\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vec3 dirVector = normalize( lDirection.xyz );\n    float dotProduct = dot( transformedNormal, dirVector );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n\n    vLightFront += directionalLightColor[ 0 ] * directionalLightWeighting;\n\n    gl_Position = projectionMatrix * mvPosition;\n}".replace("#define GLSLIFY 1",""),uniforms:W},Y={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},Z={fragmentShader:"\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\n\nuniform mat4 viewMatrix;\nuniform float opacity;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n\nvarying vec3 vLightFront;\nvarying vec3 vLightBack;\n\nvarying vec3 vColor;\n\n\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    gl_FragColor = vec4( vec3 ( 1.0 ), opacity );\n\n    #ifndef WIREFRAME\n    if ( gl_FrontFacing )\n       gl_FragColor.xyz *= vLightFront;\n    else\n       gl_FragColor.xyz *= vLightBack;\n    #endif\n\n    vec3 color = vColor;\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    color *= shadowFactor;\n#endif\n    gl_FragColor = gl_FragColor * vec4( color, opacity );\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n\n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec3 vColor;\nvarying vec3 vLightFront;\nvarying vec3 vLightBack;\n\nvoid main() {\n\n    vColor = color;\n\n    vec3 objectNormal = normal;\n    vec3 transformedNormal = normalMatrix * objectNormal;\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n\n    vLightFront = vec3( 0.0 );\n    vLightBack = vec3( 0.0 );\n\n    transformedNormal = normalize( transformedNormal );\n\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vec3 dirVector = normalize( lDirection.xyz );\n    float dotProduct = dot( transformedNormal, dirVector );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n    vec3 directionalLightWeightingBack = vec3( max( -dotProduct, 0.0 ) );\n\n    vLightFront += directionalLightColor[ 0 ] * directionalLightWeighting;\n    vLightBack += directionalLightColor[ 0 ] * directionalLightWeightingBack;\n\n    gl_Position = projectionMatrix * mvPosition;\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:Y},X={opacity:{type:"f",value:1},outlineColor:{type:"c",value:new b.Color(0,0,0)},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},outlineWidth:{type:"f",value:.1},outlinePushback:{type:"f",value:1},outlineMaxPixels:{type:"f",value:0}},K={fragmentShader:"\n\nuniform float opacity;\nuniform vec3 outlineColor;\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n    gl_FragColor = vec4( outlineColor, 1 );\n\n    float depth = gl_FragCoord.z / gl_FragCoord.w;\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );    \n}\n\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform float outlineWidth;\nuniform float outlinePushback;\nuniform float vWidth;\nuniform float vHeight;\nuniform float outlineMaxPixels;\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvoid main() {\n\n    vec4 norm = modelViewMatrix*vec4(normalize(normal),0.0);\n    vec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    mvPosition.xy += norm.xy*outlineWidth;\n    vec4 outpos = projectionMatrix * mvPosition;\n\n    if(outlineMaxPixels > 0.0) {\n        vec4 unadjusted = projectionMatrix*modelViewMatrix * vec4( position, 1.0 );\n        float w = outpos.w;\n        //normalize homogeneous coords\n        unadjusted /= unadjusted.w;\n        outpos /= outpos.w;\n        vec2 diff = outpos.xy-unadjusted.xy;\n        //put into pixels\n        diff.x *= vWidth;\n        diff.y *= vHeight;\n        if ( length(diff) > outlineMaxPixels) {\n            vec2 ndiff = normalize(diff)*outlineMaxPixels;\n            ndiff.x /= vWidth;\n            ndiff.y /= vHeight;\n            outpos.xy = unadjusted.xy;\n            outpos.xy += ndiff;\n        }\n        outpos *= w; //if I don't do this things blow up\n    }\n    gl_Position = outpos;\n    mvPosition.z -= outlinePushback; //go backwards in model space\n    vec4 pushpos = projectionMatrix*mvPosition; //project to get z in projection space, I'm probably missing some simple math to do the same thing..\n    gl_Position.z = gl_Position.w*pushpos.z/pushpos.w;\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:X},Q={fragmentShader:"uniform sampler2D colormap;\nvarying highp vec2 vTexCoords;\nuniform vec2 dimensions;\n//DEFINEFRAGCOLOR\nvoid main (void) {\n   gl_FragColor = texture2D(colormap, vTexCoords);\n\n   //gl_FragColor.g = gl_FragColor.b =  gl_FragColor.r; //debug shading \n}\n        ".replace("#define GLSLIFY 1",""),vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ".replace("#define GLSLIFY 1",""),uniforms:{}},$={fragmentShader:"uniform highp sampler2D colormap;\nvarying highp vec2 vTexCoords;\n\n\n// Basic FXAA implementation based on the code on geeks3d.com \n#define FXAA_REDUCE_MIN (1.0/ 128.0)\n#define FXAA_REDUCE_MUL (1.0 / 8.0)\n#define FXAA_SPAN_MAX 8.0\n\n\nvec4 applyFXAA(vec2 fragCoord, highp sampler2D tex)\n{\n    vec4 color;\n    ivec2 dim = textureSize(tex,0);\n    vec2 dimensions = vec2(float(dim.x),float(dim.y));\n    vec2 inverseVP = vec2(1.0 / dimensions.x, 1.0 / dimensions.y);\n    vec4 rgbNW = texture2D(tex, fragCoord + vec2(-1.0, -1.0) * inverseVP);\n    vec4 rgbNE = texture2D(tex, fragCoord + vec2(1.0, -1.0) * inverseVP);\n    vec4 rgbSW = texture2D(tex, fragCoord + vec2(-1.0, 1.0) * inverseVP);\n    vec4 rgbSE = texture2D(tex, fragCoord + vec2(1.0, 1.0) * inverseVP);\n    vec4 rgbM  = texture2D(tex, fragCoord );\n    vec3 luma = vec3(0.299, 0.587, 0.114);\n    float lumaNW = dot(rgbNW.xyz, luma);\n    float lumaNE = dot(rgbNE.xyz, luma);\n    float lumaSW = dot(rgbSW.xyz, luma);\n    float lumaSE = dot(rgbSE.xyz, luma);\n    float lumaM  = dot(rgbM.xyz,  luma);\n    float lumaMin = min(lumaM, min(min(lumaNW, lumaNE), min(lumaSW, lumaSE)));\n    float lumaMax = max(lumaM, max(max(lumaNW, lumaNE), max(lumaSW, lumaSE)));\n\n    vec2 dir;\n    dir.x = -((lumaNW + lumaNE) - (lumaSW + lumaSE));\n    dir.y =  ((lumaNW + lumaSW) - (lumaNE + lumaSE));\n\n    float dirReduce = max((lumaNW + lumaNE + lumaSW + lumaSE) *\n                        (0.25 * FXAA_REDUCE_MUL), FXAA_REDUCE_MIN);\n\n    float rcpDirMin = 1.0 / (min(abs(dir.x), abs(dir.y)) + dirReduce);\n    dir = min(vec2(FXAA_SPAN_MAX, FXAA_SPAN_MAX),\n            max(vec2(-FXAA_SPAN_MAX, -FXAA_SPAN_MAX),\n            dir * rcpDirMin)) * inverseVP;\n\n    vec4 rgbA = 0.5 * (\n        texture2D(tex, fragCoord + dir * (1.0 / 3.0 - 0.5)) +\n        texture2D(tex, fragCoord  + dir * (2.0 / 3.0 - 0.5)));\n    vec4 rgbB = rgbA * 0.5 + 0.25 * (\n        texture2D(tex, fragCoord  + dir * -0.5) +\n        texture2D(tex, fragCoord  + dir * 0.5));\n\n    float lumaB = dot(rgbB.xyz, luma);\n    if ((lumaB < lumaMin) || (lumaB > lumaMax))\n        color = rgbA;\n    else\n        color = rgbB;\n\n    return color;\n}\n\n\n//DEFINEFRAGCOLOR\nvoid main (void) {\n    ivec2 dim = textureSize(colormap,0);\n\n  gl_FragColor = applyFXAA(vTexCoords, colormap);\n}\n        ".replace("#define GLSLIFY 1",""),vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ".replace("#define GLSLIFY 1",""),uniforms:{}},J={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},tt={vertexShader:"uniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\n\nvarying vec2 mapping;\nvarying vec3 vColor;\nvarying float rval;\nvarying vec3 vLight;\nvarying vec3 center;\n\nvoid main() {\n\n    vColor = 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rsqr = rval*rval;\n    if(lensqr > rsqr)\n       discard;\n    float z = sqrt(rsqr-lensqr);\n    vec3 cameraPos = center+ vec3(mapping.x,mapping.y,z);\n    vec4 clipPos = projectionMatrix * vec4(cameraPos, 1.0);\n    float ndcDepth = clipPos.z / clipPos.w;\n    gl_FragDepthEXT = ((gl_DepthRange.diff * ndcDepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n    vec3 norm = normalize(vec3(mapping.x,mapping.y,z));\n    float dotProduct = dot( norm, vLight );\n    vec3 directionalLightWeighting = vec3( max( dotProduct, 0.0 ) );\n    vec3 vLight = directionalLightColor[ 0 ] * directionalLightWeighting;\n    vec3 color = vLight*vColor;\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    color *= shadowFactor;\n#endif    \n    gl_FragColor = vec4(color, opacity*opacity );\n    float fogFactor = smoothstep( fogNear, fogFar, gl_FragDepthEXT/gl_FragCoord.w );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:J},et={opacity:{type:"f",value:1},outlineColor:{type:"c",value:new b.Color(0,0,0)},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},outlineWidth:{type:"f",value:.1},outlinePushback:{type:"f",value:1},outlineMaxPixels:{type:"f",value:0}},it={fragmentShader:"\n\nuniform float opacity;\nuniform vec3 outlineColor;\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\nuniform mat4 projectionMatrix;\nvarying vec2 mapping;\nvarying float rval;\nvarying vec3 center;\n\nuniform float outlinePushback;\n\n//DEFINEFRAGCOLOR\n\nvoid main() {\n    float lensqr = dot(mapping,mapping);\n    float rsqr = rval*rval;\n    if(lensqr > rsqr)\n       discard;\n    float z = sqrt(rsqr-lensqr);\n    vec3 cameraPos = center+ vec3(mapping.x,mapping.y,z-outlinePushback);\n    vec4 clipPos = projectionMatrix * 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 gl_Position = projPosition+vec4(adjust.xy,0.0,0.0);\n\n    if(outlineMaxPixels > 0.0) {\n        vec4 unadjusted = projectionMatrix*vec4(center.x+normal.x, center.y,center.z,1.0); \n        vec4 ccoord = projectionMatrix*vec4(center.xyz,1.0);\n        adjust = projectionMatrix* vec4(center.x+norm.x,center.y,center.z,1.0); \n        //subtract center \n        unadjusted.xyz -= ccoord.xyz;\n        adjust.xyz -= ccoord.xyz;\n        unadjusted /= unadjusted.w;\n        adjust /= adjust.w;\n        float diff = abs(adjust.x-unadjusted.x);\n        diff *= vWidth;\n        if(diff > outlineMaxPixels) {\n            \n            float fixlen = abs(unadjusted.x) + outlineMaxPixels/vWidth;\n            //adjsut reval by ratio of lengths\n            rval *= fixlen/abs(adjust.x);\n        }\n\n    }\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:et},rt={fragmentShader:"\n\nuniform vec3 color;\nuniform sampler2D map;\nuniform float opacity;\n\nuniform int fogType;\nuniform vec3 fogColor;\nuniform float fogDensity;\nuniform float fogNear;\nuniform float fogFar;\nuniform float alphaTest;\n\nvarying vec2 vUV;\n//DEFINEFRAGCOLOR\n\nvoid main() {\n\n    vec4 texture = texture2D(map, vUV);\n\n    if (texture.a <= alphaTest) discard;\n\n    gl_FragColor = vec4(color * texture.xyz, texture.a * opacity);\n\n    if (fogType > 0) {\n\n        float depth = gl_FragCoord.z / gl_FragCoord.w;\n        float fogFactor = 0.0;\n\n        if (fogType == 1) {\n            fogFactor = smoothstep(fogNear, fogFar, depth);\n        }\n\n        else {\n            const float LOG2 = 1.442695;\n            float fogFactor = exp2(- fogDensity * fogDensity * depth * depth * LOG2);\n            fogFactor = 1.0 - clamp(fogFactor, 0.0, 1.0);\n        }\n\n        gl_FragColor = mix(gl_FragColor, vec4(fogColor, gl_FragColor.w), fogFactor);\n\n    }\n}\n\n".replace("#define GLSLIFY 1",""),vertexShader:"\n\nuniform int useScreenCoordinates;\nuniform vec3 screenPosition;\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform float rotation;\nuniform vec2 scale;\nuniform vec2 alignment;\nuniform vec2 uvOffset;\nuniform vec2 uvScale;\n\nattribute vec2 position;\nattribute vec2 uv;\n\nvarying vec2 vUV;\n\nvoid main() {\n\n    vUV = uvOffset + uv * uvScale;\n\n    vec2 alignedPosition = position + alignment;\n\n    vec2 rotatedPosition;\n    rotatedPosition.x = ( cos(rotation) * alignedPosition.x - sin(rotation) * alignedPosition.y ) * scale.x;\n    rotatedPosition.y = ( sin(rotation) * alignedPosition.x + cos(rotation) * alignedPosition.y ) * scale.y;\n\n    vec4 finalPosition;\n\n    if(useScreenCoordinates != 0) {\n        finalPosition = vec4(screenPosition.xy + rotatedPosition, screenPosition.z, 1.0);\n    }\n\n    else {\n        finalPosition = projectionMatrix * modelViewMatrix * vec4(0.0, 0.0, 0.0, 1.0); finalPosition /= finalPosition.w;\n        finalPosition.xy += rotatedPosition; \n    }\n\n    gl_Position = finalPosition;\n\n}\n\n".replace("#define GLSLIFY 1",""),uniforms:{}},st={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},directionalLightColor:{type:"fv",value:[]},directionalLightDirection:{type:"fv",value:[]}},nt="uniform float opacity;\nuniform mat4 projectionMatrix;\n\nuniform vec3 fogColor;\nuniform float fogNear;\nuniform float fogFar;\n\nvarying vec3 vLight;\nvarying vec3 vColor;\nvarying vec3 cposition;\nvarying vec3 p1;\nvarying vec3 p2;\nvarying float r;\n\n#ifdef SHADED\nuniform highp sampler2D shading;\n#endif\n\n//DEFINEFRAGCOLOR\n\n//cylinder-ray intersection testing taken from http://mrl.nyu.edu/~dzorin/cg05/lecture12.pdf\n//also useful: http://stackoverflow.com/questions/9595300/cylinder-impostor-in-glsl\n//with a bit more care (caps) this could be a general cylinder imposter (see also outline)\nvoid main() {\n    vec3 color = abs(vColor);\n    vec3 pos = cposition;\n    vec3 p = pos; //ray point\n    vec3 v = vec3(0.0,0.0,-1.0); //ray normal - orthographic\n    if(projectionMatrix[3][3] == 0.0) v = normalize(pos); //ray normal - perspective\n    vec3 pa = p1; //cyl start\n    vec3 va = normalize(p2-p1); //cyl norm\n    vec3 tmp1 = v-(dot(v,va)*va);\n    vec3 deltap = p-pa;\n    float A = dot(tmp1,tmp1);\n    if(A == 0.0) discard;\n    vec3 tmp2 = deltap-(dot(deltap,va)*va);\n    float B = 2.0*dot(tmp1, tmp2);\n    float C = dot(tmp2,tmp2)-r*r;\n//quadratic equation!\n    float det = (B*B) - (4.0*A*C);\n    if(det < 0.0) discard;\n    float sqrtDet = sqrt(det);\n    float posT = (-B+sqrtDet)/(2.0*A);\n    float negT = (-B-sqrtDet)/(2.0*A);\n    float intersectionT = min(posT,negT);\n    vec3 qi = p+v*intersectionT;\n    float dotp1 = dot(va,qi-p1);\n    float dotp2 = dot(va,qi-p2);\n    vec3 norm;\n    if( dotp1 < 0.0 || dotp2 > 0.0) { //(p-c)^2 + 2(p-c)vt +v^2+t^2 - r^2 = 0\n       vec3 cp;\n       if( dotp1 < 0.0) {  \n//        if(vColor.x < 0.0 ) discard; //color sign bit indicates if we should cap or not\n        cp = p1;\n       } else {\n//          if(vColor.y < 0.0 ) discard;\n          cp = p2;\n       }\n       vec3 diff = p-cp;\n       A = dot(v,v);\n       B = dot(diff,v)*2.0;\n       C = dot(diff,diff)-r*r;\n       det = (B*B) - (4.0*C);\n       if(det < 0.0) discard;\n       sqrtDet = sqrt(det);\n       posT = (-B+sqrtDet)/(2.0);\n       negT = (-B-sqrtDet)/(2.0);\n       float t = min(posT,negT);\n       qi = p+v*t; \n       norm = normalize(qi-cp); \n    } else {\n       norm = normalize(qi-(dotp1*va + p1));\n    }\n    vec4 clipPos = projectionMatrix * vec4(qi, 1.0);\n    float ndcDepth = clipPos.z / clipPos.w;\n    float depth = ((gl_DepthRange.diff * ndcDepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n    gl_FragDepthEXT = depth;",at={fragmentShader:[nt,"    float dotProduct = dot( norm, vLight );\n    vec3 light = vec3( max( dotProduct, 0.0 ) );\n    color *= light;\n#ifdef SHADED\n    ivec2 dim = textureSize(shading,0);\n    float shadowFactor = texture2D(shading,vec2(gl_FragCoord.x/float(dim.x),gl_FragCoord.y/float(dim.y))).r;\n    color *= shadowFactor;\n#endif    \n    gl_FragColor = vec4(color, opacity*opacity );\n    float fogFactor = smoothstep( fogNear, fogFar, depth );\n    gl_FragColor = mix( gl_FragColor, vec4( fogColor, gl_FragColor.w ), fogFactor );\n}"].join("\n"),vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\nuniform mat3 normalMatrix;\nuniform vec3 directionalLightColor[ 1 ];\nuniform vec3 directionalLightDirection[ 1 ];\n\nattribute vec3 position;\nattribute vec3 normal;\nattribute vec3 color;\nattribute float radius;\n\nvarying vec3 vColor;\nvarying vec3 vLight;\nvarying vec3 cposition;\nvarying vec3 p1;\nvarying vec3 p2;\nvarying float r;\n\nvoid main() {\n\n    vColor = color; vColor.z = abs(vColor.z); //z indicates which vertex and so would vary\n    r = abs(radius);\n    vec4 to = modelViewMatrix*vec4(normal, 1.0); //normal is other point of cylinder\n    vec4 pt = modelViewMatrix*vec4(position, 1.0);\n    vec4 mvPosition = pt;\n    p1 = pt.xyz; p2 = to.xyz;\n    vec3 norm = to.xyz-pt.xyz;\n    float mult = 1.1; //slop to account for perspective of sphere\n    if(length(p1) > length(p2)) { //billboard at level of closest point\n       mvPosition = to;\n    }\n    vec3 n = normalize(mvPosition.xyz);\n    bool isperspective = (projectionMatrix[3][3] == 0.0);\n//intersect with the plane defined by the camera looking at the billboard point\n    if(color.z >= 0.0) { //p1\n       if(isperspective) { //perspective\n         vec3 pnorm = normalize(p1);\n         float t = dot(mvPosition.xyz-p1,n)/dot(pnorm,n);\n         mvPosition.xyz = p1+t*pnorm; \n       } else { //orthographic\n         mvPosition.xyz = p1;\n       }\n    } else {\n      if(isperspective) { //perspective\n         vec3 pnorm = normalize(p2);\n         float t = dot(mvPosition.xyz-p2,n)/dot(pnorm,n);\n         mvPosition.xyz = p2+t*pnorm;\n       } else { //orthographic\n         mvPosition.xyz = p2;\n       } \n       mult *= -1.0;\n    }\n\n    if(isperspective) { //perspective\n      vec3 cr = normalize(cross(mvPosition.xyz,norm))*radius;\n      vec3 doublecr = normalize(cross(mvPosition.xyz,cr))*radius;\n      mvPosition.xyz +=  mult*(cr + doublecr).xyz;\n    } else {\n      vec3 cr = normalize(cross(vec3(0.0,0.0,-1.0),norm))*radius;\n      vec3 doublecr = normalize(cross(vec3(0.0,0.0,-1.0),cr))*radius;\n      mvPosition.xyz +=  mult*(cr + doublecr).xyz;     \n    }\n\n    cposition = mvPosition.xyz;\n    gl_Position = projectionMatrix * mvPosition;\n    vec4 lDirection = viewMatrix * vec4( directionalLightDirection[ 0 ], 0.0 );\n    vLight = normalize( lDirection.xyz )*directionalLightColor[0]; //not really sure this is right, but color is always white so..\n}\n\n",uniforms:st},ot={fragmentShader:nt+"gl_FragColor = vec4(color,1.0);}",vertexShader:"\n\nuniform mat4 modelViewMatrix;\nuniform mat4 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//perspective\n        vec4 midbefore = pt;\n        if(length(to.xyz) < length(pt)) {\n            midbefore = to;\n        }\n        vec4 midafter = midbefore;\n        midafter.xyz += normalize(midbefore.xyz)*outlinePushback;\n\n        to.xyz += normalize(to.xyz)*outlinePushback;\n        pt.xyz += normalize(pt.xyz)*outlinePushback;\n\n        //figure out a scaling factor for radius to account for perspective setback\n        vec4 midbeforer = vec4(midbefore.x+rad,midbefore.y, midbefore.z, midbefore.w);\n        vec4 midafterr = vec4(midafter.x+rad,midafter.y, midafter.z, midafter.w);\n\n        vec4 mb = projectionMatrix*midbefore;\n        vec4 mbr = projectionMatrix*midbeforer;\n        vec4 ma = projectionMatrix*midafter;\n        vec4 mar = projectionMatrix*midafterr;\n        mb /= mb.w;\n        mbr /= mbr.w;\n        ma /= ma.w;\n        mar /= mar.w;\n        scale = abs((mbr.x-mb.x)/(mar.x-ma.x));\n        rad *= scale;\n        r = abs(rad);\n    }\n    vec4 mvPosition = pt;\n    p1 = pt.xyz; p2 = to.xyz;\n    vec3 norm = to.xyz-pt.xyz;\n    float mult = 1.1; //slop to account for perspective of sphere\n    if(length(p1) > length(p2)) { //billboard at level of closest point\n       mvPosition = to;\n    }\n\n    vec3 n = normalize(mvPosition.xyz);\n//intersect with the plane defined by the camera looking at the billboard point\n    if(color.z >= 0.0) { //p1\n       vec3 pnorm = normalize(p1);\n       float t = dot(mvPosition.xyz-p1,n)/dot(pnorm,n);\n       mvPosition.xyz = p1+t*pnorm;\n    } else {\n       vec3 pnorm = normalize(p2);\n       float t = dot(mvPosition.xyz-p2,n)/dot(pnorm,n);\n       mvPosition.xyz = p2+t*pnorm;\n       mult *= -1.0;\n    }\n\n    if(outlineMaxPixels > 0.0) {\n        vec4 cpos = mvPosition;\n        vec4 unadjusted = projectionMatrix*vec4(cpos.x+abs(scale*radius), cpos.y,cpos.z,cpos.w); \n        vec4 ccoord = projectionMatrix*cpos;\n        vec4 adjust = projectionMatrix*vec4(cpos.x+r,cpos.y,cpos.z,cpos.w); \n        unadjusted /= unadjusted.w;\n        adjust /= adjust.w;\n        unadjusted.xyz -= ccoord.xyz/ccoord.w;\n        adjust.xyz -= ccoord.xyz/ccoord.w;\n        float diff = abs(adjust.x-unadjusted.x);\n        diff *= vWidth; //this should now be in pixels\n        if(diff > outlineMaxPixels) {\n            float fixlen = abs(unadjusted.x) + outlineMaxPixels/vWidth; \n            vec4 pcoord = ccoord;\n            pcoord.x += fixlen*pcoord.w;\n            vec4 altc = projinv*pcoord;\n            r= abs(altc.x-cpos.x);\n        }\n    }\n\n    vec3 cr = normalize(cross(mvPosition.xyz,norm))*rad;\n    vec3 doublecr = normalize(cross(mvPosition.xyz,cr))*rad;\n    mvPosition.xy +=  mult*(cr + doublecr).xy;\n    cposition = mvPosition.xyz;\n    gl_Position = projectionMatrix * mvPosition;\n    vLight = vec3(1.0,1.0,1.0);\n}\n\n",uniforms:{opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},outlineColor:{type:"c",value:new b.Color(0,0,0)},outlineWidth:{type:"f",value:.1},outlinePushback:{type:"f",value:1},outlineMaxPixels:{type:"f",value:0},projinv:{type:"mat4",value:[]}}},lt={opacity:{type:"f",value:1},fogColor:{type:"c",value:new b.Color(1,1,1)},fogNear:{type:"f",value:1},fogFar:{type:"f",value:2e3},data:{type:"i",value:3},colormap:{type:"i",value:4},depthmap:{type:"i",value:5},step:{type:"f",value:1},maxdepth:{type:"f",value:100},subsamples:{type:"f",value:5},textmat:{type:"mat4",value:[]},projinv:{type:"mat4",value:[]},transfermin:{type:"f",value:-.2},transfermax:{type:"f",value:.2}},ht={fragmentShader:"\nuniform highp sampler3D data;\nuniform highp sampler2D colormap;\nuniform highp sampler2D depthmap;\n\n\nuniform mat4 textmat;\nuniform mat4 projinv;\nuniform mat4 projectionMatrix;\n\nuniform float step;\nuniform float subsamples;\nuniform float maxdepth;\nuniform float transfermin;\nuniform float transfermax;\nin  vec4 mvPosition;\nout vec4 color;\nvoid main(void) {\n\n   vec4 pos = mvPosition;\n   bool seengood = false;\n   float i = 0.0;\n   color = vec4(1,1,1,0);\n   float increment = 1.0/subsamples;\n   float maxsteps = (maxdepth*subsamples/step);\n//there's probably a better way to do this..\n//calculate farthest possible point in model coordinates\n   vec4 maxpos = vec4(pos.x,pos.y,pos.z-maxdepth,1.0);\n// convert to projection\n   maxpos = projectionMatrix*maxpos;\n   vec4 startp = projectionMatrix*pos;\n// homogonize\n   maxpos /= maxpos.w;\n   startp /= startp.w;\n//take x,y from start and z from max\n   maxpos = vec4(startp.x,startp.y,maxpos.z,1.0);\n//convert back to model space\n   maxpos = projinv*maxpos;\n   maxpos /= maxpos.w;\n   float incr = step/subsamples;\n//get depth from depthmap\n//startp is apparently [-1,1]\n   vec2 tpos = startp.xy/2.0+0.5;\n   float depth = texture(depthmap, tpos).r;\n//compute vector between start and end\n   vec4 direction = maxpos-pos;\n   for( i = 0.0; i <= maxsteps; i++) {\n      vec4 pt = (pos+(i/maxsteps)*direction);\n      vec4 ppt = projectionMatrix*pt;\n      float ptdepth = ppt.z/ppt.w;\n      ptdepth = ((gl_DepthRange.diff * ptdepth) + gl_DepthRange.near + gl_DepthRange.far) / 2.0;\n      if(ptdepth > depth) break;\n      pt = textmat*pt;\n//       pt /= pt.w;\n      if(pt.x >= -0.01 && pt.y >= -0.01 && pt.z >= -0.01 && pt.x <= 1.01 && pt.y <= 1.01 && pt.z <= 1.01) {\n         seengood = true;\n      } else if(seengood) {\n         break;\n      }\n      if( pt.x < -0.01 || pt.x > 1.01 || pt.y < -0.01 || pt.y > 1.01 || pt.z < -0.01 || pt.z > 1.01  ){\n          color.a = 0.0;\n          continue;\n      }\n      else {\n         float val = texture(data, pt.zyx).r;\n         if(isinf(val)) continue; //masked out\n         float cval = (val-transfermin)/(transfermax-transfermin); //scale to texture 0-1 range\n         vec4 val_color = texture(colormap, vec2(cval,0.5));\n         color.rgb = color.rgb*color.a + (1.0-color.a)*val_color.a*val_color.rgb;\n         color.a += (1.0 - color.a) * val_color.a; \n         if(color.a > 0.0) color.rgb /= color.a;\n//          color = vec4(pt.x, pt.y, pt.z, 1.0);\n      }\n//       color = vec4(pt.x, pt.y, pt.z, 0.0)\n    }\n}\n\n        ".replace("#define GLSLIFY 1",""),vertexShader:"uniform mat4 modelViewMatrix;\nuniform mat4 projectionMatrix;\nuniform mat4 viewMatrix;\n\nin vec3 position;\nout vec4 mvPosition;\nvoid main() {\n\n    mvPosition = modelViewMatrix * vec4( position, 1.0 );\n    gl_Position = projectionMatrix*mvPosition;\n}\n".replace("#define GLSLIFY 1",""),uniforms:lt},ct={basic:V,blur:{fragmentShader:"const float INV_TOTAL_SAMPLES_FACTOR = 1.0 / 9.0;\nuniform highp sampler2D inTex;\nvarying highp vec2 vTexCoords;\n    \nvoid main() {\n \n vec2 texelSize = 1.0 / vec2(textureSize(inTex,0));\n float blurred_visibility_factor = 0.0f;\n for (int t = -1; t <= 1; ++t) {\n  for (int s = -1; s <= 1; ++s) {\n   vec2 offset = vec2(float(s), float(t)) * texelSize;\n   blurred_visibility_factor += texture2D(inTex, vTexCoords + offset).r;\n  }\n }\n    \n gl_FragDepthEXT = blurred_visibility_factor * INV_TOTAL_SAMPLES_FACTOR;\n \n}",vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ",uniforms:{}},instanced:H,lambert:q,lambertdouble:Z,outline:K,screen:Q,screenaa:$,ssao:{fragmentShader:"uniform sampler2D depthmap;\nvarying highp vec2 vTexCoords;\nuniform float vWidth;\nuniform float vHeight;\nuniform float total_strength;\nuniform float radius;\nuniform mat4 projinv;\nuniform mat4 projectionMatrix;\n\n//code for pseudorandom vector from chatgpt\nfloat hash(vec3 p) {\n    p = fract(p * vec3(0.1031, 0.1030, 0.0973));\n    p += dot(p, p.yzx + 33.33);\n    return fract((p.x + p.y) * p.z);\n}\n\n// Generate a pseudorandom vec3 from a seed vec3\nvec3 pseudorandomVec3(vec3 seed) {\n    vec3 randomVec;\n    randomVec.x = hash(seed);\n    randomVec.y = hash(seed + vec3(1.0, 0.0, 17.1));\n    randomVec.z = hash(seed + vec3(0.0, 13.23, 0.0));\n    return randomVec;\n}\n\nvoid main(void)\n{   \n   const float base = 0.2;\n   const float area = 0.75;\n   const int cycles = 1;\n\n   const int samples = 64;\n   vec3 sample_sphere[64] = vec3[](\n      vec3(0.091258,-0.510164,0.000000),\n      vec3(-0.204347,-0.872967,0.187199),\n      vec3(0.009690,-0.263696,-0.110414),\n      vec3(0.175208,-0.563987,0.228527),\n      vec3(-0.001824,-0.003113,-0.000323),\n      vec3(0.411134,-0.719869,-0.261530),\n      vec3(-0.074272,-0.377368,0.276290),\n      vec3(-0.147773,-0.381587,-0.284529),\n      vec3(0.173317,-0.199635,0.063295),\n      vec3(-0.186452,-0.199460,0.076965),\n      vec3(0.143985,-0.308160,-0.307687),\n      vec3(0.053194,-0.148286,0.169589),\n      vec3(-0.547656,-0.486476,-0.317378),\n      vec3(0.020804,-0.015092,-0.004574),\n      vec3(-0.038006,-0.043165,0.054059),\n      vec3(-0.094795,-0.443908,-0.731525),\n      vec3(0.547552,-0.396466,0.461477),\n      vec3(-0.176886,-0.089989,0.007315),\n      vec3(0.074401,-0.048840,-0.074039),\n      vec3(-0.008240,-0.075697,0.178197),\n      vec3(-0.307880,-0.185053,-0.368943),\n      vec3(0.309520,-0.108483,0.041646),\n      vec3(-0.773478,-0.292946,0.538166),\n      vec3(0.184487,-0.231594,-0.820065),\n      vec3(0.207318,-0.100531,0.361797),\n      vec3(-0.173306,-0.037737,-0.055289),\n      vec3(0.548102,-0.105342,-0.253237),\n      vec3(-0.119342,-0.043907,0.285162),\n      vec3(-0.270247,-0.087861,-0.751357),\n      vec3(0.449312,-0.039777,0.236146),\n      vec3(-0.743773,-0.036095,0.196056),\n      vec3(0.148819,-0.004300,-0.231448),\n      vec3(0.008773,0.000809,0.051047),\n      vec3(-0.461467,0.027390,-0.357386),\n      vec3(0.169626,0.013338,-0.014053),\n      vec3(-0.043786,0.007095,0.047331),\n      vec3(0.004821,0.140371,-0.988260),\n      vec3(0.092402,0.023994,0.101860),\n      vec3(-0.295335,0.061530,-0.027372),\n      vec3(0.024903,0.007537,-0.018901),\n      vec3(-0.081463,0.125402,0.447794),\n      vec3(-0.397119,0.231805,-0.631062),\n      vec3(0.163853,0.059014,0.044905),\n      vec3(-0.495220,0.214357,0.254139),\n      vec3(0.306123,0.373687,-0.825715),\n      vec3(0.021665,0.026737,0.053220),\n      vec3(-0.208231,0.117129,-0.098685),\n      vec3(0.139749,0.080968,-0.043086),\n      vec3(-0.153599,0.182814,0.262090),\n      vec3(-0.159673,0.496777,-0.743568),\n      vec3(0.134797,0.117152,0.095753),\n      vec3(-0.155626,0.120533,0.019395),\n      vec3(0.042311,0.054462,-0.049709),\n      vec3(0.001257,0.031288,0.034468),\n      vec3(-0.002271,0.003199,-0.002304),\n      vec3(0.662104,0.717307,0.033854),\n      vec3(-0.373100,0.576021,0.308274),\n      vec3(0.024233,0.231316,-0.173688),\n      vec3(0.161311,0.420217,0.234273),\n      vec3(-0.045248,0.078031,-0.010411),\n      vec3(0.167453,0.376942,-0.094872),\n      vec3(-0.056194,0.433247,0.173218),\n      vec3(-0.016224,0.123149,-0.035569),\n      vec3(0.067127,0.407641,0.028479)\n   );\n\n   float depth = texture2D(depthmap, vTexCoords).r;\n   if(depth == 1.0) {\n      discard;\n   }\n\n   vec4 screen_position = vec4(vTexCoords, depth,1.0);\n   vec4 pos = projinv*screen_position;\n   pos /= pos.w;\n   vec3 position = pos.xyz;\n\n   //approximate the normal from the depth map; I find this simpler\n   //than trying to recompute the exact normal within every possible object shader\n\n   //pixel offset positions in screen space\n   ivec2 dim = textureSize(depthmap,0);\n   vec2 offset1 = vec2(0.0,1.0/float(dim.y));\n   vec2 offset2 = vec2(1.0/float(dim.x),0.0);\n   float depth1 = texture2D(depthmap, vTexCoords + offset1).r;\n   float depth2 = texture2D(depthmap, vTexCoords + offset2).r;\n   \n   vec3 p1 = vec3(screen_position.xy+offset1, depth1 - depth);\n   vec3 p2 = vec3(screen_position.xy+offset2, depth2 - depth);\n\n   //convert to model space\n   vec4 pos1 = projinv*vec4(p1,1);\n   pos1 /= pos1.w;\n   vec4 pos2 = projinv*vec4(p2,1);\n   pos2 /= pos2.w;\n\n   vec3 normal = normalize(cross(pos1.xyz-position, pos2.xyz-position)); //model normal, important we normalize in model space\n\n   //pseudo random number\n\n   float occlusion = 0.0;\n   for(int c = 0; c < cycles; c++) {\n   vec3 random = normalize(pseudorandomVec3(position+float(c)));\n   for(int i=0; i < samples; i++) {\n\n      vec3 ray = radius * reflect(sample_sphere[i],random);\n      vec3 hemi_ray = position + sign(dot(ray,normal)) * ray; //model space\n      vec4 hemi_screen = projectionMatrix*vec4(hemi_ray,1.0);\n      hemi_screen /= hemi_screen.w;\n      \n      float occ_depth = texture2D(depthmap, clamp(hemi_screen.xy,0.0,1.0)).r;\n      float difference = hemi_screen.z - occ_depth;\n      \n      occlusion += step(0.0, difference) * (1.0-smoothstep(0.0, area, difference));\n   }\n   }\n   float ao = 1.0 - total_strength * occlusion * (1.0 / float(cycles*samples));\n   gl_FragDepthEXT = clamp(ao+base,0.0,1.0);\n\n}",vertexShader:"attribute vec2 vertexPosition;\nvarying highp vec2 vTexCoords;\nconst vec2 scale = vec2(0.5, 0.5);\n\nvoid main() {\n   vTexCoords  = vertexPosition * scale + scale; // scale vertex attribute to [0,1] range\n   gl_Position = vec4(vertexPosition, 0.0, 1.0);\n}\n        ",uniforms:{total_strength:{type:"f",value:1},radius:{type:"f",value:5},projinv:{type:"mat4",value:[]}}},sphereimposter:tt,sphereimposteroutline:it,sprite:rt,stickimposter:at,stickimposteroutline:ot,volumetric:ht};function dt(t){let e={};for(const r in t){e[r]={},e[r].type=t[r].type;var i=t[r].value;i instanceof b.Color?e[r].value=i.clone():"number"==typeof i?e[r].value=i:i instanceof Array?e[r].value=[]:console.error("Error copying shader uniforms from ShaderLib: unknown type for uniform")}return e}const ut={clone:dt};class Camera extends Object3D{constructor(t=50,e=1,i=.1,r=2e3,s=!1){super(),this.projectionMatrix=new l.Matrix4,this.projectionMatrixInverse=new l.Matrix4,this.matrixWorldInverse=new l.Matrix4,this.fov=t,this.aspect=e,this.near=i,this.far=r;var n=this.position.z;this.right=n*Math.tan(Math.PI/180*t),this.left=-this.right,this.top=this.right/this.aspect,this.bottom=-this.top,this.ortho=!!s,this.updateProjectionMatrix()}lookAt(t){this.matrix.lookAt(this.position,t,this.up),this.rotationAutoUpdate&&(!1===this.useQuaternion&&this.rotation instanceof l.Vector3?this.rotation.setEulerFromRotationMatrix(this.matrix,this.eulerOrder):console.error("Unimplemented math operation."))}updateProjectionMatrix(){this.ortho?this.projectionMatrix.makeOrthographic(this.left,this.right,this.top,this.bottom,this.near,this.far):this.projectionMatrix.makePerspective(this.fov,this.aspect,this.near,this.far),this.projectionMatrixInverse.getInverse(this.projectionMatrix)}}class Fog{constructor(t,e=1,i=1e3){this.name="",this.color=new b.Color(t),this.near=e,this.far=i}clone(){return new Fog(this.color.getHex(),this.near,this.far)}}class SpritePlugin{constructor(){this.sprite={vertices:null,faces:null,vertexBuffer:null,elementBuffer:null,program:null,attributes:{},uniforms:null}}init(t){this.gl=t.context,this.renderer=t,this.precision=t.getPrecision(),this.sprite.vertices=new Float32Array(16),this.sprite.faces=new Uint16Array(6);var e=0;this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=-1,this.sprite.vertices[e++]=1,this.sprite.vertices[e++]=0,this.sprite.vertices[e++]=1,e=0,this.sprite.faces[e++]=0,this.sprite.faces[e++]=1,this.sprite.faces[e++]=2,this.sprite.faces[e++]=0,this.sprite.faces[e++]=2,this.sprite.faces[e++]=3,this.sprite.vertexBuffer=this.gl.createBuffer(),this.sprite.elementBuffer=this.gl.createBuffer(),this.gl.bindBuffer(this.gl.ARRAY_BUFFER,this.sprite.vertexBuffer),this.gl.bufferData(this.gl.ARRAY_BUFFER,this.sprite.vertices,this.gl.STATIC_DRAW),this.gl.bindBuffer(this.gl.ELEMENT_ARRAY_BUFFER,this.sprite.elementBuffer),this.gl.bufferData(this.gl.ELEMENT_ARRAY_BUFFER,this.sprite.faces,this.gl.STATIC_DRAW),this.sprite.program=this.createProgram(ct.sprite,this.precision||1),this.sprite.attributes={};const i={};this.sprite.attributes.position=this.gl.getAttribLocation(this.sprite.program,"position"),this.sprite.attributes.uv=this.gl.getAttribLocation(this.sprite.program,"uv"),i.uvOffset=this.gl.getUniformLocation(this.sprite.program,"uvOffset"),i.uvScale=this.gl.getUniformLocation(this.sprite.program,"uvScale"),i.rotation=this.gl.getUniformLocation(this.sprite.program,"rotation"),i.scale=this.gl.getUniformLocation(this.sprite.program,"scale"),i.alignment=this.gl.getUniformLocation(this.sprite.program,"alignment"),i.color=this.gl.getUniformLocation(this.sprite.program,"color"),i.map=this.gl.getUniformLocation(this.sprite.program,"map"),i.opacity=this.gl.getUniformLocation(this.sprite.program,"opacity"),i.useScreenCoordinates=this.gl.getUniformLocation(this.sprite.program,"useScreenCoordinates"),i.screenPosition=this.gl.getUniformLocation(this.sprite.program,"screenPosition"),i.modelViewMatrix=this.gl.getUniformLocation(this.sprite.program,"modelViewMatrix"),i.projectionMatrix=this.gl.getUniformLocation(this.sprite.program,"projectionMatrix"),i.fogType=this.gl.getUniformLocation(this.sprite.program,"fogType"),i.fogDensity=this.gl.getUniformLocation(this.sprite.program,"fogDensity"),i.fogNear=this.gl.getUniformLocation(this.sprite.program,"fogNear"),i.fogFar=this.gl.getUniformLocation(this.sprite.program,"fogFar"),i.fogColor=this.gl.getUniformLocation(this.sprite.program,"fogColor"),i.alphaTest=this.gl.getUniformLocation(this.sprite.program,"alphaTest"),this.sprite.uniforms=i}render(t,e,i,r,s){var n,a,o,l,h,c,d,u,f,p;if(!this.gl)throw new Error("WebGLRenderer not initialized");let g=[];null===(n=null==t?void 0:t.__webglSprites)||void 0===n||n.forEach((t=>{(s&&0==t.material.depthTest||!s&&t.material.depthTest)&&g.push(t)}));let m=g.length;if(!m)return;const v=this.sprite.attributes,_=this.sprite.uniforms;if(!_)throw new Error("Uniforms not defined");var y=.5*i,b=.5*r;this.gl.useProgram(this.sprite.program),this.gl.enableVertexAttribArray(v.position),this.gl.enableVertexAttribArray(v.uv),this.gl.disable(this.gl.CULL_FACE),this.gl.enable(this.gl.BLEND),this.gl.bindBuffer(this.gl.ARRAY_BUFFER,this.sprite.vertexBuffer),this.gl.vertexAttribPointer(v.position,2,this.gl.FLOAT,!1,16,0),this.gl.vertexAttribPointer(v.uv,2,this.gl.FLOAT,!1,16,8),this.gl.bindBuffer(this.gl.ELEMENT_ARRAY_BUFFER,this.sprite.elementBuffer),this.gl.uniformMatrix4fv(_.projectionMatrix,!1,e.projectionMatrix.elements),this.gl.activeTexture(this.gl.TEXTURE0),this.gl.uniform1i(_.map,0);var x,w=0,A=0,C=t.fog;let S,M,z,T;C?(this.gl.uniform3f(_.fogColor,C.color.r,C.color.g,C.color.b),this.gl.uniform1f(_.fogNear,C.near),this.gl.uniform1f(_.fogFar,C.far),this.gl.uniform1i(_.fogType,1),w=1,A=1):(this.gl.uniform1i(_.fogType,0),w=0,A=0);let E=[];for(x=0;x<m;x++)S=g[x],M=S.material,M&&(0!=M.depthTest||s)&&S.visible&&0!==M.opacity&&(M.useScreenCoordinates?S.z=-S.position.z:(S._modelViewMatrix.multiplyMatrices(e.matrixWorldInverse,S.matrixWorld),S.z=-S._modelViewMatrix.elements[14]));for(g.sort(ft),x=0;x<m;x++)if(S=g[x],M=S.material,M&&S.visible&&0!==M.opacity&&M.map&&M.map.image&&M.map.image.width){this.gl.uniform1f((null==_?void 0:_.alphaTest)||null,M.alphaTest);var L=M.map.image.width,F=M.map.image.height;E[0]=L*this.renderer.devicePixelRatio/i,E[1]=F*this.renderer.devicePixelRatio/r,!0===M.useScreenCoordinates?(this.gl.uniform1i(_.useScreenCoordinates,1),this.gl.uniform3f(_.screenPosition,(S.position.x*this.renderer.devicePixelRatio-y)/y,(b-S.position.y*this.renderer.devicePixelRatio)/b,Math.max(0,Math.min(1,S.position.z)))):(this.gl.uniform1i(_.useScreenCoordinates,0),this.gl.uniformMatrix4fv(_.modelViewMatrix,!1,S._modelViewMatrix.elements)),T=t.fog&&M.fog?A:0,w!==T&&(this.gl.uniform1i(_.fogType,T),w=T),z=1/(M.scaleByViewport?r:1),E[0]*=z*S.scale.x,E[1]*=z*S.scale.y;let e=null===(a=null==M?void 0:M.alignment)||void 0===a?void 0:a.x,s=null===(o=null==M?void 0:M.alignment)||void 0===o?void 0:o.y;M.screenOffset&&(e=(e||0)+2*M.screenOffset.x/L,s=(s||0)+2*M.screenOffset.y/F),this.gl.uniform2f(_.uvScale,(null===(l=null==M?void 0:M.uvScale)||void 0===l?void 0:l.x)||1,(null===(h=null==M?void 0:M.uvScale)||void 0===h?void 0:h.y)||1),this.gl.uniform2f(_.uvOffset,(null===(c=null==M?void 0:M.uvOffset)||void 0===c?void 0:c.x)||0,(null===(d=null==M?void 0:M.uvOffset)||void 0===d?void 0:d.y)||0),this.gl.uniform2f(_.alignment,e||0,s||0),this.gl.uniform1f(_.opacity,M.opacity),this.gl.uniform3f(_.color,(null===(u=null==M?void 0:M.color)||void 0===u?void 0:u.r)||0,(null===(f=null==M?void 0:M.color)||void 0===f?void 0:f.g)||0,(null===(p=null==M?void 0:M.color)||void 0===p?void 0:p.b)||0),this.gl.uniform1f(_.rotation,S.rotation),this.gl.uniform2fv(_.scale,E),this.renderer.setDepthTest(M.depthTest),this.renderer.setDepthWrite(M.depthWrite),this.renderer.setTexture(M.map,0),this.gl.drawElements(this.gl.TRIANGLES,6,this.gl.UNSIGNED_SHORT,0)}this.gl.enable(this.gl.CULL_FACE)}createProgram(t,e){if(!this.gl)throw new Error("WebGL Rendering context not found");var i=this.gl.createProgram();if(!i)throw new Error("Error creating webgl program");var r=this.gl.createShader(this.gl.FRAGMENT_SHADER),s=this.gl.createShader(this.gl.VERTEX_SHADER);if(!r)throw new Error("Unable to create fragment shader SpritePlugin.createProgram");if(!s)throw new Error("Unable to create vertex shader SpritePlugin.createProgram");var n="precision "+e+" float;\n";if(this.gl.shaderSource(r,n+t.fragmentShader),this.gl.shaderSource(s,n+t.vertexShader),this.gl.compileShader(r),this.gl.compileShader(s),!this.gl.getShaderParameter(r,this.gl.COMPILE_STATUS)||!this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS))throw new Error(`Error compiling shader: \n      ${this.gl.getShaderInfoLog(r)} \n      ${this.gl.getShaderInfoLog(s)}`);return this.gl.attachShader(i,r),this.gl.attachShader(i,s),this.gl.linkProgram(i),this.gl.getProgramParameter(i,this.gl.LINK_STATUS)||console.error("Could not initialize shader"),i}}function ft(t,e){return t.z!==e.z?e.z-t.z:e.id-t.id}var pt=null,gt=null;class Renderer{constructor(t){this.context=null,this.devicePixelRatio=1,this.sortObjects=!0,this.autoUpdateObjects=!0,this.autoUpdateScene=!0,this.info={memory:{programs:0,geometries:0,textures:0},render:{calls:0,vertices:0,faces:0,points:0}},this._offscreen=null,this._bitmap=null,this._programs=[],this._programs_counter=0,this._webglversion=1,this._currentProgram=null,this._currentMaterialId=-1,this._currentGeometryGroupHash=null,this._currentCamera=null,this._geometryGroupCounter=0,this._oldDoubleSided=-1,this._oldFlipSided=-1,this._oldDepthTest=-1,this._oldDepthWrite=-1,this._oldPolygonOffset=null,this._oldLineWidth=null,this._viewportWidth=0,this._viewportHeight=0,this._currentWidth=0,this._currentHeight=0,this._enabledAttributes={},this._vector3=new l.Vector3,this._worldInverse=new l.Matrix4,this._projInverse=new l.Matrix4,this._textureMatrix=new l.Matrix4,this._fullProjModelMatrix=new l.Matrix4,this._fullProjModelMatrixInv=new l.Matrix4,this._direction=new l.Vector3,this._lightsNeedUpdate=!0,this._lights={ambient:[0,0,0],directional:{length:0,colors:[],positions:[]},point:{length:0,colors:[],positions:[],distances:[]},spot:{length:0,colors:[],positions:[],distances:[],directions:[],anglesCos:[],exponents:[]},hemi:{length:0,skyColors:[],groundColors:[],positions:[]}},this.sprites=new SpritePlugin,this._screenshader=null,this._AOshader=null,this._blurshader=null,this._vertexattribpos=null,this._aovertexattribpos=null,this._blurvertexattribpos=null,this._screenQuadVBO=null,this._fb=null,this._targetTexture=null,this._depthTexture=null,this._shadingTexture=null,this._scratchTexture=null,this._AOstrength=1,this._AOradius=5,this.SHADE_TEXTURE=3,t=t||{},this.row=t.row,this.col=t.col,this.rows=t.rows,this.cols=t.cols,this._canvas=void 0!==t.canvas?t.canvas:document.createElement("canvas"),this._precision=void 0!==t.precision?t.precision:"highp",this._alpha=void 0===t.alpha||t.alpha,this._premultipliedAlpha=void 0===t.premultipliedAlpha||t.premultipliedAlpha,this._antialias=void 0!==t.antialias&&t.antialias,this._preserveDrawingBuffer=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,this._clearColor=void 0!==t.clearColor?new b.Color(t.clearColor):new b.Color(0),this._clearAlpha=void 0!==t.clearAlpha?t.clearAlpha:0,this._outlineMaterial=new MeshOutlineMaterial(t.outline),this._outlineSphereImposterMaterial=new SphereImposterOutlineMaterial(t.outline),this._outlineStickImposterMaterial=new StickImposterOutlineMaterial(t.outline),this._outlineEnabled=!!t.outline,this._AOEnabled=!!t.ambientOcclusion,t.ambientOcclusion&&void 0!==t.ambientOcclusion.strength&&(this._AOstrength=parseFloat(t.ambientOcclusion.strength)),0==this._AOstrength&&(this._AOEnabled=!1),t.ambientOcclusion&&void 0!==t.ambientOcclusion.radius&&(this._AOradius=parseFloat(t.ambientOcclusion.radius)),this.domElement=this._canvas,this._canvas.id=t.id,0!=t.containerWidth&&0!=t.containerHeight&&(this.initGL(),this.setDefaultGLState(),this.context=this._gl,this.isWebGL1()?this._extInstanced=this._gl.getExtension("ANGLE_instanced_arrays"):this._extInstanced={vertexAttribDivisorANGLE:this._gl.vertexAttribDivisor.bind(this._gl),drawElementsInstancedANGLE:this._gl.drawElementsInstanced.bind(this._gl)},this._extFragDepth=this._gl.getExtension("EXT_frag_depth"),this._extFloatLinear=this._gl.getExtension("OES_texture_float_linear"),this._extColorBufferFloat=this._gl.getExtension("EXT_color_buffer_float"),this.sprites.init(this))}supportedExtensions(){return{supportsAIA:Boolean(this._extInstanced),supportsImposters:Boolean(this._extFragDepth)||!this.isWebGL1(),regen:!1}}getContext(){return this._gl}getCanvas(){return this._canvas}isLost(){return null==this._gl||this._gl.isContextLost()}getPrecision(){return this._precision}setClearColorHex(t,e){this._clearColor.setHex(t),this._clearAlpha=e,this.isLost()||this._gl.clearColor(this._clearColor.r,this._clearColor.g,this._clearColor.b,this._clearAlpha)}enableOutline(t){this._outlineMaterial=new MeshOutlineMaterial(t),this._outlineSphereImposterMaterial=new SphereImposterOutlineMaterial(t),this._outlineStickImposterMaterial=new StickImposterOutlineMaterial(t),this._outlineEnabled=!0}disableOutline(){this._outlineEnabled=!1}enableAmbientOcclusion(t){t&&(t.strength&&(this._AOstrength=t.strength),t.scale&&(this._AOradius=t.scale)),this._AOEnabled=this._AOstrength>0}disableAmbientOcclusion(){this._AOEnabled=!1}setViewport(){if(this._offscreen&&(this._offscreen.width=this._canvas.width,this._offscreen.height=this._canvas.height),null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col){var t=this._canvas.width/this.cols,e=this._canvas.height/this.rows;this._viewportWidth=t,this._viewportHeight=e,this.isLost()||(this._gl.enable(this._gl.SCISSOR_TEST),this._gl.scissor(t*this.col,e*this.row,t,e),this._gl.viewport(t*this.col,e*this.row,t,e))}}setSize(t,e){if(this.devicePixelRatio=void 0!==window.devicePixelRatio?window.devicePixelRatio:1,this._antialias&&this.devicePixelRatio<2&&(this.devicePixelRatio*=2),null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col){var i=t/this.cols,r=e/this.rows;this._canvas.width=t*this.devicePixelRatio,this._canvas.height=e*this.devicePixelRatio,this._viewportWidth=i*this.devicePixelRatio,this._viewportHeight=r*this.devicePixelRatio,this._canvas.style.width=t+"px",this._canvas.style.height=e+"px",this.setViewport()}else this._viewportWidth=this._canvas.width=t*this.devicePixelRatio,this._viewportHeight=this._canvas.height=e*this.devicePixelRatio,this._canvas.style.width=t+"px",this._canvas.style.height=e+"px",this.isLost()||this._gl.viewport(0,0,this._gl.drawingBufferWidth,this._gl.drawingBufferHeight);this.initFrameBuffer()}clear(t,e,i){var r=0;(void 0===t||t)&&(r|=this._gl.COLOR_BUFFER_BIT),(void 0===e||e)&&(r|=this._gl.DEPTH_BUFFER_BIT),(void 0===i||i)&&(r|=this._gl.STENCIL_BUFFER_BIT),this._gl.clear(r)}setMaterialFaces(t,e){var i=t.side===a,r=t.side===n;t.imposter||(r=e?!r:r),this._oldDoubleSided!==i&&(i?this._gl.disable(this._gl.CULL_FACE):this._gl.enable(this._gl.CULL_FACE),this._oldDoubleSided=i),this._oldFlipSided!==r&&(r?this._gl.frontFace(this._gl.CW):this._gl.frontFace(this._gl.CCW),this._oldFlipSided=r),this._gl.cullFace(this._gl.BACK)}setDepthTest(t){this._oldDepthTest!==t&&(t?this._gl.enable(this._gl.DEPTH_TEST):this._gl.disable(this._gl.DEPTH_TEST),this._oldDepthTest=t)}setDepthWrite(t){this._oldDepthWrite!==t&&(this._gl.depthMask(t),this._oldDepthWrite=t)}setBlending(t){t?(this._gl.enable(this._gl.BLEND),this._gl.blendEquationSeparate(this._gl.FUNC_ADD,this._gl.FUNC_ADD),this._gl.blendFuncSeparate(this._gl.SRC_ALPHA,this._gl.ONE_MINUS_SRC_ALPHA,this._gl.ONE,this._gl.ONE_MINUS_SRC_ALPHA)):this._gl.disable(this._gl.BLEND)}initMaterial(t,e,i,r){var s,n;if(t.addEventListener("dispose",this.onMaterialDispose.bind(this)),n=t.shaderID){var a=ct[n];t.vertexShader=a.vertexShader,t.fragmentShader=a.fragmentShader,t.uniforms=ut.clone(a.uniforms),t.shaded&&t.makeShaded(this.SHADE_TEXTURE)}s={wireframe:t.wireframe,fragdepth:t.imposter,volumetric:t.volumetric,shaded:t.shaded},t.program=this.buildProgram(t.fragmentShader,t.vertexShader,t.uniforms,s)}renderBuffer(t,e,i,r,s,n){var a,o;if(r.visible&&(a=this.setProgram(t,e,i,r,n,this))){o=a.attributes;var l,h,c=!1,d=r.wireframe?1:0,u=16777215*s.id+2*a.id+d;if(u!==this._currentGeometryGroupHash&&(this._currentGeometryGroupHash=u,c=!0),c&&(this.disableAttributes(),o.position>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglVertexBuffer),this.enableAttribute(o.position),this._gl.vertexAttribPointer(o.position,3,this._gl.FLOAT,!1,0,0)),o.color>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglColorBuffer),this.enableAttribute(o.color),this._gl.vertexAttribPointer(o.color,3,this._gl.FLOAT,!1,0,0)),o.normal>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglNormalBuffer),this.enableAttribute(o.normal),this._gl.vertexAttribPointer(o.normal,3,this._gl.FLOAT,!1,0,0)),o.offset>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglOffsetBuffer),this.enableAttribute(o.offset),this._gl.vertexAttribPointer(o.offset,3,this._gl.FLOAT,!1,0,0)),o.radius>=0&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglRadiusBuffer),this.enableAttribute(o.radius),this._gl.vertexAttribPointer(o.radius,1,this._gl.FLOAT,!1,0,0))),n instanceof Mesh){if("instanced"===r.shaderID){var f=r.sphere.geometryGroups[0];c&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglVertexBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,f.vertexArray,this._gl.STATIC_DRAW),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,s.__webglNormalBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,f.normalArray,this._gl.STATIC_DRAW),this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,s.__webglFaceBuffer),this._gl.bufferData(this._gl.ELEMENT_ARRAY_BUFFER,f.faceArray,this._gl.STATIC_DRAW)),l=f.faceidx,this._extInstanced.vertexAttribDivisorANGLE(o.offset,1),this._extInstanced.vertexAttribDivisorANGLE(o.radius,1),this._extInstanced.vertexAttribDivisorANGLE(o.color,1),this._extInstanced.drawElementsInstancedANGLE(this._gl.TRIANGLES,l,this._gl.UNSIGNED_SHORT,0,s.radiusArray.length),this._extInstanced.vertexAttribDivisorANGLE(o.offset,0),this._extInstanced.vertexAttribDivisorANGLE(o.radius,0),this._extInstanced.vertexAttribDivisorANGLE(o.color,0)}else r.wireframe?(h=s.lineidx,this.setLineWidth(r.wireframeLinewidth),c&&this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,s.__webglLineBuffer),this._gl.drawElements(this._gl.LINES,h,this._gl.UNSIGNED_SHORT,0)):(l=s.faceidx,c&&this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,s.__webglFaceBuffer),this._gl.drawElements(this._gl.TRIANGLES,l,this._gl.UNSIGNED_SHORT,0));this.info.render.calls++,this.info.render.vertices+=l,this.info.render.faces+=l/3}else n instanceof Line&&(h=s.vertices,this.setLineWidth(r.linewidth),this._gl.drawArrays(this._gl.LINES,0,h),this.info.render.calls++)}}clearShading(){this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this.clear(!1,!0,!1),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._depthTexture,0)}setShading(t,e,i){let r=t.__lights,s=t.fog,n=[];for(let e=0,r=t.__webglObjects.length;e<r;e++){let r=t.__webglObjects[e];r.render&&r[i]&&n.push(r)}if(0==n.length)return;this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.COLOR_ATTACHMENT0,this._gl.TEXTURE_2D,null,0),this.renderObjects(t.__webglObjects,!0,i+"Depth",e,r,s,!1),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._scratchTexture,0),this.clear(!1,!0,!1),this._gl.useProgram(this._AOshader),this._currentProgram=this._AOshader,this.setDepthTest(-1),this.setDepthWrite(-1);let a=this._AOshader.uniforms;this._gl.uniform1f(a.total_strength,this._AOstrength),this._gl.uniform1f(a.radius,this._AOradius),this._fullProjModelMatrix=new l.Matrix4,this._fullProjModelMatrixInv=new l.Matrix4;let o=n[0].object;this._fullProjModelMatrix.multiplyMatrices(e.projectionMatrix,o._modelViewMatrix),this._fullProjModelMatrixInv.getInverse(this._fullProjModelMatrix),this._gl.uniformMatrix4fv(a.projectionMatrix,!1,this._fullProjModelMatrix.elements),this._gl.uniformMatrix4fv(a.projinv,!1,this._fullProjModelMatrixInv.elements),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.enableVertexAttribArray(this._aovertexattribpos),this._gl.vertexAttribPointer(this._aovertexattribpos,2,this._gl.FLOAT,!1,0,0),this._gl.activeTexture(this._gl.TEXTURE0),this._gl.bindTexture(this._gl.TEXTURE_2D,this._shadingTexture),this._gl.drawArrays(this._gl.TRIANGLES,0,6),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this.clear(!1,!0,!1),this._gl.useProgram(this._blurshader),this._currentProgram=this._blurshader,this.setDepthTest(-1),this.setDepthWrite(-1),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.enableVertexAttribArray(this._blurvertexattribpos),this._gl.vertexAttribPointer(this._blurvertexattribpos,2,this._gl.FLOAT,!1,0,0),this._gl.activeTexture(this._gl.TEXTURE0),this._gl.bindTexture(this._gl.TEXTURE_2D,this._scratchTexture),this._gl.drawArrays(this._gl.TRIANGLES,0,6),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.COLOR_ATTACHMENT0,this._gl.TEXTURE_2D,this._targetTexture,0),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._depthTexture,0)}render(t,e){if(e instanceof Camera==!1)return void console.error("Renderer.render: camera is not an instance of Camera.");var i,r,s,n,a,o=t.__lights,l=t.fog;if(this._currentMaterialId=-1,this._lightsNeedUpdate=!0,this.autoUpdateScene&&t.updateMatrixWorld(),void 0===e.parent&&e.updateMatrixWorld(),e.matrixWorldInverse.getInverse(e.matrixWorld),this.isLost())return;this.autoUpdateObjects&&this.initWebGLObjects(t),this.info.render.calls=0,this.info.render.vertices=0,this.info.render.faces=0,this.info.render.points=0,this._currentWidth=this._viewportWidth,this._currentHeight=this._viewportHeight,this.setViewport(),this.setFrameBuffer(),this._gl.clearColor(this._clearColor.r,this._clearColor.g,this._clearColor.b,this._clearAlpha),this.clear(!0,!0,!0),a=t.__webglObjects;let h=!1,c=this._AOEnabled;for(i=0,r=a.length;i<r;i++)n=(s=a[i]).object,s.render=!1,n.visible&&(this.setupMatrices(n,e),this.unrollBufferMaterial(s),s.render=!0,s.volumetric&&(h=!0),s.hasAO&&(c=!0));if(this.setBlending(!1),c&&this.setShading(t,e,"opaque"),this.renderObjects(t.__webglObjects,!0,"opaque",e,o,l,!1),c&&this.clearShading(),this.renderSprites(t,e,!1),this.renderObjects(t.__webglObjects,!0,"transparentDepth",e,o,l,!0),this.renderObjects(t.__webglObjects,!1,"transparent",e,o,l,!0),h&&this._fb&&(this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,null,0),this.renderObjects(t.__webglObjects,!1,"volumetric",e,o,l,!0)),this.renderFrameBuffertoScreen(),this.setDepthTest(!0),this.setDepthWrite(!0),this.renderSprites(t,e,!0),this._bitmap){const t=this._offscreen.transferToImageBitmap();this._bitmap.transferFromImageBitmap(t),t.close()}}setFrameBuffer(){if(this.isWebGL1()||!this._fb)return;let t=this._viewportWidth,e=this._viewportHeight;this._gl.enable(this._gl.SCISSOR_TEST),this._gl.scissor(0,0,t,e),this._gl.viewport(0,0,t,e),this._gl.bindTexture(this._gl.TEXTURE_2D,this._targetTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.RGBA,t,e,0,this._gl.RGBA,this._gl.UNSIGNED_BYTE,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.LINEAR),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.LINEAR),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.bindTexture(this._gl.TEXTURE_2D,this._depthTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.DEPTH_COMPONENT32F,t,e,0,this._gl.DEPTH_COMPONENT,this._gl.FLOAT,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._shadingTexture&&(this._gl.deleteTexture(this._shadingTexture),this._shadingTexture=this._gl.createTexture(),this._gl.bindTexture(this._gl.TEXTURE_2D,this._shadingTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.DEPTH_COMPONENT32F,t,e,0,this._gl.DEPTH_COMPONENT,this._gl.FLOAT,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.deleteTexture(this._scratchTexture),this._scratchTexture=this._gl.createTexture(),this._gl.bindTexture(this._gl.TEXTURE_2D,this._scratchTexture),this._gl.texImage2D(this._gl.TEXTURE_2D,0,this._gl.DEPTH_COMPONENT32F,t,e,0,this._gl.DEPTH_COMPONENT,this._gl.FLOAT,null),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(this._gl.TEXTURE_2D,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE)),this._gl.bindFramebuffer(this._gl.FRAMEBUFFER,this._fb),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.COLOR_ATTACHMENT0,this._gl.TEXTURE_2D,this._targetTexture,0),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._shadingTexture,0),this._gl.clearDepth(1),this._gl.clear(this._gl.DEPTH_BUFFER_BIT),this._gl.framebufferTexture2D(this._gl.FRAMEBUFFER,this._gl.DEPTH_ATTACHMENT,this._gl.TEXTURE_2D,this._depthTexture,0)}initFrameBuffer(){if(this.isWebGL1())return;let t=this._viewportWidth,e=this._viewportHeight;this._gl.enable(this._gl.SCISSOR_TEST),this._gl.scissor(0,0,t,e),this._gl.viewport(0,0,t,e),this._targetTexture=this._gl.createTexture(),this._depthTexture=this._gl.createTexture(),this._shadingTexture=this._gl.createTexture(),this._scratchTexture=this._gl.createTexture(),this._fb=this._gl.createFramebuffer();var i=this._antialias?ct.screenaa:ct.screen;this._screenshader=this.buildProgram(i.fragmentShader,i.vertexShader,i.uniforms,{}),this._vertexattribpos=this._gl.getAttribLocation(this._screenshader,"vertexPosition");this._screenQuadVBO=this._gl.createBuffer(),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.bufferData(this._gl.ARRAY_BUFFER,new Float32Array([1,1,-1,1,-1,-1,-1,-1,1,-1,1,1]),this._gl.STATIC_DRAW);let r=ct.ssao;this._AOshader=this.buildProgram(r.fragmentShader,r.vertexShader,r.uniforms,{}),this._aovertexattribpos=this._gl.getAttribLocation(this._AOshader,"vertexPosition");let s=ct.blur;this._blurshader=this.buildProgram(s.fragmentShader,s.vertexShader,s.uniforms,{}),this._blurvertexattribpos=this._gl.getAttribLocation(this._blurshader,"vertexPosition")}renderFrameBuffertoScreen(){this.isWebGL1()||null===this._fb||(this.setViewport(),this._gl.bindFramebuffer(this._gl.FRAMEBUFFER,null),this._gl.clear(this._gl.COLOR_BUFFER_BIT|this._gl.DEPTH_BUFFER_BIT),this._gl.frontFace(this._gl.CCW),this._gl.cullFace(this._gl.BACK),this._gl.useProgram(this._screenshader),this._currentProgram=this._screenshader,this.setDepthTest(-1),this.setDepthWrite(-1),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,this._screenQuadVBO),this._gl.enableVertexAttribArray(this._vertexattribpos),this._gl.vertexAttribPointer(this._vertexattribpos,2,this._gl.FLOAT,!1,0,0),this._gl.activeTexture(this._gl.TEXTURE0),this._gl.bindTexture(this._gl.TEXTURE_2D,this._targetTexture),this._gl.drawArrays(this._gl.TRIANGLES,0,6))}initWebGLObjects(t){if(t.__webglObjects||(t.__webglObjects=[],t.__webglObjectsImmediate=[],t.__webglSprites=[],t.__webglFlares=[]),t.__objectsAdded.length){for(;t.__objectsAdded.length;)this.addObject(t.__objectsAdded[0],t),t.__objectsAdded.splice(0,1);this._currentGeometryGroupHash=-1}for(;t.__objectsRemoved.length;)this.removeObject(t.__objectsRemoved[0],t),t.__objectsRemoved.splice(0,1);for(var e=0,i=t.__webglObjects.length;e<i;e++)this.updateObject(t.__webglObjects[e].object)}getYRatio(){return void 0!==this.rows&&void 0!==this.row?this.rows:1}getXRatio(){return void 0!==this.cols&&void 0!==this.col?this.cols:1}getAspect(t,e){null!=t&&null!=e||(t=this._canvas.width,e=this._canvas.height);var i=t/e;null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col&&(i=t/this.cols/(e/this.rows));return i}setTexture(t,e,i){if(t.needsUpdate){t.__webglInit||(t.__webglInit=!0,t.addEventListener("dispose",this.onTextureDispose.bind(this)),t.__webglTexture=this._gl.createTexture(),this.info.memory.textures++),this._gl.activeTexture(this._gl.TEXTURE0+e);var r=i?this._gl.TEXTURE_3D:this._gl.TEXTURE_2D;this._gl.bindTexture(r,t.__webglTexture),this._gl.pixelStorei(this._gl.UNPACK_FLIP_Y_WEBGL,t.flipY),this._gl.pixelStorei(this._gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL,t.premultiplyAlpha),this._gl.pixelStorei(this._gl.UNPACK_ALIGNMENT,t.unpackAlignment),this._gl.pixelStorei(this._gl.PACK_ALIGNMENT,t.unpackAlignment);var s=this.paramToGL(t.format),n=this.paramToGL(t.type);if(i)this.setTextureParameters(this._gl.TEXTURE_3D,t),this._gl.texImage3D(this._gl.TEXTURE_3D,0,this._gl.R32F,t.image.size.z,t.image.size.y,t.image.size.x,0,this._gl.RED,this._gl.FLOAT,t.image.data);else{var a=t.image,o=a.width,l=a.height;void 0===o&&(o=a.length,s==this._gl.RGBA&&(o/=4),l=1),this.setTextureParameters(this._gl.TEXTURE_2D,t),this.isWebGL1()?this._gl.texImage2D(this._gl.TEXTURE_2D,0,s,s,n,t.image):this._gl.texImage2D(this._gl.TEXTURE_2D,0,s,o,l,0,s,n,t.image)}t.needsUpdate=!1,t.onUpdate&&t.onUpdate()}else this._gl.activeTexture(this._gl.TEXTURE0+e),i?this._gl.bindTexture(this._gl.TEXTURE_3D,t.__webglTexture):this._gl.bindTexture(this._gl.TEXTURE_2D,t.__webglTexture)}supportsVolumetric(){return!this.isWebGL1()}enableAttribute(t){this._enabledAttributes[t]||(this._gl.enableVertexAttribArray(t),this._enabledAttributes[t]=!0)}disableAttributes(){for(let t in this._enabledAttributes)this._enabledAttributes[t]&&(this._gl.disableVertexAttribArray(t),this._enabledAttributes[t]=!1)}setPolygonOffset(t,e,i){this._oldPolygonOffset!==t&&(t?this._gl.enable(this._gl.POLYGON_OFFSET_FILL):this._gl.disable(this._gl.POLYGON_OFFSET_FILL))}setLineWidth(t){t!==this._oldLineWidth&&(this._gl.lineWidth(t),this._oldLineWidth=t)}deallocateGeometry(t){if(t.__webglInit=void 0,void 0!==t.__webglVertexBuffer&&this._gl.deleteBuffer(t.__webglVertexBuffer),void 0!==t.__webglColorBuffer&&this._gl.deleteBuffer(t.__webglColorBuffer),void 0!==t.geometryGroups)for(var e=0,i=t.groups;e<i;e++){var r=t.geometryGroups[e];void 0!==r.__webglVertexBuffer&&this._gl.deleteBuffer(r.__webglVertexBuffer),void 0!==r.__webglColorBuffer&&this._gl.deleteBuffer(r.__webglColorBuffer),void 0!==r.__webglNormalBuffer&&this._gl.deleteBuffer(r.__webglNormalBuffer),void 0!==r.__webglFaceBuffer&&this._gl.deleteBuffer(r.__webglFaceBuffer),void 0!==r.__webglLineBuffer&&this._gl.deleteBuffer(r.__webglLineBuffer)}}deallocateMaterial(t){var e=t.program;if(void 0!==e){var i,r,s;t.program=void 0;var n=!1;for(i=0,r=this._programs.length;i<r;i++)if((s=this._programs[i]).program===e){s.usedTimes--,0===s.usedTimes&&(n=!0);break}if(!0===n){var a=[];for(i=0,r=this._programs.length;i<r;i++)(s=this._programs[i]).program!==e&&a.push(s);this._programs=a,this._gl.deleteProgram(e),this.info.memory.programs--}}}deallocateTexture(t){if(t.image&&t.image.__webglTextureCube)this._gl.deleteTexture(t.image.__webglTextureCube);else{if(!t.__webglInit)return;t.__webglInit=!1,this._gl.deleteTexture(t.__webglTexture)}}onGeometryDispose(t){var e=t.target;e.removeEventListener("dispose",this.onGeometryDispose),this.deallocateGeometry(e),this.info.memory.geometries--}onTextureDispose(t){var e=t.target;e.removeEventListener("dispose",this.onTextureDispose),this.deallocateTexture(e),this.info.memory.textures--}onMaterialDispose(t){var e=t.target;e.removeEventListener("dispose",this.onMaterialDispose),this.deallocateMaterial(e)}getShader(t,e){var i;return this.isWebGL1()||e.startsWith("#version")||(e=e.replace(/gl_FragDepthEXT/g,"gl_FragDepth"),e="#version 300 es\n"+(e=(e=(e=(e=(e="fragment"==t?e.replace(/varying/g,"in"):e.replace(/varying/g,"out")).replace(/attribute/g,"in")).replace(/texture2D/g,"texture")).replace(/\/\/DEFINEFRAGCOLOR/g,"out vec4 glFragColor;")).replace(/gl_FragColor/g,"glFragColor"))),"fragment"===t?i=this._gl.createShader(this._gl.FRAGMENT_SHADER):"vertex"===t&&(i=this._gl.createShader(this._gl.VERTEX_SHADER)),null==i?null:(this._gl.shaderSource(i,e),this._gl.compileShader(i),this._gl.getShaderParameter(i,this._gl.COMPILE_STATUS)?i:(console.error(this._gl.getShaderInfoLog(i)),console.error("could not initialize shader"),null))}buildProgram(t,e,i,r){var s,n,a,o,l=[];for(s in l.push(t),l.push(e),r)l.push(s),l.push(r[s]);for(o=l.join(),s=0,n=this._programs.length;s<n;s++){var h=this._programs[s];if(h.code===o)return h.usedTimes++,h.program}if(this.isWebGL1()&&r.volumetric)throw new Error("Volumetric rendering requires webgl2 which is not supported by your hardware.");if(null==(a=this._gl.createProgram()))return null;var c,d,u,f="precision "+this._precision+" float;",p=[r.volumetric?"#version 300 es":"",f].join("\n"),g=[r.volumetric?"#version 300 es":"",r.fragdepth&&this.isWebGL1()?"#extension GL_EXT_frag_depth: enable":"",r.shaded?"#define SHADED 1":"",r.wireframe?"#define WIREFRAME 1":"",f].join("\n"),m=this.getShader("fragment",g+t),v=this.getShader("vertex",p+e);for(d in null!=v&&this._gl.attachShader(a,v),null!=m&&this._gl.attachShader(a,m),this._gl.linkProgram(a),this._gl.getProgramParameter(a,this._gl.LINK_STATUS)||console.error("Could not initialize shader"),a.uniforms={},a.attributes={},c=["viewMatrix","modelViewMatrix","projectionMatrix","normalMatrix","vWidth","vHeight"],i)c.push(d);for(u=0;u<c.length;u++){var _=c[u];a.uniforms[_]=this._gl.getUniformLocation(a,_)}for(c=["position","normal","color","lineDistance","offset","radius"],u=0;u<c.length;u++){var y=c[u];a.attributes[y]=this._gl.getAttribLocation(a,y)}return a.id=this._programs_counter++,this._programs.push({program:a,code:o,usedTimes:1}),this.info.memory.programs=this._programs.length,a}setProgram(t,e,i,r,s,n){if(r.needsUpdate&&(r.program&&this.deallocateMaterial(r),this.initMaterial(r,e,i,s),r.needsUpdate=!1),null==r.program)return null;var a=!1,o=r.program,l=o.uniforms,h=r.uniforms;if(o!=this._currentProgram&&(this._gl.useProgram(o),this._currentProgram=o,a=!0),r.id!=this._currentMaterialId&&(this._currentMaterialId=r.id,a=!0),t!=this._currentCamera&&(this._currentCamera=t,a=!0),l.projectionMatrix&&this._gl.uniformMatrix4fv(l.projectionMatrix,!1,t.projectionMatrix.elements),l.modelViewMatrix&&this._gl.uniformMatrix4fv(l.modelViewMatrix,!1,s._modelViewMatrix.elements),l.normalMatrix&&this._gl.uniformMatrix3fv(l.normalMatrix,!1,s._normalMatrix.elements),l.projinv&&(this._projInverse.getInverse(t.projectionMatrix),this._gl.uniformMatrix4fv(l.projinv,!1,this._projInverse.elements)),l.viewMatrix&&this._gl.uniformMatrix4fv(l.viewMatrix,!1,t.matrixWorldInverse.elements),l.vWidth&&this._gl.uniform1f(l.vWidth,this._viewportWidth),l.vHeight&&this._gl.uniform1f(l.vHeight,this._viewportHeight),a){if(h.fogColor.value=i.color,h.fogNear.value=i.near,h.fogFar.value=i.far,r.shaderID.startsWith("lambert")||"instanced"===r.shaderID||r.shaderID.endsWith("imposter"))this._lightsNeedUpdate&&(this.setupLights(o,e),this._lightsNeedUpdate=!1),h.directionalLightColor.value=this._lights.directional.colors,h.directionalLightDirection.value=this._lights.directional.positions;else if(r.shaderID.endsWith("outline"))h.outlineColor.value=r.outlineColor,h.outlineWidth.value=r.outlineWidth,h.outlinePushback.value=r.outlinePushback,h.outlineMaxPixels.value=r.outlineMaxPixels*this.devicePixelRatio;else if("volumetric"===r.shaderID){s._modelViewMatrix.getScale(this._direction),this._worldInverse.getInverse(s._modelViewMatrix),this._projInverse.getInverse(t.projectionMatrix),this._textureMatrix.multiplyMatrices(s.material.texmatrix,this._worldInverse),this._gl.uniformMatrix4fv(l.textmat,!1,this._textureMatrix.elements),this._gl.uniformMatrix4fv(l.projinv,!1,this._projInverse.elements);let e=Math.min(Math.min(this._direction.x,this._direction.y),this._direction.z);h.step.value=s.material.unit*e,h.maxdepth.value=s.material.maxdepth*e,h.transfermax.value=s.material.transfermax,h.transfermin.value=s.material.transfermin,h.subsamples.value=s.material.subsamples,n.setTexture(s.material.transferfn,4,!1),n.setTexture(s.material.map,3,!0),this._gl.activeTexture(this._gl.TEXTURE5),this._gl.bindTexture(this._gl.TEXTURE_2D,this._depthTexture)}h.opacity.value=r.opacity,this.loadMaterialUniforms(l,h)}return h.shading&&(3==h.shading.value?(this._gl.activeTexture(this._gl.TEXTURE0+this.SHADE_TEXTURE),this._gl.bindTexture(this._gl.TEXTURE_2D,this._shadingTexture)):console.error("Invalid shading textures.")),o}loadMaterialUniforms(t,e){var i,r,s,n;for(i in e)t[i]&&(r=e[i].type,s=e[i].value,n=t[i],"f"===r?this._gl.uniform1f(n,s):"i"===r?this._gl.uniform1i(n,s):"fv"===r?this._gl.uniform3fv(n,s):"c"===r?this._gl.uniform3f(n,s.r,s.g,s.b):"f4"===r&&this._gl.uniform4f(n,s[0],s[1],s[2],s[3]))}addObject(t,e){var i,r,s,n;if(!t.__webglInit&&(t.__webglInit=!0,t._modelViewMatrix=new l.Matrix4,t._normalMatrix=new l.Matrix3,void 0!==t.geometry&&void 0===t.geometry.__webglInit&&(t.geometry.__webglInit=!0,t.geometry.addEventListener("dispose",this.onGeometryDispose.bind(this))),t instanceof Mesh||t instanceof Line))for(i=0,r=(s=t.geometry).geometryGroups.length;i<r;i++)(n=s.geometryGroups[i]).id=this._geometryGroupCounter++,n.__webglVertexBuffer||(t instanceof Mesh?(this.createMeshBuffers(n),s.elementsNeedUpdate=!0,s.normalsNeedUpdate=!0):t instanceof Line&&this.createLineBuffers(n),s.verticesNeedUpdate=!0,s.colorsNeedUpdate=!0);if(!t.__webglActive){if(t instanceof Mesh||t instanceof Line)for(i=0,r=(s=t.geometry).geometryGroups.length;i<r;i++)n=s.geometryGroups[i],this.addBuffer(e.__webglObjects,n,t);else t instanceof Sprite&&e.__webglSprites.push(t);t.__webglActive=!0}}updateObject(t){var e,i=t.geometry;if(t instanceof Mesh||t instanceof Line){for(var r=0,s=i.geometryGroups.length;r<s;r++)e=i.geometryGroups[r],(i.verticesNeedUpdate||i.elementsNeedUpdate||i.colorsNeedUpdate||i.normalsNeedUpdate)&&this.setBuffers(e,this._gl.STATIC_DRAW);i.verticesNeedUpdate=!1,i.elementsNeedUpdate=!1,i.normalsNeedUpdate=!1,i.colorsNeedUpdate=!1,i.buffersNeedUpdate=!1}}removeObject(t,e){t instanceof Mesh||t instanceof Line?this.removeInstances(e.__webglObjects,t):t instanceof Sprite&&this.removeInstancesDirect(e.__webglSprites,t),t.__webglActive=!1}removeInstances(t,e){for(var i=t.length-1;i>=0;--i)t[i].object===e&&t.splice(i,1)}removeInstancesDirect(t,e){for(var i=t.length-1;i>=0;--i)t[i]===e&&t.splice(i,1)}unrollBufferMaterial(t){var e=t.object.material;if(e.volumetric)t.opaque=null,t.transparent=null,t.volumetric=e;else if(e.transparent){if(t.opaque=null,t.volumetric=null,t.transparent=e,!e.wireframe){let i=e.clone();i.opacity=0,t.transparentDepth=i}}else{if(t.opaque=e,t.transparent=null,t.volumetric=null,!e.wireframe){let i=e.clone();i.opacity=0,t.opaqueDepth=i}e.hasAO&&(t.hasAO=!0),(this._AOEnabled||t.hasAO)&&(t.opaqueShaded=e.clone(),t.opaqueShaded.shaded=!0)}}setBuffers(t,e){var i=t.vertexArray,r=t.colorArray;if(void 0!==t.__webglOffsetBuffer?(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglOffsetBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,i,e)):(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglVertexBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,i,e)),this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglColorBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,r,e),t.normalArray&&void 0!==t.__webglNormalBuffer){var s=t.normalArray;this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglNormalBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,s,e)}if(t.radiusArray&&void 0!==t.__webglRadiusBuffer&&(this._gl.bindBuffer(this._gl.ARRAY_BUFFER,t.__webglRadiusBuffer),this._gl.bufferData(this._gl.ARRAY_BUFFER,t.radiusArray,e)),t.faceArray&&void 0!==t.__webglFaceBuffer){var n=t.faceArray;this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,t.__webglFaceBuffer),this._gl.bufferData(this._gl.ELEMENT_ARRAY_BUFFER,n,e)}if(t.lineArray&&void 0!==t.__webglLineBuffer){var a=t.lineArray;this._gl.bindBuffer(this._gl.ELEMENT_ARRAY_BUFFER,t.__webglLineBuffer),this._gl.bufferData(this._gl.ELEMENT_ARRAY_BUFFER,a,e)}}createMeshBuffers(t){t.radiusArray&&(t.__webglRadiusBuffer=this._gl.createBuffer()),t.useOffset&&(t.__webglOffsetBuffer=this._gl.createBuffer()),t.__webglVertexBuffer=this._gl.createBuffer(),t.__webglNormalBuffer=this._gl.createBuffer(),t.__webglColorBuffer=this._gl.createBuffer(),t.__webglFaceBuffer=this._gl.createBuffer(),t.__webglLineBuffer=this._gl.createBuffer(),this.info.memory.geometries++}createLineBuffers(t){t.__webglVertexBuffer=this._gl.createBuffer(),t.__webglColorBuffer=this._gl.createBuffer(),this.info.memory.geometries++}addBuffer(t,e,i){t.push({buffer:e,object:i,opaque:null,transparent:null})}setupMatrices(t,e){t._modelViewMatrix.multiplyMatrices(e.matrixWorldInverse,t.matrixWorld),t._normalMatrix.getInverse(t._modelViewMatrix),t._normalMatrix.transpose()}filterFallback(t){return this._gl.LINEAR}setTextureParameters(t,e){t==this._gl.TEXTURE_2D?(this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_MAG_FILTER,this.filterFallback(e.magFilter)),this._gl.texParameteri(t,this._gl.TEXTURE_MIN_FILTER,this.filterFallback(e.minFilter))):(this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_S,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_T,this._gl.CLAMP_TO_EDGE),this._gl.texParameteri(t,this._gl.TEXTURE_WRAP_R,this._gl.CLAMP_TO_EDGE),this._extColorBufferFloat&&this._extFloatLinear?(this._gl.texParameteri(t,this._gl.TEXTURE_MAG_FILTER,this._gl.LINEAR),this._gl.texParameteri(t,this._gl.TEXTURE_MIN_FILTER,this._gl.LINEAR)):(this._gl.texParameteri(t,this._gl.TEXTURE_MAG_FILTER,this._gl.NEAREST),this._gl.texParameteri(t,this._gl.TEXTURE_MIN_FILTER,this._gl.NEAREST)))}paramToGL(t){return t===p?this._gl.UNSIGNED_BYTE:t===m?this._gl.RGBA:t===u?this._gl.NEAREST:0}setupLights(t,e){var i,r,s,n,a,o=this._lights,l=o.directional.colors,h=o.directional.positions,c=0,d=0;for(i=0,r=e.length;i<r;i++)if(n=(s=e[i]).color,a=s.intensity,s instanceof Light){if(this._direction.getPositionFromMatrix(s.matrixWorld),this._vector3.getPositionFromMatrix(s.target.matrixWorld),this._direction.sub(this._vector3),this._direction.normalize(),0===this._direction.x&&0===this._direction.y&&0===this._direction.z)continue;h[d]=this._direction.x,h[d+1]=this._direction.y,h[d+2]=this._direction.z,l[d]=n.r*a,l[d+1]=n.g*a,l[d+2]=n.b*a,d+=3,c++}o.ambient[0]=0,o.ambient[1]=0,o.ambient[2]=0,o.directional.length=c}initGL(){try{if(!OffscreenCanvas||null!=this.rows&&null!=this.cols&&null!=this.row&&null!=this.col){if(!(this._gl=this._canvas.getContext("webgl2",{alpha:this._alpha,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer}))&&!(this._gl=this._canvas.getContext("experimental-webgl",{alpha:this._alpha,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer}))&&!(this._gl=this._canvas.getContext("webgl",{alpha:this._alpha,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer})))throw"Error creating WebGL context."}else(null==gt||gt.isContextLost())&&(pt=new OffscreenCanvas(this._canvas.width,this._canvas.height),gt=pt.getContext("webgl2",{alpha:!0,premultipliedAlpha:this._premultipliedAlpha,antialias:this._antialias,preserveDrawingBuffer:this._preserveDrawingBuffer})),this._offscreen=pt,this._gl=gt,this._bitmap=this._canvas.getContext("bitmaprenderer",{alpha:!0});var t=this._gl.getParameter(this._gl.VERSION);this._webglversion=parseInt(t[6])}catch(t){console.error(t)}}isWebGL1(){return 1==this._webglversion}setDefaultGLState(){this._gl.clearDepth(1),this._gl.clearStencil(0),this._gl.enable(this._gl.DEPTH_TEST),this._gl.depthFunc(this._gl.LEQUAL),this._gl.frontFace(this._gl.CCW),this._gl.cullFace(this._gl.BACK),this._gl.enable(this._gl.CULL_FACE),this._gl.enable(this._gl.BLEND),this._gl.blendEquation(this._gl.FUNC_ADD),this._gl.blendFunc(this._gl.SRC_ALPHA,this._gl.ONE_MINUS_SRC_ALPHA),this._gl.clearColor(this._clearColor.r,this._clearColor.g,this._clearColor.b,this._clearAlpha)}renderObjects(t,e,i,r,s,n,a){var o,l,h,c,d,u,f;e?(d=t.length-1,u=-1,f=-1):(d=0,u=t.length,f=1);for(var p=d;p!==u;p+=f)if((o=t[p]).render){if(l=o.object,h=o.buffer,c=o[i],(o.hasAO||this._AOEnabled)&&o[i+"Shaded"]&&(c=o[i+"Shaded"]),!c)continue;this.setBlending(a),this.setDepthTest(c.depthTest),this.setDepthWrite(c.depthWrite),this.setPolygonOffset(c.polygonOffset,c.polygonOffsetFactor,c.polygonOffsetUnits);var g=l._modelViewMatrix.isReflected();if(this.setMaterialFaces(c,g),this.renderBuffer(r,s,n,c,h,l),(this._outlineEnabled||c.outline)&&!c.wireframe&&"basic"!==c.shaderID&&0!==c.opacity){let t=this._outlineMaterial;"sphereimposter"==c.shaderID?t=this._outlineSphereImposterMaterial:"stickimposter"==c.shaderID&&(t=this._outlineStickImposterMaterial),this.renderBuffer(r,s,n,t,h,l)}}}renderSprites(t,e,i){this._currentGeometryGroupHash=-1,this._currentProgram=null,this._currentCamera=null,this._oldDepthWrite=-1,this._oldDepthTest=-1,this._oldDoubleSided=-1,this._currentMaterialId=-1,this._oldFlipSided=-1,this._lightsNeedUpdate=!0,this.sprites.render(t,e,this._currentWidth,this._currentHeight,i),this._currentGeometryGroupHash=-1,this._currentProgram=null,this._currentCamera=null,this._oldDepthWrite=-1,this._oldDepthTest=-1,this._oldDoubleSided=-1,this._currentMaterialId=-1,this._oldFlipSided=-1}}},529:(t,e,i)=>{"use strict";i.r(e),i.d(e,{Matrix3:()=>Matrix3,Matrix4:()=>Matrix4,Quaternion:()=>Quaternion,Ray:()=>Ray,Vector2:()=>Vector2,Vector3:()=>Vector3,clamp:()=>r,conversionMatrix3:()=>d,degToRad:()=>n});class Quaternion{constructor(t,e,i,r){this.x=t||0,this.y=e||0,this.z=i||0,this.w=void 0!==r?r:1}set(t,e,i,r){return this.x=t,this.y=e,this.z=i,this.w=r,this}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=t.w,this}conjugate(){return this.x*=-1,this.y*=-1,this.z*=-1,this}inverse(){return this.conjugate().normalize()}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}lengthxyz(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}normalize(){let t=this.length();return 0===t?(this.x=0,this.y=0,this.z=0,this.w=1):(t=1/t,this.x*=t,this.y*=t,this.z*=t,this.w*=t),this}multiply(t){return this.multiplyQuaternions(this,t)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}multiplyQuaternions(t,e){const i=t.x,r=t.y,s=t.z,n=t.w,a=e.x,o=e.y,l=e.z,h=e.w;return this.x=i*h+n*a+r*l-s*o,this.y=r*h+n*o+s*a-i*l,this.z=s*h+n*l+i*o-r*a,this.w=n*h-i*a-r*o-s*l,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this}clone(){return new Quaternion(this.x,this.y,this.z,this.w)}setFromEuler(t){const e=Math.cos(t.x/2),i=Math.cos(t.y/2),r=Math.cos(t.z/2),s=Math.sin(t.x/2),n=Math.sin(t.y/2),a=Math.sin(t.z/2);return this.x=s*i*r+e*n*a,this.y=e*n*r-s*i*a,this.z=e*i*a+s*n*r,this.w=e*i*r-s*n*a,this}}class Vector2{constructor(t,e){this.x=t||0,this.y=e||0}set(t,e){return this.x=t,this.y=e,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}copy(t){return this.x=t.x,this.y=t.y,this}clone(){return new Vector2(this.x,this.y)}}function r(t,e,i){return Math.min(Math.max(t,e),i)}const s=Math.PI/180;function n(t){return t*s}var a,o,l,h,c;class Matrix4{constructor(t=1,e=0,i=0,r=0,s=0,n=1,a=0,o=0,l=0,h=0,c=1,d=0,u=0,f=0,p=0,g=1){void 0!==t&&"number"!=typeof t?this.elements=new Float32Array(t):(this.elements=new Float32Array(16),this.elements[0]=t,this.elements[4]=e,this.elements[8]=i,this.elements[12]=r,this.elements[1]=s,this.elements[5]=n,this.elements[9]=a,this.elements[13]=o,this.elements[2]=l,this.elements[6]=h,this.elements[10]=c,this.elements[14]=d,this.elements[3]=u,this.elements[7]=f,this.elements[11]=p,this.elements[15]=g)}makeScale(t,e,i){throw new Error("Method not implemented.")}set(t,e,i,r,s,n,a,o,l,h,c,d,u,f,p,g){const m=this.elements;return m[0]=t,m[4]=e,m[8]=i,m[12]=r,m[1]=s,m[5]=n,m[9]=a,m[13]=o,m[2]=l,m[6]=h,m[10]=c,m[14]=d,m[3]=u,m[7]=f,m[11]=p,m[15]=g,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}copy(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[12],e[1],e[5],e[9],e[13],e[2],e[6],e[10],e[14],e[3],e[7],e[11],e[15]),this}matrix3FromTopLeft(){const t=this.elements;return new Matrix3(t[0],t[4],t[8],t[1],t[5],t[9],t[2],t[6],t[10])}setRotationFromEuler(t,e){const i=this.elements,{x:r,y:s,z:n}=t,a=Math.cos(r),o=Math.sin(r),l=Math.cos(s),h=Math.sin(s),c=Math.cos(n),d=Math.sin(n);if(void 0===e||"XYZ"===e){const t=a*c,e=a*d,r=o*c,s=o*d;i[0]=l*c,i[4]=-l*d,i[8]=h,i[1]=e+r*h,i[5]=t-s*h,i[9]=-o*l,i[2]=s-t*h,i[6]=r+e*h,i[10]=a*l}else console.error(`Error with matrix4 setRotationFromEuler. Order: ${e}`);return this}setRotationFromQuaternion(t){const e=this.elements,{x:i,y:r,z:s,w:n}=t,a=i+i,o=r+r,l=s+s,h=i*a,c=i*o,d=i*l,u=r*o,f=r*l,p=s*l,g=n*a,m=n*o,v=n*l;return e[0]=1-(u+p),e[4]=c-v,e[8]=d+m,e[1]=c+v,e[5]=1-(h+p),e[9]=f-g,e[2]=d-m,e[6]=f+g,e[10]=1-(h+u),this}multiplyMatrices(t,e){const i=t.elements,r=e.elements,s=this.elements,n=i[0],a=i[4],o=i[8],l=i[12],h=i[1],c=i[5],d=i[9],u=i[13],f=i[2],p=i[6],g=i[10],m=i[14],v=i[3],_=i[7],y=i[11],b=i[15],x=r[0],w=r[4],A=r[8],C=r[12],S=r[1],M=r[5],z=r[9],T=r[13],E=r[2],L=r[6],F=r[10],I=r[14],O=r[3],D=r[7],k=r[11],R=r[15];return s[0]=n*x+a*S+o*E+l*O,s[4]=n*w+a*M+o*L+l*D,s[8]=n*A+a*z+o*F+l*k,s[12]=n*C+a*T+o*I+l*R,s[1]=h*x+c*S+d*E+u*O,s[5]=h*w+c*M+d*L+u*D,s[9]=h*A+c*z+d*F+u*k,s[13]=h*C+c*T+d*I+u*R,s[2]=f*x+p*S+g*E+m*O,s[6]=f*w+p*M+g*L+m*D,s[10]=f*A+p*z+g*F+m*k,s[14]=f*C+p*T+g*I+m*R,s[3]=v*x+_*S+y*E+b*O,s[7]=v*w+_*M+y*L+b*D,s[11]=v*A+_*z+y*F+b*k,s[15]=v*C+_*T+y*I+b*R,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}makeTranslation(t,e,i){return this.set(1,0,0,t,0,1,0,e,0,0,1,i,0,0,0,1),this}snap(t){t||(t=4);const e=Math.pow(10,4),i=this.elements;for(let t=0;t<16;t++){const r=Math.round(i[t]);r===Math.round(i[t]*e)/e&&(i[t]=r)}return this}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t){const e=this.elements;return e[12]=t.x,e[13]=t.y,e[14]=t.z,this}translate(t){const e=this.elements;return e[12]+=t.x,e[13]+=t.y,e[14]+=t.z,this}getInverse(t,e){const i=this.elements,r=t.elements,s=r[0],n=r[4],a=r[8],o=r[12],l=r[1],h=r[5],c=r[9],d=r[13],u=r[2],f=r[6],p=r[10],g=r[14],m=r[3],v=r[7],_=r[11],y=r[15];i[0]=c*g*v-d*p*v+d*f*_-h*g*_-c*f*y+h*p*y,i[4]=o*p*v-a*g*v-o*f*_+n*g*_+a*f*y-n*p*y,i[8]=a*d*v-o*c*v+o*h*_-n*d*_-a*h*y+n*c*y,i[12]=o*c*f-a*d*f-o*h*p+n*d*p+a*h*g-n*c*g,i[1]=d*p*m-c*g*m-d*u*_+l*g*_+c*u*y-l*p*y,i[5]=a*g*m-o*p*m+o*u*_-s*g*_-a*u*y+s*p*y,i[9]=o*c*m-a*d*m-o*l*_+s*d*_+a*l*y-s*c*y,i[13]=a*d*u-o*c*u+o*l*p-s*d*p-a*l*g+s*c*g,i[2]=h*g*m-d*f*m+d*u*v-l*g*v-h*u*y+l*f*y,i[6]=o*f*m-n*g*m-o*u*v+s*g*v+n*u*y-s*f*y,i[10]=n*d*m-o*h*m+o*l*v-s*d*v-n*l*y+s*h*y,i[14]=o*h*u-n*d*u-o*l*f+s*d*f+n*l*g-s*h*g,i[3]=c*f*m-h*p*m-c*u*v+l*p*v+h*u*_-l*f*_,i[7]=n*p*m-a*f*m+a*u*v-s*p*v-n*u*_+s*f*_,i[11]=a*h*m-n*c*m-a*l*v+s*c*v+n*l*_-s*h*_,i[15]=n*c*u-a*h*u+a*l*f-s*c*f-n*l*p+s*h*p;const b=s*i[0]+l*i[4]+u*i[8]+m*i[12];if(0===b){const t="Matrix4.getInverse(): can't invert matrix, determinant is 0";if(e)throw new Error(t);return console.warn(t),this.identity(),this}return this.multiplyScalar(1/b),this}isReflected(){const t=this.elements,e=t[0],i=t[4],r=t[8],s=t[1],n=t[5],a=t[9],o=t[2],l=t[6],h=t[10];return e*n*h+s*l*r+o*i*a-o*n*r-s*i*h-e*l*a<0}scale(t){const e=this.elements,{x:i}=t,{y:r}=t,{z:s}=t;return e[0]*=i,e[4]*=r,e[8]*=s,e[1]*=i,e[5]*=r,e[9]*=s,e[2]*=i,e[6]*=r,e[10]*=s,e[3]*=i,e[7]*=r,e[11]*=s,this}getMaxScaleOnAxis(){const t=this.elements,e=t[0]*t[0]+t[1]*t[1]+t[2]*t[2],i=t[4]*t[4]+t[5]*t[5]+t[6]*t[6],r=t[8]*t[8]+t[9]*t[9]+t[10]*t[10];return Math.sqrt(Math.max(e,Math.max(i,r)))}makeFrustum(t,e,i,r,s,n){const a=this.elements,o=2*s/(e-t),l=2*s/(r-i),h=(e+t)/(e-t),c=(r+i)/(r-i),d=-(n+s)/(n-s),u=-2*n*s/(n-s);return a[0]=o,a[4]=0,a[8]=h,a[12]=0,a[1]=0,a[5]=l,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=d,a[14]=u,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makePerspective(t,e,i,r){const s=i*Math.tan(n(.5*t)),a=-s,o=a*e,l=s*e;return this.makeFrustum(o,l,a,s,i,r)}makeOrthographic(t,e,i,r,s,n){const a=this.elements,o=1/(e-t),l=1/(i-r),h=1/(n-s),c=(e+t)*o,d=(i+r)*l,u=(n+s)*h;return a[0]=2*o,a[4]=0,a[8]=0,a[12]=-c,a[1]=0,a[5]=2*l,a[9]=0,a[13]=-d,a[2]=0,a[6]=0,a[10]=-2*h,a[14]=-u,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}isEqual(t){const e=t.elements,i=this.elements;return i[0]===e[0]&&i[4]===e[4]&&i[8]===e[8]&&i[12]===e[12]&&i[1]===e[1]&&i[5]===e[5]&&i[9]===e[9]&&i[13]===e[13]&&i[2]===e[2]&&i[6]===e[6]&&i[10]===e[10]&&i[14]===e[14]&&i[3]===e[3]&&i[7]===e[7]&&i[11]===e[11]&&i[15]===e[15]}clone(){const t=this.elements;return new Matrix4(t[0],t[4],t[8],t[12],t[1],t[5],t[9],t[13],t[2],t[6],t[10],t[14],t[3],t[7],t[11],t[15])}isIdentity(){const t=this.elements;return 1===t[0]&&0===t[4]&&0===t[8]&&0===t[12]&&0===t[1]&&1===t[5]&&0===t[9]&&0===t[13]&&0===t[2]&&0===t[6]&&1===t[10]&&0===t[14]&&0===t[3]&&0===t[7]&&0===t[11]&&1===t[15]}isNearlyIdentity(t){return this.clone().snap(t).isIdentity()}getScale(t){const e=this.elements;return t=t||new Vector3,l.set(e[0],e[1],e[2]),h.set(e[4],e[5],e[6]),c.set(e[8],e[9],e[10]),t.x=l.length(),t.y=h.length(),t.z=c.length(),t}lookAt(t,e,i){const r=this.elements;return c.subVectors(t,e).normalize(),0===c.length()&&(c.z=1),l.crossVectors(i,c).normalize(),0===l.length()&&(c.x+=1e-4,l.crossVectors(i,c).normalize()),h.crossVectors(c,l),r[0]=l.x,r[4]=h.x,r[8]=c.x,r[1]=l.y,r[5]=h.y,r[9]=c.y,r[2]=l.z,r[6]=h.z,r[10]=c.z,this}compose(t,e,i){const r=this.elements;return a.identity(),a.setRotationFromQuaternion(e),o.makeScale(i.x,i.y,i.z),this.multiplyMatrices(a,o),r[12]=t.x,r[13]=t.y,r[14]=t.z,this}}a=new Matrix4,o=new Matrix4;class Vector3{constructor(t,e,i){this.x=t||0,this.y=e||0,this.z=i||0,this.atomid=void 0}set(t,e,i){return this.x=t,this.y=e,this.z=i,this}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t){return this.x+=t.x,this.y+=t.y,this.z+=t.z,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}sub(t){return this.x-=t.x,this.y-=t.y,this.z-=t.z,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}divideScalar(t){return 0!==t?(this.x/=t,this.y/=t,this.z/=t):(this.x=0,this.y=0,this.z=0),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,i=this.y-t.y,r=this.z-t.z;return e*e+i*i+r*r}applyMatrix3(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements;return this.x=s[0]*e+s[3]*i+s[6]*r,this.y=s[1]*e+s[4]*i+s[7]*r,this.z=s[2]*e+s[5]*i+s[8]*r,this}applyMatrix4(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements;return this.x=s[0]*e+s[4]*i+s[8]*r+s[12],this.y=s[1]*e+s[5]*i+s[9]*r+s[13],this.z=s[2]*e+s[6]*i+s[10]*r+s[14],this}applyProjection(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements,n=s[3]*e+s[7]*i+s[11]*r+s[15];return this.x=(s[0]*e+s[4]*i+s[8]*r+s[12])/n,this.y=(s[1]*e+s[5]*i+s[9]*r+s[13])/n,this.z=(s[2]*e+s[6]*i+s[10]*r+s[14])/n,this}applyQuaternion(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.x,n=t.y,a=t.z,o=t.w,l={};l.x=2*(i*a-r*n),l.y=2*(r*s-e*a),l.z=2*(e*n-i*s);const h={};return h.x=l.y*a-l.z*n,h.y=l.z*s-l.x*a,h.z=l.x*n-l.y*s,this.x=e+o*l.x+h.x,this.y=i+o*l.y+h.y,this.z=r+o*l.z+h.z,this}negate(){return this.multiplyScalar(-1)}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}normalize(){return this.divideScalar(this.length())}cross(t){const{x:e}=this,{y:i}=this,{z:r}=this;return this.x=i*t.z-r*t.y,this.y=r*t.x-e*t.z,this.z=e*t.y-i*t.x,this}crossVectors(t,e){return this.x=t.y*e.z-t.z*e.y,this.y=t.z*e.x-t.x*e.z,this.z=t.x*e.y-t.y*e.x,this}equals(t){return this.x==t.x&&this.y==t.y&&this.z==t.z}getPositionFromMatrix(t){return this.x=t.elements[12],this.y=t.elements[13],this.z=t.elements[14],this}setEulerFromRotationMatrix(t,e){const i=t.elements,s=i[0],n=i[4],a=i[8],o=i[5],l=i[9],h=i[6],c=i[10];return void 0===e||"XYZ"===e?(this.y=Math.asin(r(a,-1,1)),Math.abs(a)<.99999?(this.x=Math.atan2(-l,c),this.z=Math.atan2(-n,s)):(this.x=Math.atan2(h,o),this.z=0)):console.error(`Error with vector's setEulerFromRotationMatrix: Unknown order: ${e}`),this}rotateAboutVector(t,e){t.normalize();const i=Math.cos(e),r=Math.sin(e),s=this.clone().multiplyScalar(i),n=t.clone().cross(this).multiplyScalar(r),a=t.clone().multiplyScalar(t.clone().dot(this)).multiplyScalar(1-i),o=s.add(n).add(a);return this.x=o.x,this.y=o.y,this.z=o.z,this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}transformDirection(t){const{x:e}=this,{y:i}=this,{z:r}=this,s=t.elements;return this.x=s[0]*e+s[4]*i+s[8]*r,this.y=s[1]*e+s[5]*i+s[9]*r,this.z=s[2]*e+s[6]*i+s[10]*r,this.normalize()}clone(){return new Vector3(this.x,this.y,this.z)}unproject(t){const e=a;return e.multiplyMatrices(t.matrixWorld,e.getInverse(t.projectionMatrix)),this.applyMatrix4(e)}}l=new Vector3,h=new Vector3,c=new Vector3;class Matrix3{constructor(t=1,e=0,i=0,r=0,s=1,n=0,a=0,o=0,l=1){this.elements=new Float32Array(9),this.set(t,e,i,r,s,n,a,o,l)}set(t,e,i,r,s,n,a,o,l){const h=this.elements;return h[0]=t,h[3]=e,h[6]=i,h[1]=r,h[4]=s,h[7]=n,h[2]=a,h[5]=o,h[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=t.elements;this.set(e[0],e[3],e[6],e[1],e[4],e[7],e[2],e[5],e[8])}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}getInverse3(t){const e=t.elements,i=this.elements;i[0]=e[4]*e[8]-e[5]*e[7],i[3]=e[6]*e[5]-e[3]*e[8],i[6]=e[3]*e[7]-e[6]*e[4],i[1]=e[7]*e[2]-e[1]*e[8],i[4]=e[0]*e[8]-e[6]*e[2],i[7]=e[1]*e[6]-e[0]*e[7],i[2]=e[1]*e[5]-e[2]*e[4],i[5]=e[2]*e[3]-e[0]*e[5],i[8]=e[0]*e[4]-e[1]*e[3];const r=e[0]*i[0]+e[3]*i[1]+e[6]*i[2];return this.multiplyScalar(1/r),this}getInverse(t,e){const i=t.elements,r=this.elements;r[0]=i[10]*i[5]-i[6]*i[9],r[1]=-i[10]*i[1]+i[2]*i[9],r[2]=i[6]*i[1]-i[2]*i[5],r[3]=-i[10]*i[4]+i[6]*i[8],r[4]=i[10]*i[0]-i[2]*i[8],r[5]=-i[6]*i[0]+i[2]*i[4],r[6]=i[9]*i[4]-i[5]*i[8],r[7]=-i[9]*i[0]+i[1]*i[8],r[8]=i[5]*i[0]-i[1]*i[4];const s=i[0]*r[0]+i[1]*r[3]+i[2]*r[6];if(0===s){const t="Matrix3.getInverse(): can't invert matrix, determinant is 0";if(e)throw new Error(t);return console.warn(t),this.identity(),this}return this.multiplyScalar(1/s),this}getDeterminant(){const t=this.elements;return t[0]*t[4]*t[8]+t[1]*t[5]*t[6]+t[2]*t[3]*t[7]-t[2]*t[4]*t[6]-t[1]*t[3]*t[8]-t[0]*t[5]*t[7]}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}clone(){const t=this.elements;return new Matrix3(t[0],t[3],t[6],t[1],t[4],t[7],t[2],t[5],t[8])}getMatrix4(){const t=this.elements;return new Matrix4(t[0],t[3],t[6],0,t[1],t[4],t[7],0,t[2],t[5],t[8],0)}}class Ray{constructor(t,e){this.origin=void 0!==t?t:new Vector3,this.direction=void 0!==e?e:new Vector3}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return(e||new Vector3).copy(this.direction).multiplyScalar(t).add(this.origin)}recast(t){const e=l;return this.origin.copy(this.at(t,e)),this}closestPointToPoint(t,e){const i=e||new Vector3;i.subVectors(t,this.origin);const r=i.dot(this.direction);return i.copy(this.direction).multiplyScalar(r).add(this.origin)}distanceToPoint(t){const e=l,i=e.subVectors(t,this.origin).dot(this.direction);return e.copy(this.direction).multiplyScalar(i).add(this.origin),e.distanceTo(t)}isIntersectionCylinder(){}isIntersectionSphere(t){return this.distanceToPoint(t.center)<=t.radius}isIntersectionPlane(t){return 0!==t.normal.dot(this.direction)||0===t.distanceToPoint(this.origin)}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:void 0;return-(this.origin.dot(t.normal)+t.constant)/e}intersectPlane(t,e){const i=this.distanceToPlane(t);if(void 0!==i)return this.at(i,e)}applyMatrix4(t){return this.direction.add(this.origin).applyMatrix4(t),this.origin.applyMatrix4(t),this.direction.sub(this.origin),this}clone(){return(new Ray).copy(this)}}function d(t,e,i,r,s,n){r=r*Math.PI/180,s=s*Math.PI/180,n=n*Math.PI/180;const a=t=>t*t,o=Math.cos(r),l=Math.cos(s),h=Math.cos(n),c=Math.sin(n);return new Matrix3(t,e*h,i*l,0,e*c,i*(o-l*h)/c,0,0,i*Math.sqrt(1-a(o)-a(l)-a(h)+2*o*l*h)/c)}},99:(t,e,i)=>{"use strict";i.r(e),i.d(e,{Cylinder:()=>Cylinder,Sphere:()=>Sphere,Triangle:()=>Triangle});var r=i(529);class Sphere{constructor(t={x:0,y:0,z:0},e=0){this.center=new r.Vector3(t.x,t.y,t.z),this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new Sphere).copy(this)}}let s=new r.Vector3;class Cylinder{constructor(t=new r.Vector3,e=new r.Vector3,i=0){this.c1=t,this.c2=e,this.radius=i,this.direction=(new r.Vector3).subVectors(this.c2,this.c1).normalize()}copy(t){return this.c1.copy(t.c1),this.c2.copy(t.c2),this.direction.copy(t.direction),this.radius=t.radius,this}lengthSq(){return s.subVectors(this.c2,this.c1).lengthSq()}applyMatrix4(t){return this.direction.add(this.c1).applyMatrix4(t),this.c1.applyMatrix4(t),this.c2.applyMatrix4(t),this.direction.sub(this.c1).normalize(),this.radius=this.radius*t.getMaxScaleOnAxis(),this}}const n=new r.Vector3;class Triangle{constructor(t=new r.Vector3,e=new r.Vector3,i=new r.Vector3){this.a=t,this.b=e,this.c=i}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}applyMatrix4(t){return this.a.applyMatrix4(t),this.b.applyMatrix4(t),this.c.applyMatrix4(t),this}getNormal(){var t=this.a.clone();return t.sub(this.b),n.subVectors(this.c,this.b),t.cross(n),t.normalize(),t}}},222:(t,e,i)=>{"use strict";i.r(e),i.d(e,{CC:()=>CC,Color:()=>Color,builtinColorSchemes:()=>h,chains:()=>l,elementColors:()=>a,htmlColors:()=>r,residues:()=>o,ssColors:()=>s});class Color{constructor(t,e,i){return this.r=0,this.g=0,this.b=0,arguments.length>1&&"number"==typeof t?(this.r=t||0,this.g=e||0,this.b=i||0,this):this.set(t||0)}set(t){return t instanceof Color?t.clone():("number"==typeof t?this.setHex(t):"object"==typeof t&&(this.r=(null==t?void 0:t.r)||0,this.g=(null==t?void 0:t.g)||0,this.b=(null==t?void 0:t.b)||0),this)}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}getHex(){return Math.round(255*this.r)<<16|Math.round(255*this.g)<<8|Math.round(255*this.b)}clone(){return new Color(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}scaled(){var t={};return t.r=Math.round(255*this.r),t.g=Math.round(255*this.g),t.b=Math.round(255*this.b),t.a=1,t}}class CC{static color(t){if(!t)return CC.cache[0];if(t instanceof Color)return t;if("number"==typeof t&&void 0!==CC.cache[t])return CC.cache[t];if(t&&Array.isArray(t))return t.map(CC.color);let e=CC.getHex(t),i=new Color(e);return CC.cache[e]=i,i}static getHex(t){var e;if(Array.isArray(t))return t.map(CC.getHex);if("string"==typeof t){let i=t;if(!isNaN(parseInt(i)))return parseInt(i);if(i=i.trim(),4==i.length&&"#"==i[0]&&(i="#"+i[1]+i[1]+i[2]+i[2]+i[3]+i[3]),7==i.length&&"#"==i[0])return parseInt(i.substring(1),16);let r=CC.rgbRegEx.exec(i);if(r){""!=r[1]&&console.log("WARNING: Opacity value in rgba ignored.  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e=0;e<t._pdbx_struct_oper_list_id.length;e++){const i=parseFloat(t["_pdbx_struct_oper_list_matrix[1][1]"][e]),r=parseFloat(t["_pdbx_struct_oper_list_matrix[1][2]"][e]),a=parseFloat(t["_pdbx_struct_oper_list_matrix[1][3]"][e]),o=parseFloat(t["_pdbx_struct_oper_list_vector[1]"][e]),l=parseFloat(t["_pdbx_struct_oper_list_matrix[2][1]"][e]),h=parseFloat(t["_pdbx_struct_oper_list_matrix[2][2]"][e]),c=parseFloat(t["_pdbx_struct_oper_list_matrix[2][3]"][e]),d=parseFloat(t["_pdbx_struct_oper_list_vector[2]"][e]),u=parseFloat(t["_pdbx_struct_oper_list_matrix[3][1]"][e]),f=parseFloat(t["_pdbx_struct_oper_list_matrix[3][2]"][e]),p=parseFloat(t["_pdbx_struct_oper_list_matrix[3][3]"][e]),g=parseFloat(t["_pdbx_struct_oper_list_vector[3]"][e]),m=new n.Matrix4(i,r,a,o,l,h,c,d,u,f,p,g);s[s.length-1].symmetries.push(m)}const l=function(t){const e=t.match("-"),i=(t=t.replace(/[-xyz]/g,"")).split("/");let r,s;return s=void 0===i[1]?1:parseInt(i[1]),r=""===i[0]?1:parseInt(i[0]),r/s*(e?-1:1)};if(void 0!==t._symmetry_equiv_pos_as_xyz&&!r)for(let e=0;e<t._symmetry_equiv_pos_as_xyz.length;e++){const i=t._symmetry_equiv_pos_as_xyz[e].replace(/["' ]/g,"").split(",").map((function(t){return t.replace(/-/g,"+-")}));let r=new n.Matrix4(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1);for(let t=0;t<3;t++){const e=i[t].split("+");for(let i=0;i<e.length;i++){const s=e[i];if(""===s)continue;const n=l(s);s.match("x")?r.elements[t+0]=n:s.match("y")?r.elements[t+4]=n:s.match("z")?r.elements[t+8]=n:r.elements[t+12]=n}}const o=a.getMatrix4(),h=(new n.Matrix4).getInverse(o,!0);r=(new n.Matrix4).multiplyMatrices(r,h),r=(new n.Matrix4).multiplyMatrices(o,r),s[s.length-1].symmetries.push(r)}}var f,p,g,_;for(let t=0;t<i.length;t++)!a||e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms||b(i[t],e),m(i[t],e.hbondCutoff),v(s[t].symmetries,i[t],e,s[t].cryst),e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms&&a&&b(i[t],e);return i}let w={"C.1":"C",C1:"C","C.2":"C",C2:"C","C.3":"C",C3:"C","C.ar":"C",Car:"C","C.cat":"C",Ccat:"C","H.spc":"H",Hspc:"H","H.t3p":"H",Ht3p:"H","N.1":"N",N1:"N","N.2":"N",N2:"N","N.3":"N",N3:"N","N.4":"N",N4:"N","N.am":"N",Nam:"N","N.ar":"N",Nar:"N","N.p13":"N",Np13:"N","O.2":"O",O2:"O","O.3":"O",O3:"O","O.co2":"O",Oco2:"O","O.spc":"O",Ospc:"O","O.t3p":"O",Ot3p:"O","P.3":"P",P3:"P","S.2":"S",S2:"S","S.3":"S",S3:"S","S.o":"S",So:"S","S.o2":"S",So2:"S"};function A(t,e){var i=[[]],r=!1;void 0!==e.keepH&&(r=!e.keepH);var s=t.search(/@<TRIPOS>MOLECULE/),n=t.search(/@<TRIPOS>ATOM/);if(-1==s||-1==n)return i;for(var a=t.substring(s).split(/\r?\n|\r/);a.length>0;){var o=[],l=a[2].replace(/^\s+/,"").replace(/\s+/g," ").split(" "),h=parseInt(l[0]),c=0;l.length>1&&(c=parseInt(l[1]));var d,u=4;for(d=3;d<a.length;d++)if("@<TRIPOS>ATOM"==a[d]){u=d+1;break}var f=i[i.length-1].length,p=f+h;for(d=f;d<p;d++){var g={},m=(l=a[u++].replace(/^\s+/,"").replace(/\s+/g," ").split(" "))[5];if(m=void 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i.length>0&&"H"===i[0]&&"Hg"!==i&&"He"!==i&&"Hf"!==i&&"Hs"!==i&&"Ho"!==i&&(i="H"),i.length>1&&(i=i[0].toUpperCase()+i.substring(1).toLowerCase(),void 0===C.bondTable[i]?i=i[0]:e&&("Ca"===i||"Cd"===i)&&(i="C")),i}function M(t){for(const e in t)return!1;return!0}function z(t,e,i){const r=[],s=void 0===e.assignBonds||e.assignBonds,a=!e.keepH,o=!!e.noSecondaryStructure,l=!e.noComputeSecondaryStructure,h=!e.doAssembly,c=e.altLoc?e.altLoc:"A",d={symmetries:[],cryst:void 0};let u,f=[];const p=[];let g;const _={};for(let e=0;e<t.length;e++){g=t[e].replace(/^\s*/,"");const s=g.substring(0,6);let o,l,m;if(0===s.indexOf("END")){if(f=t.slice(e+1),"END"===s)for(const t in i)i.hasOwnProperty(t)&&delete i[t];break}if("ATOM  "===s||"HETATM"===s){let t,e,i,n,o,l,h,d,f,m,v,_;if(v=g.substring(16,17)," "!==v&&v!==c&&"*"!==c)continue;if(m=parseInt(g.substring(6,11)),u=g.substring(12,16).replace(/ /g,""),t=g.substring(17,20).replace(/ 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0!==o)if(_[a]){_[a]+=1;for(let t=0;t<e.bonds.length;t++)if(e.bonds[t]==n){const i=_[a];e.bondOrder[t]=i>=4?1:i}}else _[a]=1,0!=e.bonds.length&&e.bonds[e.bonds.length-1]===n||(e.bonds.push(n),e.bondOrder.push(1))}}else if("HELIX "===s){o=g.substring(19,20),l=parseInt(g.substring(21,25)),m=parseInt(g.substring(33,37)),o in i||(i[o]={}),i[o][l]="h1";for(let t=l+1;t<m;t++)i[o][t]="h";i[o][m]="h2"}else if(h||"REMARK"!==s||"BIOMT"!==g.substring(13,18)){if("CRYST1"===s){let t,e,i,r,s,n;t=parseFloat(g.substring(7,15)),e=parseFloat(g.substring(16,24)),i=parseFloat(g.substring(25,33)),r=parseFloat(g.substring(34,40)),s=parseFloat(g.substring(41,47)),n=parseFloat(g.substring(48,54)),d.cryst={a:t,b:e,c:i,alpha:r,beta:s,gamma:n}}else if("ANISOU"===s){const t=r[p[parseInt(g.substring(6,11))]];if(t){const e=g.substring(30).trim().split(/\s+/),i={u11:parseInt(e[0]),u22:parseInt(e[1]),u33:parseInt(e[2]),u12:parseInt(e[3]),u13:parseInt(e[4]),u23:parseInt(e[5])};t.uMat=i}}}else{let i,r=new n.Matrix4;for(i=1;i<=3;i++)if(g=t[e].replace(/^\s*/,""),parseInt(g.substring(18,19))==i)r.elements[i-1]=parseFloat(g.substring(23,33)),r.elements[i-1+4]=parseFloat(g.substring(33,43)),r.elements[i-1+8]=parseFloat(g.substring(43,53)),r.elements[i-1+12]=parseFloat(g.substring(53)),e++;else for(;"BIOMT"===g.substring(13,18);)e++,g=t[e].replace(/^\s*/,"");r.elements[3]=0,r.elements[7]=0,r.elements[11]=0,r.elements[15]=1,d.symmetries.push(r),e--}}if(function(t,e){for(let i=0,r=t.length;i<r;i++){const r=t[i];for(let i=0;i<r.bonds.length;i++){const s=t[r.bonds[i]],n=e[r.serial];s&&n&&s.bonds.indexOf(n)<0&&(s.bonds.push(n),s.bondOrder.push(r.bondOrder[i]))}}}(r,p),s&&b(r,e),h||v(d.symmetries,r,e,d.cryst),l&&!o&&m(r,e.hbondCutoff),!M(i))for(let t=0;t<r.length;t++){const e=r[t];if(void 0!==e&&(e.chain in i&&e.resi in i[e.chain])){const t=i[e.chain][e.resi];e.ss=t[0],t.length>1&&("1"==t[1]?e.ssbegin=!0:"2"==t[1]&&(e.ssend=!0))}}return[r,d,f]}function T(t,e){e=e||{};var i=[],r={};i.modelData=[];for(var s=t.split(/\r?\n|\r/);s.length>0;){var n=z(s,e,r),a=n[0],o=n[1];if(s=n[2],0!=a.length){if(e.multimodel&&e.onemol&&i.length>0)for(var l=i[0].length,h=0;h<a.length;h++){var c=a[h];c.index=h;for(var d=0;d<c.bonds.length;d++)c.bonds[d]+=l;i[0].push(c)}else i.modelData.push(o),i.push(a);if(!e.multimodel)break}}return i}function E(t,e){var i=[[]],r=!e.noSecondaryStructure;i.modelData=[{symmetries:[]}];var s,n=[],a=t.split(/\r?\n|\r/);for(let t=0;t<a.length;t++){var o=(s=a[t].replace(/^\s*/,"")).substring(0,6);if(0==o.indexOf("END")){if(e.multimodel){e.onemol||i.push([]);continue}break}if("ATOM  "==o||"HETATM"==o){var l;let t=parseInt(s.substring(6,11)),e=s.substring(12,16).replace(/ /g,""),r=s.substring(17,20).trim(),a=s.substring(21,22),o=parseInt(s.substring(22,26));var h=s.substring(30).trim().split(/\s+/),c=parseFloat(h[0]),d=parseFloat(h[1]),u=parseFloat(h[2]),f=parseFloat(h[3]),p=parseFloat(h[4]),g=e[0];e.length>1&&e[1].toUpperCase()!=e[1]&&(g=e.substring(0,2)),l="H"==s[0],n[t]=i[i.length-1].length,i[i.length-1].push({resn:r,x:c,y:d,z:u,elem:g,hetflag:l,chain:a,resi:o,serial:t,atom:e,bonds:[],ss:"c",bondOrder:[],properties:{charge:f,partialCharge:f,radius:p},pdbline:s})}else if("CONECT"==o){var v=parseInt(s.substring(6,11)),_=i[i.length-1][n[v]];for(let t=0;t<4;t++){var y=parseInt(s.substring([11,16,21,26][t],[11,16,21,26][t]+5)),x=i[i.length-1][n[y]];void 0!==_&&void 0!==x&&(_.bonds.push(n[y]),_.bondOrder.push(1))}}}for(let t=0;t<i.length;t++)b(i[t],e),r&&m(i[t],e.hbondCutoff);return i}var L=i(864),F=i(471),I=function(t){return String.fromCharCode.apply(null,t).replace(/\0/g,"")},O=function(t){return 0==t||2==t||4==t?"h":3==t?"s":"c"};let D=new Set(["D-SACCHARIDE","D-SACCHARIDE 1,4 AND 1,4 LINKING","D-SACCHARIDE 1,4 AND 1,6 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i=[],r=t.split(/\r?\n|\r/);for(;r.length>0;){const t=parseInt(r[1]);if(r.length<3||isNaN(t)||t<=0||r.length<t+3)break;const e=[];i.push(e);let s=2;const n=e.length,a=n+t;for(let t=n;t<a;t++){const i=r[s++],n={};n.serial=t,n.atom=i.slice(10,15).trim(),n.elem=S(n.atom,!0),n.x=10*parseFloat(i.slice(20,28)),n.y=10*parseFloat(i.slice(28,36)),n.z=10*parseFloat(i.slice(36,44)),n.resi=parseInt(i.slice(0,5)),n.resn=i.slice(5,10).trim(),n.bonds=[],n.bondOrder=[],n.properties={},i.length>44&&(n.dx=10*parseFloat(i.slice(44,52)),n.dy=10*parseFloat(i.slice(52,60)),n.dz=10*parseFloat(i.slice(60,68))),e[t]=n}if(r.length<=s+3){const t=r[s++].trim().split(/\s+/);if(3===t.length){for(let e=0;e<3;e++)t[e]=(10*parseFloat(t[e])).toString();i.box=t}}r.splice(0,++s)}for(let t=0;t<i.length;t++)b(i[t],e);return i}const U={id:"serial",type:"atom",element:"elem",q:"charge",radius:"radius",x:"x",xu:"x",xs:"x",xsu:"x",y:"y",yu:"y",ys:"y",ysu:"y",z:"z",zu:"z",zs:"z",zsu:"z"};function B(t,e){const i=[],r=t.split(/\r?\n|\r/);let s=0,n=0,a=0;for(;a<r.length-9;){for(var o=a;o<r.length;o++)if(r[o].match(/ITEM: NUMBER OF ATOMS/)&&(n=parseInt(r[o+1])),r[o].match(/ITEM: ATOMS/)){s=o+1;break}const t=r[s-1].replace("ITEM: ATOMS ","").split(" ");i.push([]);for(let e=s;e<s+n;e++){const n={},a={},o=r[e].split(" ");for(let e=0;e<o.length;e++){const i=U[t[e]];void 0!==i&&("serial"===i?n[i]=parseInt(o[e]):"x"===i||"y"===i||"z"===i?n[i]=parseFloat(o[e]):"charge"===i||"radius"===i?a[i]=parseFloat(o[e]):n[i]=o[e]),n.properties=a,n.bonds=[],n.bondOrder=[]}i[i.length-1][e-s]=n}a=s+n-1}if(e.assignBonds)for(let t=0;t<i.length;t++)(0,u.assignBonds)(i[t],e);return i}var N;!function(t){let e,i;!function(t){t[t.Int8=1]="Int8",t[t.Int16=2]="Int16",t[t.Int32=3]="Int32",t[t.Uint8=4]="Uint8",t[t.Uint16=5]="Uint16",t[t.Uint32=6]="Uint32"}(e=t.IntDataType||(t.IntDataType={})),function(t){t[t.Float32=32]="Float32",t[t.Float64=33]="Float64"}(i=t.FloatDataType||(t.FloatDataType={})),t.getDataType=function(e){let 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new Int16Array(e);case N.IntDataType.Int32:return new Int32Array(e);case N.IntDataType.Uint8:return new Uint8Array(e);case N.IntDataType.Uint16:return new Uint16Array(e);case N.IntDataType.Uint32:return new Uint32Array(e);default:return new Int32Array(e)}}function W(t,e){switch(t){case N.FloatDataType.Float32:return new Float32Array(e);case N.FloatDataType.Float64:default:return new Float64Array(e)}}function q(t,e,i){return 0==t.byteOffset&&t.byteLength==t.buffer.byteLength||(t=new Uint8Array(t)),new i(G?t.buffer:function(t,e){const i=new ArrayBuffer(t.length),r=new Uint8Array(i);for(let i=0,s=t.length;i<s;i+=e)for(let s=0;s<e;s++)r[i+e-s-1]=t[i+s];return i}(t,e))}function Y(t){const e=Object.create(null),i=Object.create(null);for(const i of t.columns)e[i.name]=i;return{rowCount:t.rowCount,name:t.name.substring(1),fieldNames:t.columns.map((t=>t.name)),getField(t){const r=e[t];if(r)return i[t]||(i[t]=V(r.data)),i[t]}}}var Z=i(471);class Connectivity{constructor(t){if(this.C={},t){let e=t.getField("comp_id"),i=t.getField("atom_id_1"),r=t.getField("atom_id_2"),s=t.getField("value_order");for(let t=0;t<e.length;t++){let n=e[t],a=i[t],o=r[t],l=s[t],h=1;"doub"==l?h=2:"trip"==l&&(h=3),null==this.C[n]&&(this.C[n]={}),null==this.C[n][a]&&(this.C[n][a]={}),null==this.C[n][o]&&(this.C[n][o]={}),this.C[n][a][o]=h,this.C[n][o][a]=h}}}order(t,e,i){return void 0!==this.C[t]&&void 0!==this.C[t][e]&&void 0!==this.C[t][e][i]?this.C[t][e][i]:0}}class StructConn{constructor(t){if(this.C=[],t){let e=t.getField("conn_type_id"),i=t.getField("ptnr1_label_asym_id"),r=t.getField("ptnr1_label_seq_id"),s=t.getField("ptnr1_label_comp_id"),n=t.getField("ptnr1_label_atom_id"),a=t.getField("ptnr2_label_asym_id"),o=t.getField("ptnr2_label_seq_id"),l=t.getField("ptnr2_label_comp_id"),h=t.getField("ptnr2_label_atom_id"),c=t.getField("pdbx_value_order");for(let t=0;t<e.length;t++)if("disulf"==e[t]||"covale"==e[t]){let e=c?""==c[t]?1:parseInt(c[t]):1;this.C.push([[i[t],r[t],s[t],n[t]],[a[t],o[t],l[t],h[t]],e])}}}}class Residues{constructor(){this.R={}}add(t){null==this.R[t.lchain]&&(this.R[t.lchain]={}),null==this.R[t.lchain][t.lresi]&&(this.R[t.lchain][t.lresi]={}),null==this.R[t.lchain][t.lresi][t.lresn]&&(this.R[t.lchain][t.lresi][t.lresn]=[]),this.R[t.lchain][t.lresi][t.lresn].push(t),this.R[t.lchain][t.lresi][t.lresn][t.atom]=t}geta([t,e,i,r]){if(void 0!==this.R[t]&&void 0!==this.R[t][e]&&void 0!==this.R[t][e][i])return this.R[t][e][i][r]}setBonds(t,e){for(let e in this.R)for(let i in this.R[e])for(let r in this.R[e][i]){let s=this.R[e][i][r];for(let e=0;e<s.length;e++)for(let i=e+1;i<s.length;i++){let n=s[e],a=s[i],o=t.order(r,n.atom,a.atom);n.altLoc!=a.altLoc&&""!=n.altLoc&&""!=a.altLoc&&(o=0),o>0&&(n.bonds.push(a.index),a.bonds.push(n.index),n.bondOrder.push(o),a.bondOrder.push(o))}}for(let t of e.C){let e=t[0],i=t[1],r=t[2],s=this.geta(e),n=this.geta(i);null!=s&&null!=n&&(s.bonds.push(n.index),n.bonds.push(s.index),s.bondOrder.push(r),n.bondOrder.push(r))}}}function X(t,e){var i=!e.keepH,r=e.altLoc?e.altLoc:"A",s=!e.noComputeSecondaryStructure;const a=!e.doAssembly,o=void 0===e.assignBonds||e.assignBonds;if("string"==typeof t)try{t=(0,L.base64ToArray)(t)}catch(e){t=(new TextEncoder).encode(t)}else t=new Uint8Array(t);var l=Z.decodeMsgpack(t);31==l&&(t=(0,L.inflateString)(t,!1),l=Z.decodeMsgpack(t));var h=[],c=h.modelData=[],d=l.dataBlocks.length;if(0==d)return h;e.multimodel||(d=1);for(let t=0;t<d;t++){let s=h.length;const o=[];c.push({symmetries:[]}),h.push([]);const u=l.dataBlocks[t],f=Object.create(null);for(const t of u.categories)f[t.name.substr(1)]=Y(t);let p={},g=f.struct_conf;if(g){let t=g.getField("conf_type_id"),e=g.getField("beg_label_asym_id"),i=g.getField("beg_label_seq_id"),r=g.getField("end_label_seq_id");for(let s=0;s<t.length;s++)if(t[s].startsWith("H")){let t=e[s],n=i[s],a=r[s];t in p||(p[t]={}),p[t][n]="h1";for(let e=n+1;e<a;e++)p[t][e]="h";p[t][a]="h2"}}let m=f.struct_sheet_range;if(m){let t=m.getField("id"),e=m.getField("beg_label_asym_id"),i=m.getField("beg_label_seq_id"),r=m.getField("end_label_seq_id");for(let s=0;s<t.length;s++){let t=e[s],n=i[s],a=r[s];t in p||(p[t]={}),p[t][n]="s1";for(let e=n+1;e<a;e++)p[t][e]="s";p[t][a]="s2"}}let v=f.pdbx_struct_oper_list,_=v.getField("id");if(_&&!a){let t=v.getField("matrix[1][1]"),e=v.getField("matrix[1][2]"),i=v.getField("matrix[1][3]"),r=v.getField("matrix[2][1]"),s=v.getField("matrix[2][2]"),a=v.getField("matrix[2][3]"),o=v.getField("matrix[3][1]"),l=v.getField("matrix[3][2]"),h=v.getField("matrix[3][3]"),d=v.getField("vector[1]"),u=v.getField("vector[2]"),f=v.getField("vector[3]");for(let p=0;p<_.length;p++){const g=new n.Matrix4(t[p],e[p],i[p],d[p],r[p],s[p],a[p],u[p],o[p],l[p],h[p],f[p]);c[c.length-1].symmetries.push(g)}}let y=new Connectivity(f.chem_comp_bond),b=new Residues,x=new StructConn(f.struct_conn),w=f.atom_site,A=w.rowCount,C=w.getField("group_PDB"),S=w.getField("Cartn_x"),z=w.getField("Cartn_y"),T=w.getField("Cartn_z"),E=w.getField("auth_asym_id"),L=w.getField("label_asym_id"),F=w.getField("auth_seq_id"),I=w.getField("label_seq_id"),O=w.getField("auth_comp_id"),D=w.getField("label_comp_id"),k=w.getField("auth_atom_id"),R=w.getField("label_atom_id"),P=w.getField("type_symbol"),U=w.getField("B_iso_or_equiv"),B=w.getField("id"),N=w.getField("label_alt_id"),G=w.getField("pdbx_PDB_model_num"),V=G?G[0]:0;for(let t=0;t<A;t++){if(void 0!==C&&"TER"===C[t])continue;if(G&&G[t]!=V){if(V=G[t],!e.multimodel)break;e.onemol||(h.push([]),c.push(c[c.length-1]),V=G[t],b.setBonds(y,x),b=new Residues)}const s={};s.x=S[t],s.y=z[t],s.z=T[t],s.chain=E?E[t]:L?L[t]:void 0,s.lchain=L?L[t]:void 0,s.resi=F?F[t]:I?I[t]:void 0,s.lresi=I?I[t]:void 0,s.resn=O?O[t].trim():D?D[t].trim():void 0,s.lresn=D?D[t].trim():void 0,s.atom=k?k[t].replace(/"/gm,""):R?R[t].replace(/"/gm,""):void 0,s.icode=N?N[t]:void 0,s.altLoc=s.icode,s.hetflag=!C||"HETA"===C[t]||"HETATM"===C[t];let n="X";P&&(n=P[t].replace(/\(?\+?\d+.*/,"")),s.elem=n[0].toUpperCase()+n.substring(1,2).toLowerCase(),U&&(s.b=U[t]),i&&"H"==s.elem||(""!=s.altLoc&&s.altLoc!=r&&"*"!=r||(s.bonds=[],s.ss="c",s.serial=B[t],s.model=V,s.bondOrder=[],s.properties={},s.index=h[h.length-1].length,o[s.serial]=[h.length,s.index],h[h.length-1].push(s),b.add(s)))}if(b.setBonds(y,x),!M(p))for(let t=s;t<h.length;t++){let e=h[t];for(let t=0;t<e.length;t++){const i=e[t];if(void 0!==i&&(i.lchain in p&&i.lresi in p[i.lchain])){const t=p[i.lchain][i.lresi];i.ss=t[0],t.length>1&&("1"==t[1]?i.ssbegin=!0:"2"==t[1]&&(i.ssend=!0))}}}e.multimodel&&t<d-1&&(e.onemol||(h.push([]),c.push({symmetries:[]})))}for(let t=0;t<h.length;t++)!o||e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms||b(h[t],e),s&&m(h[t],e.hbondCutoff),v(c[t].symmetries,h[t],e,c[t].cryst),e.duplicateAssemblyAtoms&&!e.dontConnectDuplicatedAtoms&&o&&b(h[t],e);return h}const K={vasp:c.VASP,VASP:c.VASP,cube:d.CUBE,CUBE:d.CUBE,xyz:f,XYZ:f,sdf:p,SDF:p,json:g,cdjson:g,CDJSON:g,mcif:x,cif:x,CIF:x,mol2:A,MOL2:A,pdb:T,PDB:T,pdbqt:T,PDBQT:T,pqr:E,PQR:E,mmtf:k,MMTF:k,prmtop:R,PRMTOP:R,gro:P,GRO:P,lammpstrj:B,LAMMPSTRJ:B,bcif:X,BCIF:X};var Q,$=i(529),J=i(99);!function(t){t[t.VDW=1]="VDW",t[t.MS=2]="MS",t[t.SAS=3]="SAS",t[t.SES=4]="SES"}(Q||(Q={}));var tt=!1;function et(t){tt=t}(window.navigator.userAgent.indexOf("MSIE ")>=0||window.navigator.userAgent.indexOf("Trident/")>=0)&&(tt=!0);class MarchingCubeInitializer{constructor(){this.ISDONE=2,this.edgeTable=new Uint32Array([0,0,0,0,0,0,0,2816,0,0,0,1792,0,3328,3584,3840,0,0,0,138,0,21,0,134,0,0,0,652,0,2067,3865,3600,0,0,0,42,0,0,0,294,0,0,21,28,0,3875,1049,3360,0,168,162,170,0,645,2475,2210,0,687,293,172,4010,3747,3497,3232,0,0,0,0,0,69,0,900,0,0,0,1792,138,131,1608,1920,0,81,0,2074,84,85,84,86,0,81,0,3676,330,1105,1881,1616,0,0,0,42,0,69,0,502,0,0,21,3580,138,2035,1273,1520,2816,104,2337,106,840,581,367,102,2816,3695,3429,3180,1898,1635,1385,1120,0,0,0,0,0,0,0,3910,0,0,69,588,42,2083,41,2880,0,0,0,1722,0,2293,4095,3830,0,255,757,764,2538,2291,3065,2800,0,0,81,338,0,3925,1119,3414,84,855,85,340,2130,2899,89,2384,1792,712,194,1162,4036,3781,3535,3270,708,719,197,204,3018,2755,2505,2240,0,0,0,0,168,420,168,1958,162,162,676,2988,170,163,680,928,3328,3096,3328,3642,52,53,1855,1590,2340,2111,2869,2620,298,51,825,560,3584,3584,3090,3482,1668,1941,1183,1430,146,2975,2069,2460,154,915,153,400,3840,3592,3329,3082,1796,1541,1295,1030,2818,2575,2309,2060,778,515,265,0]),this.triTable=[[],[],[],[],[],[],[],[11,9,8],[],[],[],[8,10,9],[],[10,8,11],[9,11,10],[8,10,9,8,11,10],[],[],[],[1,7,3],[],[4,2,0],[],[2,1,7],[],[],[],[2,7,3,2,9,7],[],[1,4,11,1,0,4],[3,8,0,11,9,4,11,10,9],[4,11,9,11,10,9],[],[],[],[5,3,1],[],[],[],[2,5,8,2,1,5],[],[],[2,4,0],[3,2,4],[],[0,9,1,8,10,5,8,11,10],[3,4,0,3,10,4],[5,8,10,8,11,10],[],[3,5,7],[7,1,5],[1,7,3,1,5,7],[],[9,2,0,9,7,2],[0,3,8,1,7,11,1,5,7],[11,1,7,1,5,7],[],[9,1,0,5,3,2,5,7,3],[8,2,5,8,0,2],[2,5,3,5,7,3],[3,9,1,3,8,9,7,11,10,7,10,5],[9,1,0,10,7,11,10,5,7],[3,8,0,7,10,5,7,11,10],[11,5,7,11,10,5],[],[],[],[],[],[0,6,2],[],[7,2,9,7,9,8],[],[],[],[8,10,9],[7,1,3],[7,1,0],[6,9,3,6,10,9],[7,10,8,10,9,8],[],[6,0,4],[],[11,1,4,11,3,1],[2,4,6],[2,0,4,2,4,6],[2,4,6],[1,4,2,4,6,2],[],[6,0,4],[],[2,11,3,6,9,4,6,10,9],[8,6,1,8,1,3],[10,0,6,0,4,6],[8,0,3,9,6,10,9,4,6],[10,4,6,10,9,4],[],[],[],[5,3,1],[],[0,6,2],[],[7,4,8,5,2,1,5,6,2],[],[],[2,4,0],[7,4,8,2,11,3,10,5,6],[7,1,3],[5,6,10,0,9,1,8,7,4],[5,6,10,7,0,3,7,4,0],[10,5,6,4,8,7],[9,11,8],[3,5,6],[0,5,11,0,11,8],[6,3,5,3,1,5],[3,9,6,3,8,9],[9,6,0,6,2,0],[0,3,8,2,5,6,2,1,5],[1,6,2,1,5,6],[9,11,8],[1,0,9,6,10,5,11,3,2],[6,10,5,2,8,0,2,11,8],[3,2,11,10,5,6],[10,5,6,9,3,8,9,1,3],[0,9,1,5,6,10],[8,0,3,10,5,6],[10,5,6],[],[],[],[],[],[],[],[1,10,2,9,11,6,9,8,11],[],[],[6,0,2],[3,6,9,3,2,6],[3,5,1],[0,5,1,0,11,5],[0,3,5],[6,9,11,9,8,11],[],[],[],[4,5,9,7,1,10,7,3,1],[],[11,6,7,2,4,5,2,0,4],[11,6,7,8,0,3,1,10,2,9,4,5],[6,7,11,1,10,2,9,4,5],[],[4,1,0,4,5,1,6,7,3,6,3,2],[9,4,5,0,6,7,0,2,6],[4,5,9,6,3,2,6,7,3],[6,7,11,5,3,8,5,1,3],[6,7,11,4,1,0,4,5,1],[4,5,9,3,8,0,11,6,7],[9,4,5,7,11,6],[],[],[0,6,4],[8,6,4,8,1,6],[],[0,10,2,0,9,10,4,8,11,4,11,6],[10,2,1,6,0,3,6,4,0],[10,2,1,11,4,8,11,6,4],[4,2,6],[1,0,9,2,4,8,2,6,4],[2,4,0,2,6,4],[8,2,4,2,6,4],[11,4,1,11,6,4],[0,9,1,4,11,6,4,8,11],[3,6,0,6,4,0],[8,6,4,8,11,6],[10,8,9],[6,3,9,6,7,3],[6,7,1],[10,7,1,7,3,1],[7,11,6,8,10,2,8,9,10],[11,6,7,10,0,9,10,2,0],[2,1,10,7,11,6,8,0,3],[1,10,2,6,7,11],[7,2,6,7,9,2],[1,0,9,3,6,7,3,2,6],[7,0,6,0,2,6],[2,7,3,2,6,7],[7,11,6,3,9,1,3,8,9],[9,1,0,11,6,7],[0,3,8,11,6,7],[11,6,7],[],[],[],[],[5,3,7],[8,5,2,8,7,5],[5,3,7],[1,10,2,5,8,7,5,9,8],[1,7,5],[1,7,5],[9,2,7,9,7,5],[11,3,2,8,5,9,8,7,5],[1,3,7,1,7,5],[0,7,1,7,5,1],[9,3,5,3,7,5],[9,7,5,9,8,7],[8,10,11],[3,4,10,3,10,11],[8,10,11],[5,9,4,1,11,3,1,10,11],[2,4,5],[5,2,4,2,0,4],[0,3,8,5,9,4,10,2,1],[2,1,10,9,4,5],[2,8,5,2,11,8],[3,2,11,1,4,5,1,0,4],[9,4,5,8,2,11,8,0,2],[11,3,2,9,4,5],[8,5,3,5,1,3],[5,0,4,5,1,0],[3,8,0,4,5,9],[9,4,5],[11,9,10],[11,9,10],[1,11,4,1,10,11],[8,7,4,11,1,10,11,3,1],[2,7,9,2,9,10],[4,8,7,0,10,2,0,9,10],[2,1,10,0,7,4,0,3,7],[10,2,1,8,7,4],[1,7,4],[3,2,11,4,8,7,9,1,0],[11,4,2,4,0,2],[2,11,3,7,4,8],[4,1,7,1,3,7],[1,0,9,8,7,4],[3,4,0,3,7,4],[8,7,4],[8,9,10,8,10,11],[3,9,11,9,10,11],[0,10,8,10,11,8],[10,3,1,10,11,3],[2,8,10,8,9,10],[9,2,0,9,10,2],[8,0,3,1,10,2],[10,2,1],[1,11,9,11,8,9],[11,3,2,0,9,1],[11,0,2,11,8,0],[11,3,2],[8,1,3,8,9,1],[9,1,0],[8,0,3],[]],this.edgeTable2=[0,265,515,778,2060,2309,2575,2822,1030,1295,1541,1804,3082,3331,3593,3840,400,153,915,666,2460,2197,2975,2710,1430,1183,1941,1692,3482,3219,3993,3728,560,825,51,314,2620,2869,2111,2358,1590,1855,1077,1340,3642,3891,3129,3376,928,681,419,170,2988,2725,2479,2214,1958,1711,1445,1196,4010,3747,3497,3232,2240,2505,2755,3018,204,453,719,966,3270,3535,3781,4044,1226,1475,1737,1984,2384,2137,2899,2650,348,85,863,598,3414,3167,3925,3676,1370,1107,1881,1616,2800,3065,2291,2554,764,1013,255,502,3830,4095,3317,3580,1786,2035,1273,1520,2912,2665,2403,2154,876,613,367,102,3942,3695,3429,3180,1898,1635,1385,1120,1120,1385,1635,1898,3180,3429,3695,3942,102,367,613,876,2154,2403,2665,2912,1520,1273,2035,1786,3580,3317,4095,3830,502,255,1013,764,2554,2291,3065,2800,1616,1881,1107,1370,3676,3925,3167,3414,598,863,85,348,2650,2899,2137,2384,1984,1737,1475,1226,4044,3781,3535,3270,966,719,453,204,3018,2755,2505,2240,3232,3497,3747,4010,1196,1445,1711,1958,2214,2479,2725,2988,170,419,681,928,3376,3129,3891,3642,1340,1077,1855,1590,2358,2111,2869,2620,314,51,825,560,3728,3993,3219,3482,1692,1941,1183,1430,2710,2975,2197,2460,666,915,153,400,3840,3593,3331,3082,1804,1541,1295,1030,2822,2575,2309,2060,778,515,265,0],this.triTable2=[[],[8,3,0],[9,0,1],[8,3,1,8,1,9],[11,2,3],[11,2,0,11,0,8],[11,2,3,0,1,9],[2,1,11,1,9,11,11,9,8],[10,1,2],[8,3,0,1,2,10],[9,0,2,9,2,10],[3,2,8,2,10,8,8,10,9],[10,1,3,10,3,11],[1,0,10,0,8,10,10,8,11],[0,3,9,3,11,9,9,11,10],[8,10,9,8,11,10],[8,4,7],[3,0,4,3,4,7],[1,9,0,8,4,7],[9,4,1,4,7,1,1,7,3],[2,3,11,7,8,4],[7,11,4,11,2,4,4,2,0],[3,11,2,4,7,8,9,0,1],[2,7,11,2,1,7,1,4,7,1,9,4],[10,1,2,8,4,7],[2,10,1,0,4,7,0,7,3],[4,7,8,0,2,10,0,10,9],[2,7,3,2,9,7,7,9,4,2,10,9],[8,4,7,11,10,1,11,1,3],[11,4,7,1,4,11,1,11,10,1,0,4],[3,8,0,7,11,4,11,9,4,11,10,9],[7,11,4,4,11,9,11,10,9],[9,5,4],[3,0,8,4,9,5],[5,4,0,5,0,1],[4,8,5,8,3,5,5,3,1],[11,2,3,9,5,4],[9,5,4,8,11,2,8,2,0],[3,11,2,1,5,4,1,4,0],[8,5,4,2,5,8,2,8,11,2,1,5],[2,10,1,9,5,4],[0,8,3,5,4,9,10,1,2],[10,5,2,5,4,2,2,4,0],[3,4,8,3,2,4,2,5,4,2,10,5],[5,4,9,1,3,11,1,11,10],[0,9,1,4,8,5,8,10,5,8,11,10],[3,4,0,3,10,4,4,10,5,3,11,10],[4,8,5,5,8,10,8,11,10],[9,5,7,9,7,8],[0,9,3,9,5,3,3,5,7],[8,0,7,0,1,7,7,1,5],[1,7,3,1,5,7],[11,2,3,8,9,5,8,5,7],[9,2,0,9,7,2,2,7,11,9,5,7],[0,3,8,2,1,11,1,7,11,1,5,7],[2,1,11,11,1,7,1,5,7],[1,2,10,5,7,8,5,8,9],[9,1,0,10,5,2,5,3,2,5,7,3],[5,2,10,8,2,5,8,5,7,8,0,2],[10,5,2,2,5,3,5,7,3],[3,9,1,3,8,9,7,11,10,7,10,5],[9,1,0,10,7,11,10,5,7],[3,8,0,7,10,5,7,11,10],[11,5,7,11,10,5],[11,7,6],[0,8,3,11,7,6],[9,0,1,11,7,6],[7,6,11,3,1,9,3,9,8],[2,3,7,2,7,6],[8,7,0,7,6,0,0,6,2],[1,9,0,3,7,6,3,6,2],[7,6,2,7,2,9,2,1,9,7,9,8],[1,2,10,6,11,7],[2,10,1,7,6,11,8,3,0],[11,7,6,10,9,0,10,0,2],[7,6,11,3,2,8,8,2,10,8,10,9],[6,10,7,10,1,7,7,1,3],[6,10,1,6,1,7,7,1,0,7,0,8],[9,0,3,6,9,3,6,10,9,6,3,7],[6,10,7,7,10,8,10,9,8],[8,4,6,8,6,11],[11,3,6,3,0,6,6,0,4],[0,1,9,4,6,11,4,11,8],[1,9,4,11,1,4,11,3,1,11,4,6],[3,8,2,8,4,2,2,4,6],[2,0,4,2,4,6],[1,9,0,3,8,2,2,8,4,2,4,6],[9,4,1,1,4,2,4,6,2],[10,1,2,11,8,4,11,4,6],[10,1,2,11,3,6,6,3,0,6,0,4],[0,2,10,0,10,9,4,11,8,4,6,11],[2,11,3,6,9,4,6,10,9],[8,4,6,8,6,1,6,10,1,8,1,3],[1,0,10,10,0,6,0,4,6],[8,0,3,9,6,10,9,4,6],[10,4,6,10,9,4],[9,5,4,7,6,11],[4,9,5,3,0,8,11,7,6],[6,11,7,4,0,1,4,1,5],[6,11,7,4,8,5,5,8,3,5,3,1],[4,9,5,6,2,3,6,3,7],[9,5,4,8,7,0,0,7,6,0,6,2],[4,0,1,4,1,5,6,3,7,6,2,3],[7,4,8,5,2,1,5,6,2],[6,11,7,1,2,10,9,5,4],[11,7,6,8,3,0,1,2,10,9,5,4],[11,7,6,10,5,2,2,5,4,2,4,0],[7,4,8,2,11,3,10,5,6],[4,9,5,6,10,7,7,10,1,7,1,3],[5,6,10,0,9,1,8,7,4],[5,6,10,7,0,3,7,4,0],[10,5,6,4,8,7],[5,6,9,6,11,9,9,11,8],[0,9,5,0,5,3,3,5,6,3,6,11],[0,1,5,0,5,11,5,6,11,0,11,8],[11,3,6,6,3,5,3,1,5],[9,5,6,3,9,6,3,8,9,3,6,2],[5,6,9,9,6,0,6,2,0],[0,3,8,2,5,6,2,1,5],[1,6,2,1,5,6],[1,2,10,5,6,9,9,6,11,9,11,8],[1,0,9,6,10,5,11,3,2],[6,10,5,2,8,0,2,11,8],[3,2,11,10,5,6],[10,5,6,9,3,8,9,1,3],[0,9,1,5,6,10],[8,0,3,10,5,6],[10,5,6],[10,6,5],[8,3,0,10,6,5],[0,1,9,5,10,6],[10,6,5,9,8,3,9,3,1],[3,11,2,10,6,5],[6,5,10,2,0,8,2,8,11],[1,9,0,6,5,10,11,2,3],[1,10,2,5,9,6,9,11,6,9,8,11],[1,2,6,1,6,5],[0,8,3,2,6,5,2,5,1],[5,9,6,9,0,6,6,0,2],[9,6,5,3,6,9,3,9,8,3,2,6],[11,6,3,6,5,3,3,5,1],[0,5,1,0,11,5,5,11,6,0,8,11],[0,5,9,0,3,5,3,6,5,3,11,6],[5,9,6,6,9,11,9,8,11],[10,6,5,4,7,8],[5,10,6,7,3,0,7,0,4],[5,10,6,0,1,9,8,4,7],[4,5,9,6,7,10,7,1,10,7,3,1],[7,8,4,2,3,11,10,6,5],[11,6,7,10,2,5,2,4,5,2,0,4],[11,6,7,8,0,3,1,10,2,9,4,5],[6,7,11,1,10,2,9,4,5],[7,8,4,5,1,2,5,2,6],[4,1,0,4,5,1,6,7,3,6,3,2],[9,4,5,8,0,7,0,6,7,0,2,6],[4,5,9,6,3,2,6,7,3],[6,7,11,4,5,8,5,3,8,5,1,3],[6,7,11,4,1,0,4,5,1],[4,5,9,3,8,0,11,6,7],[9,4,5,7,11,6],[10,6,4,10,4,9],[8,3,0,9,10,6,9,6,4],[1,10,0,10,6,0,0,6,4],[8,6,4,8,1,6,6,1,10,8,3,1],[2,3,11,6,4,9,6,9,10],[0,10,2,0,9,10,4,8,11,4,11,6],[10,2,1,11,6,3,6,0,3,6,4,0],[10,2,1,11,4,8,11,6,4],[9,1,4,1,2,4,4,2,6],[1,0,9,3,2,8,2,4,8,2,6,4],[2,4,0,2,6,4],[3,2,8,8,2,4,2,6,4],[1,4,9,11,4,1,11,1,3,11,6,4],[0,9,1,4,11,6,4,8,11],[11,6,3,3,6,0,6,4,0],[8,6,4,8,11,6],[6,7,10,7,8,10,10,8,9],[9,3,0,6,3,9,6,9,10,6,7,3],[6,1,10,6,7,1,7,0,1,7,8,0],[6,7,10,10,7,1,7,3,1],[7,11,6,3,8,2,8,10,2,8,9,10],[11,6,7,10,0,9,10,2,0],[2,1,10,7,11,6,8,0,3],[1,10,2,6,7,11],[7,2,6,7,9,2,2,9,1,7,8,9],[1,0,9,3,6,7,3,2,6],[8,0,7,7,0,6,0,2,6],[2,7,3,2,6,7],[7,11,6,3,9,1,3,8,9],[9,1,0,11,6,7],[0,3,8,11,6,7],[11,6,7],[11,7,5,11,5,10],[3,0,8,7,5,10,7,10,11],[9,0,1,10,11,7,10,7,5],[3,1,9,3,9,8,7,10,11,7,5,10],[10,2,5,2,3,5,5,3,7],[5,10,2,8,5,2,8,7,5,8,2,0],[9,0,1,10,2,5,5,2,3,5,3,7],[1,10,2,5,8,7,5,9,8],[2,11,1,11,7,1,1,7,5],[0,8,3,2,11,1,1,11,7,1,7,5],[9,0,2,9,2,7,2,11,7,9,7,5],[11,3,2,8,5,9,8,7,5],[1,3,7,1,7,5],[8,7,0,0,7,1,7,5,1],[0,3,9,9,3,5,3,7,5],[9,7,5,9,8,7],[4,5,8,5,10,8,8,10,11],[3,0,4,3,4,10,4,5,10,3,10,11],[0,1,9,4,5,8,8,5,10,8,10,11],[5,9,4,1,11,3,1,10,11],[3,8,4,3,4,2,2,4,5,2,5,10],[10,2,5,5,2,4,2,0,4],[0,3,8,5,9,4,10,2,1],[2,1,10,9,4,5],[8,4,5,2,8,5,2,11,8,2,5,1],[3,2,11,1,4,5,1,0,4],[9,4,5,8,2,11,8,0,2],[11,3,2,9,4,5],[4,5,8,8,5,3,5,1,3],[5,0,4,5,1,0],[3,8,0,4,5,9],[9,4,5],[7,4,11,4,9,11,11,9,10],[3,0,8,7,4,11,11,4,9,11,9,10],[11,7,4,1,11,4,1,10,11,1,4,0],[8,7,4,11,1,10,11,3,1],[2,3,7,2,7,9,7,4,9,2,9,10],[4,8,7,0,10,2,0,9,10],[2,1,10,0,7,4,0,3,7],[10,2,1,8,7,4],[2,11,7,2,7,1,1,7,4,1,4,9],[3,2,11,4,8,7,9,1,0],[7,4,11,11,4,2,4,0,2],[2,11,3,7,4,8],[9,1,4,4,1,7,1,3,7],[1,0,9,8,7,4],[3,4,0,3,7,4],[8,7,4],[8,9,10,8,10,11],[0,9,3,3,9,11,9,10,11],[1,10,0,0,10,8,10,11,8],[10,3,1,10,11,3],[3,8,2,2,8,10,8,9,10],[9,2,0,9,10,2],[8,0,3,1,10,2],[10,2,1],[2,11,1,1,11,9,11,8,9],[11,3,2,0,9,1],[11,0,2,11,8,0],[11,3,2],[8,1,3,8,9,1],[9,1,0],[8,0,3],[]]}march(t,e,i,r){let s=!!r.fulltable,n=r.hasOwnProperty("origin")&&r.origin.hasOwnProperty("x")?r.origin:{x:0,y:0,z:0},a=!!r.voxel,o=r.matrix,l=r.nX||0,h=r.nY||0,c=r.nZ||0,d=r.scale||1,u=null;u=r.unitCube?r.unitCube:{x:d,y:d,z:d};let f,p,g=new Int32Array(l*h*c);for(f=0,p=g.length;f<p;++f)g[f]=-1;let m=function(t,i,r,s,l,d){let f={x:0,y:0,z:0},p=l;if(!!!(s&1<<l)&&!!(s&1<<d)&&(p=d),1&p&&r++,2&p&&i++,4&p&&t++,o){let e=new $.Vector3(t,i,r);e=e.applyMatrix4(o),f={x:e.x,y:e.y,z:e.z}}else f.x=n.x+u.x*t,f.y=n.y+u.y*i,f.z=n.z+u.z*r;let m=(h*t+i)*c+r;return a?(e.push(f),e.length-1):(g[m]<0&&(g[m]=e.length,e.push(f)),g[m])},v=new Int32Array(12),_=s?this.edgeTable2:this.edgeTable,y=s?this.triTable2:this.triTable;for(f=0;f<l-1;++f)for(let r=0;r<h-1;++r)for(let s=0;s<c-1;++s){let n=0;for(let e=0;e<8;++e){n|=!!(t[(h*(f+((4&e)>>2))+r+((2&e)>>1))*c+s+(1&e)]&this.ISDONE)<<e}if(0===n||255===n)continue;let o=_[n];if(0===o)continue;let l=y[n];1&o&&(v[0]=m(f,r,s,n,0,1)),2&o&&(v[1]=m(f,r,s,n,1,3)),4&o&&(v[2]=m(f,r,s,n,3,2)),8&o&&(v[3]=m(f,r,s,n,2,0)),16&o&&(v[4]=m(f,r,s,n,4,5)),32&o&&(v[5]=m(f,r,s,n,5,7)),64&o&&(v[6]=m(f,r,s,n,7,6)),128&o&&(v[7]=m(f,r,s,n,6,4)),256&o&&(v[8]=m(f,r,s,n,0,4)),512&o&&(v[9]=m(f,r,s,n,1,5)),1024&o&&(v[10]=m(f,r,s,n,3,7)),2048&o&&(v[11]=m(f,r,s,n,2,6));for(let t=0;t<l.length;t+=3){let r=v[l[t]],s=v[l[t+1]],n=v[l[t+2]];a&&t>=3&&(e.push(e[r]),r=e.length-1,e.push(e[s]),s=e.length-1,e.push(e[n]),n=e.length-1),i.push(r),i.push(s),i.push(n)}}}laplacianSmooth(t,e,i){let r,s,n,a,o,l=new Array(e.length);for(r=0,s=e.length;r<s;r++)l[r]={x:0,y:0,z:0};let h,c=new Array(20);for(r=0;r<20;r++)c[r]=new Array(e.length);for(r=0,s=e.length;r<s;r++)c[0][r]=0;for(r=0,s=i.length/3;r<s;r++){let t=3*r,e=3*r+1,s=3*r+2;for(h=!0,n=0,a=c[0][i[t]];n<a;n++)if(i[e]==c[n+1][i[t]]){h=!1;break}for(h&&(c[0][i[t]]++,c[c[0][i[t]]][i[t]]=i[e]),h=!0,n=0,a=c[0][i[t]];n<a;n++)if(i[s]==c[n+1][i[t]]){h=!1;break}for(h&&(c[0][i[t]]++,c[c[0][i[t]]][i[t]]=i[s]),h=!0,n=0,a=c[0][i[e]];n<a;n++)if(i[t]==c[n+1][i[e]]){h=!1;break}for(h&&(c[0][i[e]]++,c[c[0][i[e]]][i[e]]=i[t]),h=!0,n=0,a=c[0][i[e]];n<a;n++)if(i[s]==c[n+1][i[e]]){h=!1;break}for(h&&(c[0][i[e]]++,c[c[0][i[e]]][i[e]]=i[s]),h=!0,n=0;n<c[0][i[s]];n++)if(i[t]==c[n+1][i[s]]){h=!1;break}for(h&&(c[0][i[s]]++,c[c[0][i[s]]][i[s]]=i[t]),h=!0,n=0,a=c[0][i[s]];n<a;n++)if(i[e]==c[n+1][i[s]]){h=!1;break}h&&(c[0][i[s]]++,c[c[0][i[s]]][i[s]]=i[e])}let d=.5;for(o=0;o<t;o++){for(r=0,s=e.length;r<s;r++)if(c[0][r]<3)l[r].x=e[r].x,l[r].y=e[r].y,l[r].z=e[r].z;else if(3==c[0][r]||4==c[0][r]){for(l[r].x=0,l[r].y=0,l[r].z=0,n=0,a=c[0][r];n<a;n++)l[r].x+=e[c[n+1][r]].x,l[r].y+=e[c[n+1][r]].y,l[r].z+=e[c[n+1][r]].z;l[r].x+=d*e[r].x,l[r].y+=d*e[r].y,l[r].z+=d*e[r].z,l[r].x/=d+c[0][r],l[r].y/=d+c[0][r],l[r].z/=d+c[0][r]}else{for(l[r].x=0,l[r].y=0,l[r].z=0,n=0,a=c[0][r];n<a;n++)l[r].x+=e[c[n+1][r]].x,l[r].y+=e[c[n+1][r]].y,l[r].z+=e[c[n+1][r]].z;l[r].x+=1*e[r].x,l[r].y+=1*e[r].y,l[r].z+=1*e[r].z,l[r].x/=1+c[0][r],l[r].y/=1+c[0][r],l[r].z/=1+c[0][r]}for(r=0,s=e.length;r<s;r++)e[r].x=l[r].x,e[r].y=l[r].y,e[r].z=l[r].z}}}let MarchingCube=new MarchingCubeInitializer;class PointGrid{constructor(t,e,i){this.data=new Int32Array(t*e*i*3),this.width=e,this.height=i}set(t,e,i,r){let s=3*((t*this.width+e)*this.height+i);this.data[s]=r.ix,this.data[s+1]=r.iy,this.data[s+2]=r.iz}get(t,e,i){let r=3*((t*this.width+e)*this.height+i);return{ix:this.data[r],iy:this.data[r+1],iz:this.data[r+2]}}}class ProteinSurface{constructor(){this.INOUT=1,this.ISDONE=2,this.ISBOUND=4,this.ptranx=0,this.ptrany=0,this.ptranz=0,this.probeRadius=1.4,this.defaultScaleFactor=2,this.scaleFactor=this.defaultScaleFactor,this.pHeight=0,this.pWidth=0,this.pLength=0,this.cutRadius=0,this.vpBits=null,this.vpDistance=null,this.vpAtomID=null,this.pminx=0,this.pminy=0,this.pminz=0,this.pmaxx=0,this.pmaxy=0,this.pmaxz=0,this.depty={},this.widxz={},this.faces=[],this.verts=[],this.vdwRadii={H:1.2,Li:1.82,Na:2.27,K:2.75,C:1.7,N:1.55,O:1.52,F:1.47,P:1.8,S:1.8,CL:1.75,BR:1.85,SE:1.9,ZN:1.39,CU:1.4,NI:1.63,X:2},this.nb=[new Int32Array([1,0,0]),new Int32Array([-1,0,0]),new Int32Array([0,1,0]),new Int32Array([0,-1,0]),new Int32Array([0,0,1]),new Int32Array([0,0,-1]),new Int32Array([1,1,0]),new Int32Array([1,-1,0]),new Int32Array([-1,1,0]),new Int32Array([-1,-1,0]),new Int32Array([1,0,1]),new Int32Array([1,0,-1]),new Int32Array([-1,0,1]),new Int32Array([-1,0,-1]),new Int32Array([0,1,1]),new Int32Array([0,1,-1]),new Int32Array([0,-1,1]),new Int32Array([0,-1,-1]),new Int32Array([1,1,1]),new Int32Array([1,1,-1]),new Int32Array([1,-1,1]),new Int32Array([-1,1,1]),new Int32Array([1,-1,-1]),new Int32Array([-1,-1,1]),new Int32Array([-1,1,-1]),new Int32Array([-1,-1,-1])],ProteinSurface.MarchingCube||(ProteinSurface.MarchingCube=new MarchingCubeInitializer)}getVDWIndex(t){return t.elem&&void 0!==this.vdwRadii[t.elem]?t.elem:"X"}getFacesAndVertices(t){let e={};for(let i=0,r=t.length;i<r;i++)e[t[i]]=!0;let i=this.verts;for(let t=0,e=i.length;t<e;t++)i[t].x=i[t].x/this.scaleFactor-this.ptranx,i[t].y=i[t].y/this.scaleFactor-this.ptrany,i[t].z=i[t].z/this.scaleFactor-this.ptranz;let r=[];for(let t=0,s=this.faces.length;t<s;t+=3){let s=this.faces[t],n=this.faces[t+1],a=this.faces[t+2],o=i[s].atomid,l=i[n].atomid,h=i[a].atomid,c=o;l<c&&(c=l),h<c&&(c=h),e[c]&&(s!==n&&n!==a&&s!==a&&(r.push(s),r.push(n),r.push(a)))}return this.vpBits=null,this.vpDistance=null,this.vpAtomID=null,{vertices:i,faces:r}}initparm(t,e,i){i>1e6&&(this.scaleFactor=this.defaultScaleFactor/2);let r=1/this.scaleFactor*5.5;this.pminx=t[0][0],this.pmaxx=t[1][0],this.pminy=t[0][1],this.pmaxy=t[1][1],this.pminz=t[0][2],this.pmaxz=t[1][2],e?(this.pminx-=this.probeRadius+r,this.pminy-=this.probeRadius+r,this.pminz-=this.probeRadius+r,this.pmaxx+=this.probeRadius+r,this.pmaxy+=this.probeRadius+r,this.pmaxz+=this.probeRadius+r):(this.pminx-=r,this.pminy-=r,this.pminz-=r,this.pmaxx+=r,this.pmaxy+=r,this.pmaxz+=r),this.pminx=Math.floor(this.pminx*this.scaleFactor)/this.scaleFactor,this.pminy=Math.floor(this.pminy*this.scaleFactor)/this.scaleFactor,this.pminz=Math.floor(this.pminz*this.scaleFactor)/this.scaleFactor,this.pmaxx=Math.ceil(this.pmaxx*this.scaleFactor)/this.scaleFactor,this.pmaxy=Math.ceil(this.pmaxy*this.scaleFactor)/this.scaleFactor,this.pmaxz=Math.ceil(this.pmaxz*this.scaleFactor)/this.scaleFactor,this.ptranx=-this.pminx,this.ptrany=-this.pminy,this.ptranz=-this.pminz,this.pLength=Math.ceil(this.scaleFactor*(this.pmaxx-this.pminx))+1,this.pWidth=Math.ceil(this.scaleFactor*(this.pmaxy-this.pminy))+1,this.pHeight=Math.ceil(this.scaleFactor*(this.pmaxz-this.pminz))+1,this.boundingatom(e),this.cutRadius=this.probeRadius*this.scaleFactor,this.vpBits=new Uint8Array(this.pLength*this.pWidth*this.pHeight),this.vpDistance=new Float64Array(this.pLength*this.pWidth*this.pHeight),this.vpAtomID=new Int32Array(this.pLength*this.pWidth*this.pHeight)}boundingatom(t){let e={};for(const i in this.vdwRadii){let r=this.vdwRadii[i];e[i]=t?(r+this.probeRadius)*this.scaleFactor+.5:r*this.scaleFactor+.5;let s=e[i]*e[i];this.widxz[i]=Math.floor(e[i])+1,this.depty[i]=new Int32Array(this.widxz[i]*this.widxz[i]);let n=0;for(let t=0;t<this.widxz[i];t++)for(let e=0;e<this.widxz[i];e++){let r=t*t+e*e;if(r>s)this.depty[i][n]=-1;else{let t=Math.sqrt(s-r);this.depty[i][n]=Math.floor(t)}n++}}}fillvoxels(t,e){for(let t=0,e=this.vpBits.length;t<e;t++)this.vpBits[t]=0,this.vpDistance[t]=-1,this.vpAtomID[t]=-1;for(let i in e){let r=t[e[i]];void 0!==r&&this.fillAtom(r,t)}for(let t=0,e=this.vpBits.length;t<e;t++)this.vpBits[t]&this.INOUT&&(this.vpBits[t]|=this.ISDONE)}fillAtom(t,e){let i=Math.floor(.5+this.scaleFactor*(t.x+this.ptranx)),r=Math.floor(.5+this.scaleFactor*(t.y+this.ptrany)),s=Math.floor(.5+this.scaleFactor*(t.z+this.ptranz)),n=this.getVDWIndex(t),a=0,o=this.pWidth*this.pHeight;for(let l=0,h=this.widxz[n];l<h;l++)for(let c=0;c<h;c++){if(-1!=this.depty[n][a])for(let h=-1;h<2;h++)for(let d=-1;d<2;d++)for(let u=-1;u<2;u++)if(0!==h&&0!==d&&0!==u){let f=h*l,p=u*c;for(let l=0;l<=this.depty[n][a];l++){let n=l*d,a=i+f,h=r+n,c=s+p;if(a<0||h<0||c<0||a>=this.pLength||h>=this.pWidth||c>=this.pHeight)continue;let u=a*o+h*this.pHeight+c;if(this.vpBits[u]&this.INOUT){let a=e[this.vpAtomID[u]];if(a.serial!=t.serial){let e=i+f-Math.floor(.5+this.scaleFactor*(a.x+this.ptranx)),o=r+n-Math.floor(.5+this.scaleFactor*(a.y+this.ptrany)),l=s+p-Math.floor(.5+this.scaleFactor*(a.z+this.ptranz));f*f+n*n+p*p<e*e+o*o+l*l&&(this.vpAtomID[u]=t.serial)}}else this.vpBits[u]|=this.INOUT,this.vpAtomID[u]=t.serial}}a++}}fillvoxelswaals(t,e){for(let t=0,e=this.vpBits.length;t<e;t++)this.vpBits[t]&=~this.ISDONE;for(let i in e){let r=t[e[i]];void 0!==r&&this.fillAtomWaals(r,t)}}fillAtomWaals(t,e){let i=0,r=Math.floor(.5+this.scaleFactor*(t.x+this.ptranx)),s=Math.floor(.5+this.scaleFactor*(t.y+this.ptrany)),n=Math.floor(.5+this.scaleFactor*(t.z+this.ptranz)),a=this.getVDWIndex(t),o=this.pWidth*this.pHeight;for(let l=0,h=this.widxz[a];l<h;l++)for(let c=0;c<h;c++){if(-1!=this.depty[a][i])for(let h=-1;h<2;h++)for(let d=-1;d<2;d++)for(let u=-1;u<2;u++)if(0!==h&&0!==d&&0!==u){let f=h*l,p=u*c;for(let l=0;l<=this.depty[a][i];l++){let i=l*d,a=r+f,h=s+i,c=n+p;if(a<0||h<0||c<0||a>=this.pLength||h>=this.pWidth||c>=this.pHeight)continue;let u=a*o+h*this.pHeight+c;if(this.vpBits[u]&this.ISDONE){let a=e[this.vpAtomID[u]];if(a.serial!=t.serial){let e=r+f-Math.floor(.5+this.scaleFactor*(a.x+this.ptranx)),o=s+i-Math.floor(.5+this.scaleFactor*(a.y+this.ptrany)),l=n+p-Math.floor(.5+this.scaleFactor*(a.z+this.ptranz));f*f+i*i+p*p<e*e+o*o+l*l&&(this.vpAtomID[u]=t.serial)}}else this.vpBits[u]|=this.ISDONE,this.vpAtomID[u]=t.serial}}i++}}buildboundary(){let t=this.pWidth*this.pHeight;for(let e=0;e<this.pLength;e++)for(let i=0;i<this.pHeight;i++)for(let r=0;r<this.pWidth;r++){let s=e*t+r*this.pHeight+i;if(this.vpBits[s]&this.INOUT){let n=0;for(;n<26;){let a=e+this.nb[n][0],o=i+this.nb[n][2],l=r+this.nb[n][1];if(a>-1&&a<this.pLength&&l>-1&&l<this.pWidth&&o>-1&&o<this.pHeight&&!(this.vpBits[a*t+l*this.pHeight+o]&this.INOUT)){this.vpBits[s]|=this.ISBOUND;break}n++}}}}fastdistancemap(){let t,e=new PointGrid(this.pLength,this.pWidth,this.pHeight),i=this.pWidth*this.pHeight,r=this.cutRadius*this.cutRadius,s=[],n=[];for(let r=0;r<this.pLength;r++)for(let n=0;n<this.pWidth;n++)for(let a=0;a<this.pHeight;a++)if(t=r*i+n*this.pHeight+a,this.vpBits[t]&=~this.ISDONE,this.vpBits[t]&this.INOUT&&this.vpBits[t]&this.ISBOUND){let i={ix:r,iy:n,iz:a};e.set(r,n,a,i),s.push(i),this.vpDistance[t]=0,this.vpBits[t]|=this.ISDONE,this.vpBits[t]&=~this.ISBOUND}do{n=this.fastoneshell(s,e),s=[];for(let e=0,a=n.length;e<a;e++)t=i*n[e].ix+this.pHeight*n[e].iy+n[e].iz,this.vpBits[t]&=~this.ISBOUND,this.vpDistance[t]<=1.0404*r&&s.push({ix:n[e].ix,iy:n[e].iy,iz:n[e].iz})}while(0!==s.length);s=[],n=[],e=null;let a=this.scaleFactor-.5;a<0&&(a=0);let o=r-.5/(.1+a);for(let e=0;e<this.pLength;e++)for(let r=0;r<this.pWidth;r++)for(let s=0;s<this.pHeight;s++)t=e*i+r*this.pHeight+s,this.vpBits[t]&=~this.ISBOUND,this.vpBits[t]&this.INOUT&&(this.vpBits[t]&this.ISDONE&&!(this.vpBits[t]&this.ISDONE&&this.vpDistance[t]>=o)||(this.vpBits[t]|=this.ISBOUND))}fastoneshell(t,e){let i,r,s,n,a,o,l,h,c,d=[];if(0===t.length)return d;let u={ix:-1,iy:-1,iz:-1},f=this.pWidth*this.pHeight;for(let p=0,g=t.length;p<g;p++){i=t[p].ix,r=t[p].iy,s=t[p].iz,h=e.get(i,r,s);for(let t=0;t<6;t++)u.ix=i+this.nb[t][0],u.iy=r+this.nb[t][1],u.iz=s+this.nb[t][2],u.ix<this.pLength&&u.ix>-1&&u.iy<this.pWidth&&u.iy>-1&&u.iz<this.pHeight&&u.iz>-1&&(c=u.ix*f+this.pHeight*u.iy+u.iz,this.vpBits[c]&this.INOUT&&!(this.vpBits[c]&this.ISDONE)?(e.set(u.ix,u.iy,s+this.nb[t][2],h),n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,this.vpDistance[c]=l,this.vpBits[c]|=this.ISDONE,this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})):this.vpBits[c]&this.INOUT&&this.vpBits[c]&this.ISDONE&&(n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,l<this.vpDistance[c]&&(e.set(u.ix,u.iy,u.iz,h),this.vpDistance[c]=l,this.vpBits[c]&this.ISBOUND||(this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})))))}for(let p=0,g=t.length;p<g;p++){i=t[p].ix,r=t[p].iy,s=t[p].iz,h=e.get(i,r,s);for(let t=6;t<18;t++)u.ix=i+this.nb[t][0],u.iy=r+this.nb[t][1],u.iz=s+this.nb[t][2],u.ix<this.pLength&&u.ix>-1&&u.iy<this.pWidth&&u.iy>-1&&u.iz<this.pHeight&&u.iz>-1&&(c=u.ix*f+this.pHeight*u.iy+u.iz,this.vpBits[c]&this.INOUT&&!(this.vpBits[c]&this.ISDONE)?(e.set(u.ix,u.iy,s+this.nb[t][2],h),n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,this.vpDistance[c]=l,this.vpBits[c]|=this.ISDONE,this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})):this.vpBits[c]&this.INOUT&&this.vpBits[c]&this.ISDONE&&(n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,l<this.vpDistance[c]&&(e.set(u.ix,u.iy,u.iz,h),this.vpDistance[c]=l,this.vpBits[c]&this.ISBOUND||(this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})))))}for(let p=0,g=t.length;p<g;p++){i=t[p].ix,r=t[p].iy,s=t[p].iz,h=e.get(i,r,s);for(let t=18;t<26;t++)u.ix=i+this.nb[t][0],u.iy=r+this.nb[t][1],u.iz=s+this.nb[t][2],u.ix<this.pLength&&u.ix>-1&&u.iy<this.pWidth&&u.iy>-1&&u.iz<this.pHeight&&u.iz>-1&&(c=u.ix*f+this.pHeight*u.iy+u.iz,this.vpBits[c]&this.INOUT&&!(this.vpBits[c]&this.ISDONE)?(e.set(u.ix,u.iy,s+this.nb[t][2],h),n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,this.vpDistance[c]=l,this.vpBits[c]|=this.ISDONE,this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})):this.vpBits[c]&this.INOUT&&this.vpBits[c]&this.ISDONE&&(n=u.ix-h.ix,a=u.iy-h.iy,o=u.iz-h.iz,l=n*n+a*a+o*o,l<this.vpDistance[c]&&(e.set(u.ix,u.iy,u.iz,h),this.vpDistance[c]=l,this.vpBits[c]&this.ISBOUND||(this.vpBits[c]|=this.ISBOUND,d.push({ix:u.ix,iy:u.iy,iz:u.iz})))))}return d}marchingcubeinit(t){for(let e=0,i=this.vpBits.length;e<i;e++)1==t?this.vpBits[e]&=~this.ISBOUND:4==t?(this.vpBits[e]&=~this.ISDONE,this.vpBits[e]&this.ISBOUND&&(this.vpBits[e]|=this.ISDONE),this.vpBits[e]&=~this.ISBOUND):2==t?this.vpBits[e]&this.ISBOUND&&this.vpBits[e]&this.ISDONE?this.vpBits[e]&=~this.ISBOUND:this.vpBits[e]&this.ISBOUND&&!(this.vpBits[e]&this.ISDONE)&&(this.vpBits[e]|=this.ISDONE):3==t&&(this.vpBits[e]&=~this.ISBOUND)}marchingcube(t){this.marchingcubeinit(t),this.verts=[],this.faces=[],ProteinSurface.MarchingCube.march(this.vpBits,this.verts,this.faces,{smooth:1,nX:this.pLength,nY:this.pWidth,nZ:this.pHeight});let e=this.pWidth*this.pHeight;for(let t=0,i=this.verts.length;t<i;t++)this.verts[t].atomid=this.vpAtomID[this.verts[t].x*e+this.pHeight*this.verts[t].y+this.verts[t].z];ProteinSurface.MarchingCube.laplacianSmooth(1,this.verts,this.faces)}}ProteinSurface.MarchingCube=new MarchingCubeInitializer;var it,rt,st=i(848);function nt(t,e){var i,r,s,n,a,o,l,h,c,d=[],u=t;for((u=[]).push(t[0]),i=1,r=t.length-1;i<r;i++)if(a=t[i],o=t[i+1],a.smoothen){var f=new $.Vector3((a.x+o.x)/2,(a.y+o.y)/2,(a.z+o.z)/2);f.atom=a.atom,u.push(f)}else u.push(a);for(u.push(t[t.length-1]),i=-1,s=u.length;i<=s-3;i++)if(n=u[-1===i?0:i],a=u[i+1],o=u[i+2],l=u[i===s-3?s-1:i+3],h=(new $.Vector3).subVectors(o,n).multiplyScalar(.5),c=(new $.Vector3).subVectors(l,a).multiplyScalar(.5),!o.skip)for(var p=0;p<e;p++){var g=1/e*p,m=a.x+g*h.x+g*g*(-3*a.x+3*o.x-2*h.x-c.x)+g*g*g*(2*a.x-2*o.x+h.x+c.x),v=a.y+g*h.y+g*g*(-3*a.y+3*o.y-2*h.y-c.y)+g*g*g*(2*a.y-2*o.y+h.y+c.y),_=a.z+g*h.z+g*g*(-3*a.z+3*o.z-2*h.z-c.z)+g*g*g*(2*a.z-2*o.z+h.z+c.z),y=new $.Vector3(m,v,_);y.atom=p<e/2?a.atom:o.atom,d.push(y)}return d.push(u[u.length-1]),d}!function(t){t[t.NONE=0]="NONE",t[t.FLAT=1]="FLAT",t[t.ROUND=2]="ROUND"}(it||(it={})),function(t){function e(t,e,i){var r,s,n,a,o,l=Math.hypot(t,e);l<1e-4?(s=0,n=1):(s=-t/l,n=e/l),e=-s*t+n*e,(r=Math.hypot(e,i))<1e-4?(a=0,o=1):(a=i/r,o=e/r);var h=new Float32Array(9);return h[0]=n,h[1]=s,h[2]=0,h[3]=-s*o,h[4]=n*o,h[5]=a,h[6]=s*a,h[7]=-n*a,h[8]=o,h}var i=new class CylVertexCache{constructor(){this.cache={};let t,e=[],i=Math.pow(2,4),r=2,s=Math.pow(2,r),n=i/s;for(e[0]=new $.Vector3(-1,0,0),e[n]=new $.Vector3(0,0,1),e[2*n]=new $.Vector3(1,0,0),e[3*n]=new $.Vector3(0,0,-1),r=3;r<=4;r++){for(s=Math.pow(2,r-1),n=i/s,t=0;t<s-1;t++)e[n/2+t*n]=e[t*n].clone().add(e[(t+1)*n]).normalize();t=s-1,e[n/2+t*n]=e[t*n].clone().add(e[0]).normalize()}this.basisVectors=e}getVerticesForRadius(t,e,i){if(void 0!==this.cache&&void 0!==this.cache[t]&&void 0!==this.cache[t][e+i])return this.cache[t][e+i];for(var r,s=this.basisVectors.length,n=[],a=[],o=0;o<s;o++)n.push(this.basisVectors[o].clone().multiplyScalar(t)),n.push(this.basisVectors[o].clone().multiplyScalar(t)),r=this.basisVectors[o].clone().normalize(),a.push(r),a.push(r);var l=[],h=10,c=s;var d,u,f=2*Math.PI,p=Math.PI,g=!1,m=!1;for(u=0;u<=h;u++){g=0===u||u===h,m=5===u;var v=[],_=[];for(d=0;d<=c;d++)if(m){var y=d<c?2*d:0;_.push(y+1),v.push(y)}else{var b=d/c,x=u/h;if(g&&0!==d)g&&v.push(n.length-1);else if(d<c){var w=new $.Vector3;w.x=-t*Math.cos(0+b*f)*Math.sin(0+x*p),w.y=1==e?0:t*Math.cos(0+x*p),w.z=t*Math.sin(0+b*f)*Math.sin(0+x*p),Math.abs(w.x)<1e-5&&(w.x=0),Math.abs(w.y)<1e-5&&(w.y=0),Math.abs(w.z)<1e-5&&(w.z=0),e==it.FLAT?(r=new $.Vector3(0,Math.cos(0+x*p),0)).normalize():(r=new $.Vector3(w.x,w.y,w.z)).normalize(),n.push(w),a.push(r),v.push(n.length-1)}else v.push(n.length-c)}m&&l.push(_),l.push(v)}var A={vertices:n,normals:a,verticesRows:l,w:c,h};return t in this.cache||(this.cache[t]={}),this.cache[t][e+i]=A,A}};t.drawCylinder=function(t,r,s,n,a,o=0,l=0){if(!r||!s)return;let h=function(t){if("string"==typeof t){let e=t;return"flat"==e.toLowerCase()?it.FLAT:"round"==e.toLowerCase()?it.ROUND:it.NONE}return t};o=h(o);var c=(l=h(l))||o;a=a||{r:0,g:0,b:0};var d,u,f,p,g,m,v=e(s.x-r.x,s.y-r.y,s.z-r.z),_=i.getVerticesForRadius(n,l,"to"),y=_.w,b=_.h,x=c?b*y+2:2*y,w=t.updateGeoGroup(x),A=_.vertices,C=_.normals,S=_.verticesRows,M=S[b/2],z=S[b/2+1],T=w.vertices,E=w.vertexArray,L=w.normalArray,F=w.colorArray,I=w.faceArray;for(f=0;f<y;++f){var O=2*f;p=v[0]*A[O].x+v[3]*A[O].y+v[6]*A[O].z,g=v[1]*A[O].x+v[4]*A[O].y+v[7]*A[O].z,m=v[5]*A[O].y+v[8]*A[O].z,d=3*(T+O),u=w.faceidx,E[d]=p+r.x,E[d+1]=g+r.y,E[d+2]=m+r.z,E[d+3]=p+s.x,E[d+4]=g+s.y,E[d+5]=m+s.z,L[d]=p,L[d+3]=p,L[d+1]=g,L[d+4]=g,L[d+2]=m,L[d+5]=m,F[d]=a.r,F[d+3]=a.r,F[d+1]=a.g,F[d+4]=a.g,F[d+2]=a.b,F[d+5]=a.b,I[u]=z[f]+T,I[u+1]=z[f+1]+T,I[u+2]=M[f]+T,I[u+3]=M[f]+T,I[u+4]=z[f+1]+T,I[u+5]=M[f+1]+T,w.faceidx+=6}if(c){var D,k,R,P,U,B,N,G,V,j,H,W,q,Y,Z,X,K,Q,$,J,tt,et,rt,st,nt,at,ot,lt,ht,ct,dt,ut,ft=o?b+1:b/2+1;for(g=l?0:b/2;g<ft;g++)if(g!==b/2){var pt=g<=b/2?s:r,gt=i.getVerticesForRadius(n,l,"to"),mt=i.getVerticesForRadius(n,o,"from");for(pt===s?(A=gt.vertices,C=gt.normals,S=gt.verticesRows):pt==r&&(A=mt.vertices,C=mt.normals,S=mt.verticesRows),p=0;p<y;p++)u=w.faceidx,ht=3*((D=S[g][p+1])+T),ct=3*((k=S[g][p])+T),dt=3*((R=S[g+1][p])+T),ut=3*((P=S[g+1][p+1])+T),U=v[0]*A[D].x+v[3]*A[D].y+v[6]*A[D].z,B=v[0]*A[k].x+v[3]*A[k].y+v[6]*A[k].z,N=v[0]*A[R].x+v[3]*A[R].y+v[6]*A[R].z,G=v[0]*A[P].x+v[3]*A[P].y+v[6]*A[P].z,V=v[1]*A[D].x+v[4]*A[D].y+v[7]*A[D].z,j=v[1]*A[k].x+v[4]*A[k].y+v[7]*A[k].z,H=v[1]*A[R].x+v[4]*A[R].y+v[7]*A[R].z,W=v[1]*A[P].x+v[4]*A[P].y+v[7]*A[P].z,q=v[5]*A[D].y+v[8]*A[D].z,Y=v[5]*A[k].y+v[8]*A[k].z,Z=v[5]*A[R].y+v[8]*A[R].z,X=v[5]*A[P].y+v[8]*A[P].z,E[ht]=U+pt.x,E[ct]=B+pt.x,E[dt]=N+pt.x,E[ut]=G+pt.x,E[ht+1]=V+pt.y,E[ct+1]=j+pt.y,E[dt+1]=H+pt.y,E[ut+1]=W+pt.y,E[ht+2]=q+pt.z,E[ct+2]=Y+pt.z,E[dt+2]=Z+pt.z,E[ut+2]=X+pt.z,F[ht]=a.r,F[ct]=a.r,F[dt]=a.r,F[ut]=a.r,F[ht+1]=a.g,F[ct+1]=a.g,F[dt+1]=a.g,F[ut+1]=a.g,F[ht+2]=a.b,F[ct+2]=a.b,F[dt+2]=a.b,F[ut+2]=a.b,K=v[0]*C[D].x+v[3]*C[D].y+v[6]*C[D].z,Q=v[0]*C[k].x+v[3]*C[k].y+v[6]*C[k].z,$=v[0]*C[R].x+v[3]*C[R].y+v[6]*C[R].z,J=v[0]*C[P].x+v[3]*C[P].y+v[6]*C[P].z,tt=v[1]*C[D].x+v[4]*C[D].y+v[7]*C[D].z,et=v[1]*C[k].x+v[4]*C[k].y+v[7]*C[k].z,rt=v[1]*C[R].x+v[4]*C[R].y+v[7]*C[R].z,st=v[1]*C[P].x+v[4]*C[P].y+v[7]*C[P].z,nt=v[5]*C[D].y+v[8]*C[D].z,at=v[5]*C[k].y+v[8]*C[k].z,ot=v[5]*C[R].y+v[8]*C[R].z,lt=v[5]*C[P].y+v[8]*C[P].z,0===g?(L[ht]=K,L[dt]=$,L[ut]=J,L[ht+1]=tt,L[dt+1]=rt,L[ut+1]=st,L[ht+2]=nt,L[dt+2]=ot,L[ut+2]=lt,I[u]=D+T,I[u+1]=R+T,I[u+2]=P+T,w.faceidx+=3):g===ft-1?(L[ht]=K,L[ct]=Q,L[dt]=$,L[ht+1]=tt,L[ct+1]=et,L[dt+1]=rt,L[ht+2]=nt,L[ct+2]=at,L[dt+2]=ot,I[u]=D+T,I[u+1]=k+T,I[u+2]=R+T,w.faceidx+=3):(L[ht]=K,L[ct]=Q,L[ut]=J,L[ht+1]=tt,L[ct+1]=et,L[ut+1]=st,L[ht+2]=nt,L[ct+2]=at,L[ut+2]=lt,L[ct]=Q,L[dt]=$,L[ut]=J,L[ct+1]=et,L[dt+1]=rt,L[ut+1]=st,L[ct+2]=at,L[dt+2]=ot,L[ut+2]=lt,I[u]=D+T,I[u+1]=k+T,I[u+2]=P+T,I[u+3]=k+T,I[u+4]=R+T,I[u+5]=P+T,w.faceidx+=6)}}w.vertices+=x},t.drawCone=function(t,r,s,n,a){if(!r||!s)return;a=a||{r:0,g:0,b:0};let o=new $.Vector3(s.x-r.x,s.y-r.y,s.z-r.z);var l=e(o.x,o.y,o.z);o=o.normalize();var h,c,d,u,f,p,g=i.basisVectors.length,m=i.basisVectors,v=g+2,_=t.updateGeoGroup(v),y=_.vertices,b=_.vertexArray,x=_.normalArray,w=_.colorArray,A=_.faceArray;for(b[h=3*y]=r.x,b[h+1]=r.y,b[h+2]=r.z,x[h]=-o.x,x[h+1]=-o.y,x[h+2]=-o.z,w[h]=a.r,w[h+1]=a.g,w[h+2]=a.b,b[h+3]=s.x,b[h+4]=s.y,b[h+5]=s.z,x[h+3]=o.x,x[h+4]=o.y,x[h+5]=o.z,w[h+3]=a.r,w[h+4]=a.g,w[h+5]=a.b,h+=6,d=0;d<g;++d){var C=m[d].clone();C.multiplyScalar(n),u=l[0]*C.x+l[3]*C.y+l[6]*C.z,f=l[1]*C.x+l[4]*C.y+l[7]*C.z,p=l[5]*C.y+l[8]*C.z,b[h]=u+r.x,b[h+1]=f+r.y,b[h+2]=p+r.z,x[h]=u,x[h+1]=f,x[h+2]=p,w[h]=a.r,w[h+1]=a.g,w[h+2]=a.b,h+=3}for(_.vertices+=g+2,c=_.faceidx,d=0;d<g;d++){var S=y+2+d,M=y+2+(d+1)%g;A[c]=S,A[c+1]=M,A[c+2]=y,A[c+=3]=S,A[c+1]=M,A[c+2]=y+1,c+=3}_.faceidx+=6*g};var r=new class SphereVertexCache{constructor(){this.cache=new Map}getVerticesForRadius(t,e){e=e||2,this.cache.has(e)||this.cache.set(e,new Map);let i=this.cache.get(e);if(i.has(t))return i.get(t);var r={vertices:[],verticesRows:[],normals:[]},s=16*e,n=10*e;t<1&&(s=10*e,n=8*e);var a,o,l=2*Math.PI,h=Math.PI;for(o=0;o<=n;o++){let e=[];for(a=0;a<=s;a++){let i=a/s,d=o/n,u=-t*Math.cos(0+i*l)*Math.sin(0+d*h),f=t*Math.cos(0+d*h),p=t*Math.sin(0+i*l)*Math.sin(0+d*h);var c=new $.Vector3(u,f,p);c.normalize(),r.vertices.push({x:u,y:f,z:p}),r.normals.push(c),e.push(r.vertices.length-1)}r.verticesRows.push(e)}return i.set(t,r),r}};t.drawSphere=function(t,e,i,s,n){var a=r.getVerticesForRadius(i,n),o=a.vertices,l=a.normals,h=t.updateGeoGroup(o.length),c=h.vertices,d=h.vertexArray,u=h.colorArray,f=h.faceArray,p=h.lineArray,g=h.normalArray;for(let t=0,i=o.length;t<i;++t){let i=3*(c+t),r=o[t];d[i]=r.x+e.x,d[i+1]=r.y+e.y,d[i+2]=r.z+e.z,u[i]=s.r,u[i+1]=s.g,u[i+2]=s.b}h.vertices+=o.length;let m=a.verticesRows,v=m.length-1;for(let t=0;t<v;t++){let e=m[t].length-1;for(let r=0;r<e;r++){let e=h.faceidx,s=h.lineidx,n=m[t][r+1]+c,a=3*n,d=m[t][r]+c,u=3*d,v=m[t+1][r]+c,_=3*v,y=m[t+1][r+1]+c,b=3*y,x=l[n-c],w=l[d-c],A=l[v-c],C=l[y-c];Math.abs(o[n-c].y)===i?(g[a]=x.x,g[_]=A.x,g[b]=C.x,g[a+1]=x.y,g[_+1]=A.y,g[b+1]=C.y,g[a+2]=x.z,g[_+2]=A.z,g[b+2]=C.z,f[e]=n,f[e+1]=v,f[e+2]=y,p[s]=n,p[s+1]=v,p[s+2]=n,p[s+3]=y,p[s+4]=v,p[s+5]=y,h.faceidx+=3,h.lineidx+=6):Math.abs(o[v-c].y)===i?(g[a]=x.x,g[u]=w.x,g[_]=A.x,g[a+1]=x.y,g[u+1]=w.y,g[_+1]=A.y,g[a+2]=x.z,g[u+2]=w.z,g[_+2]=A.z,f[e]=n,f[e+1]=d,f[e+2]=v,p[s]=n,p[s+1]=d,p[s+2]=n,p[s+3]=v,p[s+4]=d,p[s+5]=v,h.faceidx+=3,h.lineidx+=6):(g[a]=x.x,g[u]=w.x,g[b]=C.x,g[a+1]=x.y,g[u+1]=w.y,g[b+1]=C.y,g[a+2]=x.z,g[u+2]=w.z,g[b+2]=C.z,g[u]=w.x,g[_]=A.x,g[b]=C.x,g[u+1]=w.y,g[_+1]=A.y,g[b+1]=C.y,g[u+2]=w.z,g[_+2]=A.z,g[b+2]=C.z,f[e]=n,f[e+1]=d,f[e+2]=y,f[e+3]=d,f[e+4]=v,f[e+5]=y,p[s]=n,p[s+1]=d,p[s+2]=n,p[s+3]=y,p[s+4]=d,p[s+5]=v,p[s+6]=v,p[s+7]=y,h.faceidx+=6,h.lineidx+=8)}}}}(rt||(rt={}));const at=.5,ot=1.3,lt=.8,ht=.4,ct=.4;function dt(t,e,i,r){for(var n,a,o,l,h=0,c=e.length;h<c;h++){l=Math.round(h*(r.length-1)/c),o=s.CC.color(r[l]);var d=t.updateGeoGroup(2),u=d.vertexArray,f=d.colorArray,p=d.faceArray;u[a=3*(n=d.vertices)]=e[h].x,u[a+1]=e[h].y,u[a+2]=e[h].z,u[a+3]=i[h].x,u[a+4]=i[h].y,u[a+5]=i[h].z;for(var g=0;g<6;++g)f[a+3*g]=o.r,f[a+1+3*g]=o.g,f[a+2+3*g]=o.b;if(h>0){var m=[n,n+1,n-1,n-2],v=d.faceidx;p[v]=m[0],p[v+1]=m[1],p[v+2]=m[3],p[v+3]=m[1],p[v+4]=m[2],p[v+5]=m[3],d.faceidx+=6}d.vertices+=2}}function ut(t,e,i,r,n,a,o){o&&"default"!==o||(o="rectangle"),"edged"===o?function(t,e,i,r,n){if(!(e.length<2)){var a,o;if(a=e[0],o=e[e.length-1],a=nt(a,r),o=nt(o,r),!n)return dt(t,a,o,i);var l,h,c,d,u,f,p,g,m,v,_,y,b,x,w,A,C,S,M,z,T,E,L=[],F=[[0,2,-6,-8],[-4,-2,6,4],[7,-1,-5,3],[-3,5,1,-7]];for(b=0,x=a.length;b<x;b++){if(v=Math.round(b*(i.length-1)/x),m=s.CC.color(i[v]),L.push(h=a[b]),L.push(h),L.push(c=o[b]),L.push(c),b<x-1){var I=a[b+1].clone().sub(a[b]);l=o[b].clone().sub(a[b]).cross(I).normalize().multiplyScalar(n)}for(L.push(d=a[b].clone().add(l)),L.push(d),L.push(u=o[b].clone().add(l)),L.push(u),void 0!==h.atom&&(_=h.atom),z=(M=t.updateGeoGroup(8)).vertexArray,T=M.colorArray,E=M.faceArray,z[p=3*(f=M.vertices)]=h.x,z[p+1]=h.y,z[p+2]=h.z,z[p+3]=h.x,z[p+4]=h.y,z[p+5]=h.z,z[p+6]=c.x,z[p+7]=c.y,z[p+8]=c.z,z[p+9]=c.x,z[p+10]=c.y,z[p+11]=c.z,z[p+12]=d.x,z[p+13]=d.y,z[p+14]=d.z,z[p+15]=d.x,z[p+16]=d.y,z[p+17]=d.z,z[p+18]=u.x,z[p+19]=u.y,z[p+20]=u.z,z[p+21]=u.x,z[p+22]=u.y,z[p+23]=u.z,w=0;w<8;++w)T[p+3*w]=m.r,T[p+1+3*w]=m.g,T[p+2+3*w]=m.b;if(b>0){var O=void 0!==y&&void 0!==_&&y.serial!==_.serial;for(w=0;w<4;w++){var D=[f+F[w][0],f+F[w][1],f+F[w][2],f+F[w][3]];if(E[g=M.faceidx]=D[0],E[g+1]=D[1],E[g+2]=D[3],E[g+3]=D[1],E[g+4]=D[2],E[g+5]=D[3],M.faceidx+=6,_.clickable||y.clickable||_.hoverable||y.hoverable){var k=L[D[3]].clone(),R=L[D[0]].clone(),P=L[D[2]].clone(),U=L[D[1]].clone();if(k.atom=L[D[3]].atom||null,P.atom=L[D[2]].atom||null,R.atom=L[D[0]].atom||null,U.atom=L[D[1]].atom||null,O){var B=k.clone().add(R).multiplyScalar(.5),N=P.clone().add(U).multiplyScalar(.5),G=k.clone().add(U).multiplyScalar(.5);w%2==0?((y.clickable||y.hoverable)&&(A=new J.Triangle(B,G,k),C=new J.Triangle(N,P,G),S=new J.Triangle(G,P,k),y.intersectionShape.triangle.push(A),y.intersectionShape.triangle.push(C),y.intersectionShape.triangle.push(S)),(_.clickable||_.hoverable)&&(A=new J.Triangle(R,U,G),C=new J.Triangle(U,N,G),S=new J.Triangle(R,G,B),_.intersectionShape.triangle.push(A),_.intersectionShape.triangle.push(C),_.intersectionShape.triangle.push(S))):((_.clickable||_.hoverable)&&(A=new J.Triangle(B,G,k),C=new J.Triangle(N,P,G),S=new J.Triangle(G,P,k),_.intersectionShape.triangle.push(A),_.intersectionShape.triangle.push(C),_.intersectionShape.triangle.push(S)),(y.clickable||y.hoverable)&&(A=new J.Triangle(R,U,G),C=new J.Triangle(U,N,G),S=new J.Triangle(R,G,B),y.intersectionShape.triangle.push(A),y.intersectionShape.triangle.push(C),y.intersectionShape.triangle.push(S)))}else(_.clickable||_.hoverable)&&(A=new J.Triangle(R,U,k),C=new J.Triangle(U,P,k),_.intersectionShape.triangle.push(A),_.intersectionShape.triangle.push(C))}}}M.vertices+=8,y=_}var V=L.length-8;for(z=(M=t.updateGeoGroup(8)).vertexArray,T=M.colorArray,E=M.faceArray,p=3*(f=M.vertices),g=M.faceidx,b=0;b<4;b++){L.push(L[2*b]),L.push(L[V+2*b]);var j=L[2*b],H=L[V+2*b];z[p+6*b]=j.x,z[p+1+6*b]=j.y,z[p+2+6*b]=j.z,z[p+3+6*b]=H.x,z[p+4+6*b]=H.y,z[p+5+6*b]=H.z,T[p+6*b]=m.r,T[p+1+6*b]=m.g,T[p+2+6*b]=m.b,T[p+3+6*b]=m.r,T[p+4+6*b]=m.g,T[p+5+6*b]=m.b}V+=8,A=[f,f+2,f+6,f+4],C=[f+1,f+5,f+7,f+3],E[g]=A[0],E[g+1]=A[1],E[g+2]=A[3],E[g+3]=A[1],E[g+4]=A[2],E[g+5]=A[3],E[g+6]=C[0],E[g+7]=C[1],E[g+8]=C[3],E[g+9]=C[1],E[g+10]=C[2],E[g+11]=C[3],M.faceidx+=12,M.vertices+=8}}(t,e,i,r,n):"rectangle"!==o&&"oval"!==o&&"parabola"!==o||function(t,e,i,r,n,a,o){var l,h,c,d,u,f,p,g,m,v;if((c=e.length)<2||e[0].length<2)return;for(l=0;l<c;l++)e[l]=nt(e[l],r);if(d=e[0].length,!n)return dt(t,e[0],e[c-1],i);var _=[],y=[],b=[];for(h=0;h<c;h++)_.push(.25+1.5*Math.sqrt((c-1)*h-Math.pow(h,2))/(c-1)),y.push(.5),b.push(2*(Math.pow(h/c,2)-h/c)+.6);var x,w,A,C,S,M,z,T,E,L,F=[];for(h=0;h<2*c-1;h++)F[h]=[h,h+1,h+1-2*c,h-2*c];F[2*c-1]=[h,h+1-2*c,h+1-4*c,h-2*c];let I=t.updateGeoGroup();for(l=0;l<d;l++){let r=t.groups,a=!1;var O,D;for(I=t.updateGeoGroup(2*c),r!=t.groups&&l>0&&(l-=1,a=!0),M=Math.round(l*(i.length-1)/d),S=s.CC.color(i[M]),m=p,v=g,p=[],g=[],u=[],void 0!==e[0][l].atom&&(C=e[0][l].atom,"oval"===o?f=_:"rectangle"===o?f=y:"parabola"===o&&(f=b)),f||(f=y),h=0;h<c;h++)O=l<d-1?e[h][l+1].clone().sub(e[h][l]):e[h][l-1].clone().sub(e[h][l]).negate(),D=h<c-1?e[h+1][l].clone().sub(e[h][l]):e[h-1][l].clone().sub(e[h][l]).negate(),u[h]=D.cross(O).normalize().multiplyScalar(n*f[h]);for(h=0;h<c;h++)p[h]=e[h][l].clone().add(u[h].clone().negate());for(h=0;h<c;h++)g[h]=e[h][l].clone().add(u[h]);for(z=I.vertexArray,T=I.colorArray,E=I.faceArray,w=3*(x=I.vertices),h=0;h<c;h++)z[w+3*h+0]=p[h].x,z[w+3*h+1]=p[h].y,z[w+3*h+2]=p[h].z;for(h=0;h<c;h++)z[w+3*h+0+3*c]=g[c-1-h].x,z[w+3*h+1+3*c]=g[c-1-h].y,z[w+3*h+2+3*c]=g[c-1-h].z;for(h=0;h<2*c;++h)T[w+3*h+0]=S.r,T[w+3*h+1]=S.g,T[w+3*h+2]=S.b;if(l>0&&!a){for(h=0;h<2*c;h++)L=[x+F[h][0],x+F[h][1],x+F[h][2],x+F[h][3]],E[A=I.faceidx]=L[0],E[A+1]=L[1],E[A+2]=L[3],E[A+3]=L[1],E[A+4]=L[2],E[A+5]=L[3],I.faceidx+=6;if(C.clickable||C.hoverable){var k=[];for(h in k.push(new J.Triangle(m[0],p[0],p[c-1])),k.push(new J.Triangle(m[0],p[c-1],m[c-1])),k.push(new J.Triangle(m[c-1],p[c-1],g[c-1])),k.push(new J.Triangle(m[c-1],g[c-1],v[c-1])),k.push(new J.Triangle(g[0],v[0],v[c-1])),k.push(new J.Triangle(g[c-1],g[0],v[c-1])),k.push(new J.Triangle(p[0],m[0],v[0])),k.push(new J.Triangle(g[0],p[0],v[0])),k)C.intersectionShape.triangle.push(k[h])}}I.vertices+=2*c}for(z=I.vertexArray,T=I.colorArray,E=I.faceArray,w=3*(x=I.vertices),A=I.faceidx,l=0;l<c-1;l++)L=[l,l+1,2*c-2-l,2*c-1-l],E[A=I.faceidx]=L[0],E[A+1]=L[1],E[A+2]=L[3],E[A+3]=L[1],E[A+4]=L[2],E[A+5]=L[3],I.faceidx+=6;for(l=0;l<c-1;l++)L=[x-1-l,x-2-l,x-2*c+l+1,x-2*c+l],E[A=I.faceidx]=L[0],E[A+1]=L[1],E[A+2]=L[3],E[A+3]=L[1],E[A+4]=L[2],E[A+5]=L[3],I.faceidx+=6}(t,e,i,r,n,0,o)}function ft(t){return t&&"C"===t.elem&&"CA"===t.atom}function pt(t,e){if(t&&e&&t.chain===e.chain){if(!(t.hetflag||e.hetflag||t.reschain!==e.reschain||t.resi!==e.resi&&t.resi!==e.resi-1))return!0;if(t.resi<e.resi){var i=t.x-e.x,r=t.y-e.y,s=t.z-e.z,n=i*i+r*r+s*s;if("CA"==t.atom&&"CA"==e.atom&&n<16)return!0;if(("P"==t.atom||"P"==e.atom)&&n<64)return!0}}return!1}function gt(t,e,i,r,s){if(null!=e&&0!=e.vertices){s&&(e.initTypedArrays(),e.setUpNormals());var a=new n.MeshDoubleLambertMaterial;a.vertexColors=n.Coloring.FaceColors,"number"==typeof i&&i>=0&&i<1&&(a.transparent=!0,a.opacity=i),a.outline=r;var o=new n.Mesh(e,a);t.add(o)}}function mt(t,e,i,r,s,n,a,o,l){var h,c,d,u,f,p;if(r&&s&&a){var g=s.sub(r);g.normalize();var m=o[l];for(c=l+1;c<o.length&&(m=o[c]).atom!=a.atom;c++);if((m=m?new $.Vector3(m.x,m.y,m.z):new $.Vector3(0,0,0)).sub(r),"arrow start"===a.ss){var v=m.clone().multiplyScalar(.3).cross(s);r.add(v);var _=m.clone().cross(g).normalize();g.rotateAboutVector(_,.43)}for(a.style.cartoon.ribbon?h=a.style.cartoon.thickness||ht:a.style.cartoon.width?h=a.style.cartoon.width:"c"===a.ss?h="P"===a.atom?lt:at:"arrow start"===a.ss?(h=ot,f=!0):h="arrow end"===a.ss||"h"===a.ss&&a.style.cartoon.tubes||"tube start"===a.ss?at:ot,null!=n&&g.dot(n)<0&&g.negate(),g.multiplyScalar(h),c=0;c<e;c++)d=2*c/(e-1)-1,(u=new $.Vector3(r.x+d*g.x,r.y+d*g.y,r.z+d*g.z)).atom=a,i&&"s"===a.ss&&(u.smoothen=!0),t[c].push(u);if(f)for(g.multiplyScalar(2),c=0;c<e;c++)d=2*c/(e-1)-1,(u=new $.Vector3(r.x+d*g.x,r.y+d*g.y,r.z+d*g.z)).atom=a,u.smoothen=!1,u.skip=!0,t[c].push(u);return p=a.style.cartoon.style||"default",t.style?t.style!=p&&(console.log("Warning: a cartoon chain's strand-style is ambiguous"),t.style="default"):t.style=p,"arrow start"!==a.ss&&"arrow end"!==a.ss||(a.ss="s"),f}}const vt={C:!0,CA:!0,O:!0,P:!0,OP2:!0,O2P:!0,"O5'":!0,"O3'":!0,"C5'":!0,"C2'":!0,"O5*":!0,"O3*":!0,"C5*":!0,"C2*":!0,N1:!0,N3:!0},_t={DA:!0,DG:!0,A:!0,G:!0},yt={DT:!0,DC:!0,U:!0,C:!0,T:!0},bt={DA:!0,DG:!0,A:!0,G:!0,DT:!0,DC:!0,U:!0,C:!0,T:!0};function xt(t,e,i,a=10){let o=a,l=a;var h,c,d,u,f,p,g,m,v,_,y,b,x,w,A,C,S,M,z=new n.Geometry(!0),T=new n.Geometry(!0),E=[],F=[],I=1,O=!1,D={};for(var k in r.Gradient.builtinGradients)r.Gradient.builtinGradients.hasOwnProperty(k)&&(D[k]=new r.Gradient.builtinGradients[k](i[1],i[0]));var R=function(t,e){return i&&"spectrum"===e.color?e.colorscheme in D?D[e.colorscheme].valueToHex(t.resi):D.sinebow.valueToHex(t.resi):(0,L.getColorFromStyle)(t,e).getHex()};for(m=0;m<o;m++)F[m]=[];var P=!1,U=!1,B=[];for(m in e){if("C"===(u=e[m]).elem&&"CA"===u.atom){var N=pt(d,u);N&&"s"===u.ss?P=!0:P&&(d&&c&&d.style.cartoon.arrows&&c.style.cartoon.arrows&&(d.ss="arrow end",c.ss="arrow start"),P=!1),N&&("h"===d.ss||"tube start"==d.ss)&&d.style.cartoon.tubes?!U&&"tube start"!=d.ss&&u.style.cartoon.tubes&&(u.ss="tube start",U=!0):U&&("tube start"===d.ss?d.ss="tube end":c&&c.style.cartoon.tubes&&(c.ss="tube end"),U=!1),c=d,d=u}u&&u.atom in vt&&B.push(u)}U&&d.style.cartoon.tubes&&(d.ss="tube end",U=!1);var G=function(e){F[0].length>0&&ut(T,F,E,l,g,0,F.style);var i=[],r=null;if(e){for(m=0;m<o;m++)i[m]=F[m][F[m].length-1];r=E[E.length-1]}for(F=[],m=0;m<o;m++)F[m]=[];if(E=[],e){for(m=0;m<o;m++)F[m].push(i[m]);E.push(r)}gt(t,T,I,O,!0),gt(t,z,I,O,!1),T=new n.Geometry(!0),z=new n.Geometry(!0)};d=void 0;for(var V=0;V<B.length;V++){var j=(u=B[V]).resn.trim(),H=j in bt;if(I=1,h=u.style.cartoon,d&&d.style.cartoon&&(I=d.style.cartoon.opacity),d&&d.style.cartoon&&d.style.cartoon.outline&&(O=d.style.cartoon.outline),!d||!d.style.cartoon||u.style.cartoon&&d.style.cartoon.opacity==u.style.cartoon.opacity||G(d.chain==u.chain),"trace"===h.style){if(u.hetflag);else if("C"===u.elem&&"CA"===u.atom||H&&"P"===u.atom||"BB"===u.atom){if(p=R(u,h),g=(0,L.isNumeric)(h.thickness)?h.thickness:ht,pt(d,u))if(p==f){var W=s.CC.color(p);rt.drawCylinder(z,d,u,g,W,2,2)}else{var q=(new $.Vector3).addVectors(d,u).multiplyScalar(.5),Y=s.CC.color(f),Z=s.CC.color(p);rt.drawCylinder(z,d,q,g,Y,2,0),rt.drawCylinder(z,q,u,g,Z,0,2)}if((!0===u.clickable||u.hoverable)&&void 0!==u.intersectionShape){var X=new $.Vector3(u.x,u.y,u.z);u.intersectionShape.sphere.push(new J.Sphere(X,g))}d=u,f=p}}else{if(ft(u)||H&&("P"===u.atom||0==u.atom.indexOf("O5"))){if(M)if("tube end"===u.ss)M=!1,S=new $.Vector3(u.x,u.y,u.z),rt.drawCylinder(z,C,S,2,s.CC.color(f),1,1),u.ss="h";else{if(d.chain==u.chain&&"tube end"!==d.ss)continue;M=!1,d.ss="h",S=new $.Vector3(d.x,d.y,d.z),rt.drawCylinder(z,C,S,2,s.CC.color(f),1,1)}if(d&&(!pt(d,u)||"tube start"===d.ss)){for("tube start"===d.ss&&(M=!0,C=new $.Vector3(d.x,d.y,d.z),d.ss="h"),A&&(w=b?(new $.Vector3).addVectors(d,b).multiplyScalar(.5):new $.Vector3(d.x,d.y,d.z),rt.drawCylinder(z,w,A,ct,s.CC.color(A.color),0,2),mt(F,o,!0,b,x,y,d,B,V),E.push(p),w=null,A=null),F[0].length>0&&ut(T,F,E,l,g,0,F.style),F=[],m=0;m<o;m++)F[m]=[];E=[]}if(void 0===d||d.rescode!=u.rescode||d.resi!=u.resi){if(A&&null!=d){var K=(w=(new $.Vector3).addVectors(d,u).multiplyScalar(.5)).clone().sub(A).multiplyScalar(.02);w.add(K),rt.drawCylinder(z,w,A,ct,s.CC.color(A.color),0,2),w=null,A=null}p=R(u,h),E.push(p),g=(0,L.isNumeric)(h.thickness)?h.thickness:ht,d=u,(v=new $.Vector3(d.x,d.y,d.z)).resi=d.resi,f=p}!0!==u.clickable&&!0!==u.hoverable||void 0!==u.intersectionShape&&void 0!==u.intersectionShape.triangle||(u.intersectionShape={sphere:null,cylinder:[],line:[],triangle:[]})}else null!=d&&(ft(d)&&"O"===u.atom||H&&"P"===d.atom&&("OP2"===u.atom||"O2P"===u.atom)||H&&0==d.atom.indexOf("O5")&&0==u.atom.indexOf("C5"))?((_=new $.Vector3(u.x,u.y,u.z)).resi=u.resi,"OP2"!==u.atom&&"O2P"!==u.atom||(x=new $.Vector3(u.x,u.y,u.z))):H&&0==u.atom.indexOf("O3")?b=new $.Vector3(u.x,u.y,u.z):("N1"===u.atom&&j in _t||"N3"===u.atom&&j in yt)&&((A=new $.Vector3(u.x,u.y,u.z)).color=(0,L.getColorFromStyle)(u,h).getHex());_&&v&&_.resi===v.resi&&(mt(F,o,!0,v,_,y,d,B,V),y=_,v=null,_=null,E.push(p))}}A&&(w=b?(new $.Vector3).addVectors(d,b).multiplyScalar(.5):new $.Vector3(d.x,d.y,d.z),rt.drawCylinder(z,w,A,ct,s.CC.color(A.color),0,2),mt(F,o,!0,b,x,y,d,B,V),E.push(p)),G(!1)}class GLShape{static finalizeGeo(t){var e=t.updateGeoGroup(0);e.vertices>0&&e.truncateArrayBuffers(!0,!0)}static updateColor(t,e){var i,r,n;e=e||s.CC.color(e),t.colorsNeedUpdate=!0,e.constructor!==Array&&(i=e.r,r=e.g,n=e.b);for(let s in t.geometryGroups){let a=t.geometryGroups[s],o=a.colorArray;for(let t=0,s=a.vertices;t<s;++t){if(e.constructor===Array){let s=e[t];i=s.r,r=s.g,n=s.b}o[3*t]=i,o[3*t+1]=r,o[3*t+2]=n}}}static drawArrow(t,e,i){var r=i.start,s=i.end,n=i.radius,a=i.radiusRatio,o=i.mid,l=i.midpos;if(!r||!s)return;var h=e.updateGeoGroup(51),c=new $.Vector3(s.x,s.y,s.z).sub(r);if(l){let t=c.length();o=l>0?l/t:(t+l)/t}c.multiplyScalar(o);var d=new $.Vector3(r.x,r.y,r.z).add(c),u=c.clone().negate();let f=new $.Vector3(r.x,r.y,r.z);t.intersectionShape.cylinder.push(new J.Cylinder(f,d.clone(),n)),t.intersectionShape.sphere.push(new J.Sphere(f,n));var p=[];p[0]=c.clone(),Math.abs(p[0].x)>1e-4?p[0].y+=1:p[0].x+=1,p[0].cross(c),p[0].normalize(),p[4]=p[0].clone(),p[4].crossVectors(p[0],c),p[4].normalize(),p[8]=p[0].clone().negate(),p[12]=p[4].clone().negate(),p[2]=p[0].clone().add(p[4]).normalize(),p[6]=p[4].clone().add(p[8]).normalize(),p[10]=p[8].clone().add(p[12]).normalize(),p[14]=p[12].clone().add(p[0]).normalize(),p[1]=p[0].clone().add(p[2]).normalize(),p[3]=p[2].clone().add(p[4]).normalize(),p[5]=p[4].clone().add(p[6]).normalize(),p[7]=p[6].clone().add(p[8]).normalize(),p[9]=p[8].clone().add(p[10]).normalize(),p[11]=p[10].clone().add(p[12]).normalize(),p[13]=p[12].clone().add(p[14]).normalize(),p[15]=p[14].clone().add(p[0]).normalize();var g,m,v,_,y,b,x,w,A,C,S,M,z,T,E,L,F,I,O,D,k,R,P=h.vertices,U=h.vertexArray,B=h.faceArray,N=h.normalArray,G=h.lineArray;for(m=0,v=p.length;m<v;++m){g=3*(P+3*m);var V=p[m].clone().multiplyScalar(n).add(r),j=p[m].clone().multiplyScalar(n).add(d),H=p[m].clone().multiplyScalar(n*a).add(d);if(U[g]=V.x,U[g+1]=V.y,U[g+2]=V.z,U[g+3]=j.x,U[g+4]=j.y,U[g+5]=j.z,U[g+6]=H.x,U[g+7]=H.y,U[g+8]=H.z,m>0){var W=U[g-3],q=U[g-2],Y=U[g-1],Z=new $.Vector3(W,q,Y),X=new $.Vector3(s.x,s.y,s.z),K=d.clone(),Q=new $.Vector3(H.x,H.y,H.z);t.intersectionShape.triangle.push(new J.Triangle(Q,X,Z)),t.intersectionShape.triangle.push(new J.Triangle(Z.clone(),K,Q.clone()))}}h.vertices+=48,U[g=3*h.vertices]=r.x,U[g+1]=r.y,U[g+2]=r.z,U[g+3]=d.x,U[g+4]=d.y,U[g+5]=d.z,U[g+6]=s.x,U[g+7]=s.y,U[g+8]=s.z,h.vertices+=3;var tt=h.vertices-3,et=h.vertices-2,it=h.vertices-1,rt=3*tt,st=3*et,nt=3*it;for(m=0,v=p.length-1;m<v;++m){var at=P+3*m;g=3*at,y=h.faceidx,b=h.lineidx,z=3*(x=at),T=3*(w=at+1),E=3*(A=at+2),L=3*(C=at+4),F=3*(S=at+5),I=3*(M=at+3),O=D=p[m],k=R=p[m+1],N[z]=O.x,N[T]=D.x,N[I]=R.x,N[z+1]=O.y,N[T+1]=D.y,N[I+1]=R.y,N[z+2]=O.z,N[T+2]=D.z,N[I+2]=R.z,N[T]=D.x,N[L]=k.x,N[I]=R.x,N[T+1]=D.y,N[L+1]=k.y,N[I+1]=R.y,N[T+2]=D.z,N[L+2]=k.z,N[I+2]=R.z,N[E]=D.x,N[F]=k.x,N[E+1]=D.y,N[F+1]=k.y,N[E+2]=D.z,N[F+2]=k.z,B[y]=x,B[y+1]=w,B[y+2]=M,B[y+3]=w,B[y+4]=C,B[y+5]=M,B[y+6]=x,B[y+7]=M,B[y+8]=tt,B[y+9]=A,B[y+10]=et,B[y+11]=S,B[y+12]=A,B[y+13]=it,B[y+14]=S,G[b]=x,G[b+1]=w,G[b+2]=x,G[b+3]=M,G[b+4]=C,G[b+5]=M,G[b+6]=x,G[b+7]=M,G[b+8]=A,G[b+9]=w,G[b+10]=A,G[b+11]=S,G[b+12]=C,G[b+13]=S,G[b+14]=A,G[b+15]=it,G[b+16]=A,G[b+17]=S,G[b+18]=it,G[b+19]=S,h.faceidx+=15,h.lineidx+=20}_=[P+45,P+46,P+1,P,P+47,P+2],y=h.faceidx,b=h.lineidx,z=3*(x=_[0]),T=3*(w=_[1]),E=3*(A=_[4]),L=3*(C=_[2]),F=3*(S=_[5]),I=3*(M=_[3]),O=D=p[15],k=R=p[0],N[z]=O.x,N[T]=D.x,N[I]=R.x,N[z+1]=O.y,N[T+1]=D.y,N[I+1]=R.y,N[z+2]=O.z,N[T+2]=D.z,N[I+2]=R.z,N[T]=D.x,N[L]=k.x,N[I]=R.x,N[T+1]=D.y,N[L+1]=k.y,N[I+1]=R.y,N[T+2]=D.z,N[L+2]=k.z,N[I+2]=R.z,N[E]=D.x,N[F]=k.x,N[E+1]=D.y,N[F+1]=k.y,N[E+2]=D.z,N[F+2]=k.z,c.normalize(),u.normalize(),N[rt]=u.x,N[st]=N[nt]=c.x,N[rt+1]=u.y,N[st+1]=N[nt+1]=c.y,N[rt+2]=u.z,N[st+2]=N[nt+2]=c.z,B[y]=x,B[y+1]=w,B[y+2]=M,B[y+3]=w,B[y+4]=C,B[y+5]=M,B[y+6]=x,B[y+7]=M,B[y+8]=tt,B[y+9]=A,B[y+10]=et,B[y+11]=S,B[y+12]=A,B[y+13]=it,B[y+14]=S,G[b]=x,G[b+1]=w,G[b+2]=x,G[b+3]=M,G[b+4]=C,G[b+5]=M,G[b+6]=x,G[b+7]=M,G[b+8]=A,G[b+9]=w,G[b+10]=A,G[b+11]=S,G[b+12]=C,G[b+13]=S,G[b+14]=A,G[b+15]=it,G[b+16]=A,G[b+17]=S,G[b+18]=it,G[b+19]=S,h.faceidx+=15,h.lineidx+=20}static updateBoundingFromPoints(t,e,i,r){t.center.set(0,0,0);let s=1/0,n=1/0,a=1/0,o=-1/0,l=-1/0,h=-1/0;t.box&&(s=t.box.min.x,o=t.box.max.x,n=t.box.min.y,l=t.box.max.y,a=t.box.min.z,h=t.box.max.z);for(let t=0,e=r;t<e;t++){var c=i[3*t],d=i[3*t+1],u=i[3*t+2];c<s&&(s=c),d<n&&(n=d),u<a&&(a=u),c>o&&(o=c),d>l&&(l=d),u>h&&(h=u)}t.center.set((o+s)/2,(l+n)/2,(h+a)/2),t.radius=t.center.distanceTo({x:o,y:l,z:h}),t.box={min:{x:s,y:n,z:a},max:{x:o,y:l,z:h}}}static addCustomGeo(t,e,i,r,s){var n,a,o,l,h,c,d,u,f,p=e.addGeoGroup(),g=i.vertexArr,m=i.normalArr,v=i.faceArr;p.vertices=g.length,p.faceidx=v.length;var _=p.vertexArray,y=p.colorArray;for(r.constructor!==Array&&(u=r.r,f=r.g,l=r.b),c=0,d=p.vertices;c<d;++c)n=3*c,a=g[c],_[n]=a.x,_[n+1]=a.y,_[n+2]=a.z,r.constructor===Array&&(u=(h=r[c]).r,f=h.g,l=h.b),y[n]=u,y[n+1]=f,y[n+2]=l;if(s)for(c=0,d=p.faceidx/3;c<d;++c){o=v[n=3*c],l=v[n+1],h=v[n+2];var b=new $.Vector3,x=new $.Vector3,w=new $.Vector3;t.intersectionShape.triangle.push(new J.Triangle(b.copy(g[o]),x.copy(g[l]),w.copy(g[h])))}if(s){var A=new $.Vector3(0,0,0),C=0;for(let t=0;t<e.geometryGroups.length;t++)A.add(e.geometryGroups[t].getCentroid()),C++;A.divideScalar(C),GLShape.updateBoundingFromPoints(t.boundingSphere,{centroid:A},_,p.vertices)}if(p.faceArray=new Uint16Array(v),p.truncateArrayBuffers(!0,!0),m.length<p.vertices)p.setNormals();else{var S,M=p.normalArray=new Float32Array(3*p.vertices);for(c=0,d=p.vertices;c<d;++c)n=3*c,S=m[c],M[n]=S.x,M[n+1]=S.y,M[n+2]=S.z}p.setLineIndices(),p.lineidx=p.lineArray.length}static updateFromStyle(t,e){void 0!==e.color?(t.color=e.color||new s.Color,e.color instanceof s.Color||(t.color=s.CC.color(e.color))):t.color=s.CC.color(0),t.wireframe=!!e.wireframe,t.opacity=e.alpha?(0,$.clamp)(e.alpha,0,1):1,void 0!==e.opacity&&(t.opacity=(0,$.clamp)(e.opacity,0,1)),t.side=void 0!==e.side?e.side:n.DoubleSide,t.linewidth=void 0===e.linewidth?1:e.linewidth,t.clickable=!!e.clickable,t.callback=(0,L.makeFunction)(e.callback),t.hoverable=!!e.hoverable,t.hover_callback=(0,L.makeFunction)(e.hover_callback),t.unhover_callback=(0,L.makeFunction)(e.unhover_callback),t.contextMenuEnabled=!!e.contextMenuEnabled,t.hidden=e.hidden,t.frame=e.frame}constructor(t){this.color=16777215,this.hidden=!1,this.wireframe=!1,this.opacity=1,this.linewidth=1,this.clickable=!1,this.hoverable=!1,this.contextMenuEnabled=!1,this.side=n.DoubleSide,this.stylespec=t||{},this.boundingSphere=new J.Sphere,this.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},GLShape.updateFromStyle(this,this.stylespec),this.components=[],this.shapeObj=null,this.renderedShapeObj=null,this.geo=new n.Geometry(!0),this.linegeo=new n.Geometry(!0)}updateStyle(t){for(var e in t)this.stylespec[e]=t[e];if(GLShape.updateFromStyle(this,this.stylespec),t.voldata&&t.volscheme){(0,L.adjustVolumeStyle)(t);const e=t.volscheme,i=t.voldata,r=s.CC,n=e.range()||[-1,1];this.geo.setColors((function(t,s,a){let o=i.getVal(t,s,a);return r.color(e.valueToHex(o,n))})),delete this.color}}addCustom(t){t.vertexArr=t.vertexArr||[],t.faceArr=t.faceArr||[],t.normalArr=t.normalArr||[],GLShape.drawCustom(this,this.geo,t)}addSphere(t){t.center||(t.center=new $.Vector3(0,0,0)),t.radius=t.radius?(0,$.clamp)(t.radius,0,1/0):1.5,t.color=s.CC.color(t.color),this.intersectionShape.sphere.push(new J.Sphere(t.center,t.radius)),rt.drawSphere(this.geo,t.center,t.radius,t.color,t.quality),this.components.push({centroid:new $.Vector3(t.center.x,t.center.y,t.center.z)});var e=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,e.vertexArray,e.vertices)}addBox(t){var e,i,r,s=t.dimensions||{w:1,h:1,d:1};e="number"==typeof s.w?{x:s.w,y:0,z:0}:s.w,i="number"==typeof s.h?{x:0,y:s.h,z:0}:s.h,r="number"==typeof s.d?{x:0,y:0,z:s.d}:s.d;var n=t.corner;null==n&&(n=void 0!==t.center?{x:t.center.x-.5*(e.x+i.x+r.x),y:t.center.y-.5*(e.y+i.y+r.y),z:t.center.z-.5*(e.z+i.z+r.z)}:{x:0,y:0,z:0});var a=[{x:n.x,y:n.y,z:n.z},{x:n.x+e.x,y:n.y+e.y,z:n.z+e.z},{x:n.x+i.x,y:n.y+i.y,z:n.z+i.z},{x:n.x+e.x+i.x,y:n.y+e.y+i.y,z:n.z+e.z+i.z},{x:n.x+r.x,y:n.y+r.y,z:n.z+r.z},{x:n.x+e.x+r.x,y:n.y+e.y+r.y,z:n.z+e.z+r.z},{x:n.x+i.x+r.x,y:n.y+i.y+r.y,z:n.z+i.z+r.z},{x:n.x+e.x+i.x+r.x,y:n.y+e.y+i.y+r.y,z:n.z+e.z+i.z+r.z}],o=[],l=[];o.splice(o.length,0,a[0],a[1],a[2],a[3]),l.splice(l.length,0,0,2,1,1,2,3);var h=4;o.splice(o.length,0,a[2],a[3],a[6],a[7]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[4],a[5],a[0],a[1]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[6],a[7],a[4],a[5]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[3],a[1],a[7],a[5]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4,o.splice(o.length,0,a[2],a[6],a[0],a[4]),l.splice(l.length,0,h+0,h+2,h+1,h+1,h+2,h+3),h+=4;var c=(0,L.extend)({},t);c.vertexArr=o,c.faceArr=l,c.normalArr=[],GLShape.drawCustom(this,this.geo,c);var d=new $.Vector3;this.components.push({centroid:d.addVectors(a[0],a[7]).multiplyScalar(.5)});var u=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,u.vertexArray,u.vertices)}addCylinder(t){var e,i;e=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),t.end?void 0===(i=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0)).x&&(i.x=3):i=new $.Vector3(0,0,0);var r=t.radius||.1,n=s.CC.color(t.color);this.intersectionShape.cylinder.push(new J.Cylinder(e,i,r)),rt.drawCylinder(this.geo,e,i,r,n,t.fromCap,t.toCap);var a=new $.Vector3;this.components.push({centroid:a.addVectors(e,i).multiplyScalar(.5)});var o=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,o.vertexArray,o.vertices)}addDashedCylinder(t){var e,i;t.dashLength=t.dashLength||.25,t.gapLength=t.gapLength||.25,e=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),t.end?void 0===(i=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0)).x&&(i.x=3):i=new $.Vector3(3,0,0);for(var r=t.radius||.1,n=s.CC.color(t.color),a=Math.sqrt(Math.pow(e.x-i.x,2)+Math.pow(e.y-i.y,2)+Math.pow(e.z-i.z,2)),o=a/(t.gapLength+t.dashLength),l=new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0),h=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0),c=new $.Vector3((i.x-e.x)/(a/t.gapLength),(i.y-e.y)/(a/t.gapLength),(i.z-e.z)/(a/t.gapLength)),d=new $.Vector3((i.x-e.x)/(a/t.dashLength),(i.y-e.y)/(a/t.dashLength),(i.z-e.z)/(a/t.dashLength)),u=0;u<o;u++)h=new $.Vector3(l.x+d.x,l.y+d.y,l.z+d.z),this.intersectionShape.cylinder.push(new J.Cylinder(l,h,r)),rt.drawCylinder(this.geo,l,h,r,n,t.fromCap,t.toCap),l=new $.Vector3(h.x+c.x,h.y+c.y,h.z+c.z);var f=new $.Vector3;this.components.push({centroid:f.addVectors(e,i).multiplyScalar(.5)});var p=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,p.vertexArray,p.vertices)}addCurve(t){t.points=t.points||[],t.smooth=t.smooth||10,void 0===t.fromCap&&(t.fromCap=2),void 0===t.toCap&&(t.toCap=2);var e=nt(t.points,t.smooth);if(e.length<3)console.log("Too few points in addCurve");else{var i=t.radius||.1,r=s.CC.color(t.color),n=0,a=e.length-1,o=e[0].distanceTo(e[1]),l=Math.ceil(2*i/o);if(t.toArrow){let t={start:e[a-=l],end:e[e.length-1],radius:i,color:r,mid:1e-4};this.addArrow(t)}if(t.fromArrow){let t={start:e[n+=l],end:e[0],radius:i,color:r,mid:1e-4};this.addArrow(t)}for(var h=Math.ceil(e.length/2),c={radius:i,color:r,fromCap:2,toCap:2},d=n;d<a;d++)c.start=e[d],c.end=e[d+1],c.fromCap=2,c.toCap=2,d<h?(c.fromCap=2,c.toCap=0):d>h?(c.fromCap=0,c.toCap=2):(c.fromCap=2,c.toCap=2),this.addCylinder(c)}}addLine(t){var e,i;e=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),t.end?void 0===(i=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0)).x&&(i.x=3):i=new $.Vector3(3,0,0);var r=this.geo.updateGeoGroup(2),s=r.vertices,n=3*s,a=r.vertexArray;a[n]=e.x,a[n+1]=e.y,a[n+2]=e.z,a[n+3]=i.x,a[n+4]=i.y,a[n+5]=i.z,r.vertices+=2;var o=r.lineArray,l=r.lineidx;o[l]=s,o[l+1]=s+1,r.lineidx+=2;var h=new $.Vector3;this.components.push({centroid:h.addVectors(e,i).multiplyScalar(.5)}),r=this.geo.updateGeoGroup(0),GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,r.vertexArray,r.vertices)}addArrow(t){if(t.start?t.start=new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):t.start=new $.Vector3(0,0,0),t.dir instanceof $.Vector3&&"number"==typeof t.length){var e=t.dir.clone().multiplyScalar(t.length).add(t.start);t.end=e}else t.end?(t.end=new $.Vector3(t.end.x,t.end.y||0,t.end.z||0),void 0===t.end.x&&(t.end.x=3)):t.end=new $.Vector3(3,0,0);t.radius=t.radius||.1,t.radiusRatio=t.radiusRatio||1.618034,t.mid=0<t.mid&&t.mid<1?t.mid:.618034,GLShape.drawArrow(this,this.geo,t);var i=new $.Vector3;this.components.push({centroid:i.addVectors(t.start,t.end).multiplyScalar(.5)});var r=this.geo.updateGeoGroup(0);GLShape.updateBoundingFromPoints(this.boundingSphere,this.components,r.vertexArray,r.vertices)}static distance_from(t,e){return Math.sqrt(Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2)+Math.pow(t.z-e.z,2))}static inSelectedRegion(t,e,i){for(var r=0;r<e.length;r++)if(GLShape.distance_from(e[r],t)<=i)return!0;return!1}addIsosurface(t,e,i,r){var s,n,a=void 0!==e.isoval&&"number"==typeof e.isoval?e.isoval:0,o=!!e.voxel,l=void 0===e.smoothness?1:e.smoothness,h=t.size.x,c=t.size.y,d=t.size.z,u=new Int16Array(h*c*d),f=t.data;for(s=0,n=u.length;s<n;++s)u[s]=-1;var p=new Uint8Array(h*c*d);for(s=0,n=f.length;s<n;++s){(a>=0?f[s]-a:a-f[s])>0&&(p[s]|=GLShape.ISDONE)}var g=[],m=[];MarchingCube.march(p,g,m,{fulltable:!0,voxel:o,unitCube:t.unit,origin:t.origin,matrix:t.matrix,nX:h,nY:c,nZ:d}),!o&&l>0&&MarchingCube.laplacianSmooth(l,g,m);var v=[],_=[],y=[];if(e.selectedRegion&&void 0===e.coords&&(e.coords=e.selectedRegion),void 0===e.coords&&void 0!==e.selection&&(r?e.coords=r.selectedAtoms(e.selection):console.log("addIsosurface needs viewer is selection provided.")),void 0!==e.coords){var b=e.coords[0].x,x=e.coords[0].y,w=e.coords[0].z,A=e.coords[0].x,C=e.coords[0].y,S=e.coords[0].z;for(let t=0;t<e.coords.length;t++)e.coords[t].x>b?b=e.coords[t].x:e.coords[t].x<A&&(A=e.coords[t].x),e.coords[t].y>x?x=e.coords[t].y:e.coords[t].y<C&&(C=e.coords[t].y),e.coords[t].z>w?w=e.coords[t].z:e.coords[t].z<S&&(S=e.coords[t].z);var M=2;void 0!==e.radius&&(M=e.radius),void 0!==e.selectedOffset&&(M=e.selectedOffset),void 0!==e.seldist&&(M=e.seldist),A-=M,b+=M,C-=M,x+=M,S-=M,w+=M;for(let t=0;t<g.length;t++)g[t].x>A&&g[t].x<b&&g[t].y>C&&g[t].y<x&&g[t].z>S&&g[t].z<w&&GLShape.inSelectedRegion(g[t],e.coords,M)?(v.push(_.length),_.push(g[t])):v.push(-1);for(let t=0;t+2<m.length;t+=3)-1!==v[m[t]]&&-1!==v[m[t+1]]&&-1!==v[m[t+2]]&&(y.push(m[t]-(m[t]-v[m[t]])),y.push(m[t+1]-(m[t+1]-v[m[t+1]])),y.push(m[t+2]-(m[t+2]-v[m[t+2]])));g=_,m=y}GLShape.drawCustom(this,this.geo,{vertexArr:g,faceArr:m,normalArr:[],clickable:e.clickable,hoverable:e.hoverable}),this.updateStyle(e);var z=new $.Vector3(t.origin.x,t.origin.y,t.origin.z),T=new $.Vector3(t.size.x*t.unit.x,t.size.y*t.unit.y,t.size.z*t.unit.z),E=new $.Vector3(0,0,0),L=z.clone(),F=z.clone().add(T);for(let t=0;t<g.length;t++)E.add(g[t]),L.max(g[t]),F.min(g[t]);E.divideScalar(g.length);var I=E.distanceTo(F),O=E.distanceTo(L);this.boundingSphere.center=E,this.boundingSphere.radius=Math.max(I,O),"function"==typeof i&&i()}addVolumetricData(t,e,i){t=new st.VolumeData(t,e),this.addIsosurface(t,i)}finalize(){return GLShape.finalizeGeo(this.geo),this.geo.initTypedArrays(),this.geo}globj(t){if(this.renderedShapeObj&&(t.remove(this.renderedShapeObj),this.renderedShapeObj=null),!this.hidden){GLShape.finalizeGeo(this.geo),this.geo.initTypedArrays(),this.wireframe&&this.geo.setUpWireframe(),void 0!==this.color&&GLShape.updateColor(this.geo,this.color),this.shapeObj=new n.Object3D;var e=null;e=this.side==n.DoubleSide?new n.MeshDoubleLambertMaterial({wireframe:this.wireframe,side:this.side,transparent:this.opacity<1,opacity:this.opacity,wireframeLinewidth:this.linewidth,vertexColors:n.Coloring.VertexColors}):new n.MeshLambertMaterial({wireframe:this.wireframe,side:this.side,transparent:this.opacity<1,opacity:this.opacity,wireframeLinewidth:this.linewidth,vertexColors:n.Coloring.VertexColors});var i=new n.Mesh(this.geo,e);if(this.shapeObj.add(i),this.linegeo&&this.linegeo.vertices>0){var r=new n.LineBasicMaterial({linewidth:this.linewidth,color:this.color}),s=new n.Line(this.linegeo,r,n.LineStyle.LinePieces);this.shapeObj.add(s)}this.renderedShapeObj=this.shapeObj.clone(),t.add(this.renderedShapeObj)}}removegl(t){this.renderedShapeObj&&(void 0!==this.renderedShapeObj.geometry&&this.renderedShapeObj.geometry.dispose(),void 0!==this.renderedShapeObj.material&&this.renderedShapeObj.material.dispose(),t.remove(this.renderedShapeObj),this.renderedShapeObj=null),this.shapeObj=null}get position(){return this.boundingSphere.center}get x(){return this.boundingSphere.center.x}get y(){return this.boundingSphere.center.y}get z(){return this.boundingSphere.center.z}}function wt(t){if(t.vertexArr.length<64e3)return[t];var e=[{vertexArr:[],normalArr:[],faceArr:[]}];t.colorArr&&(e.colorArr=[]);var i=[],r=[],s=0,n=t.faceArr;for(let o=0,l=n.length;o<l;o+=3){let l=e[s];for(let e=0;e<3;e++){var a=n[o+e];i[a]!==s&&(i[a]=s,r[a]=l.vertexArr.length,l.vertexArr.push(t.vertexArr[a]),t.normalArr&&t.normalArr[a]&&l.normalArr.push(t.normalArr[a]),t.colorArr&&t.colorArr[a]&&l.colorArr.push(t.colorArr[a])),l.faceArr.push(r[a])}l.vertexArr.length>=64e3&&(e.push({vertexArr:[],normalArr:[],faceArr:[]}),t.colorArr&&(e.colorArr=[]),s++)}return e}GLShape.ISDONE=2,GLShape.drawCustom=function(t,e,i){var r=i,n=r.vertexArr,a=r.faceArr;0!==n.length&&0!==a.length||console.warn("Error adding custom shape component: No vertices and/or face indices supplied!");var o=i.color;void 0===o&&(o=t.color),o=s.CC.color(o);for(var l=wt(r),h=0,c=l.length;h<c;h++)GLShape.addCustomGeo(t,e,l[h],l[h].colorArr?l[h].colorArr:o,i.clickable)};class GLVolumetricRender{static interpolateArray(t,e){function i(t,e,i){return t+(e-t)*i}var r=[],s=(t.length-1)/(e-1);r[0]=t[0];for(var n=1;n<e-1;n++){var a=n*s,o=Math.floor(a),l=Math.ceil(a),h=a-o;r[n]=i(t[o],t[l],h)}return r[e-1]=t[t.length-1],r}constructor(t,e,i){this.hidden=!1,this.boundingSphere=new J.Sphere,this.renderedShapeObj=null,this.shapeObj=null,this.subsamples=5,this.data=null,this.transferfunctionbuffer=[],this.min=0,this.max=0,e=e||{};var r=Object.assign([],e.transferfn);this.subsamples=e.subsamples||5;var n,a,o,l,h,c,d,u,f,p;r.forEach((function(t){t.value=parseFloat(t.value)})),r.sort((function(t,e){return t.value-e.value})),this.min=r[0].value,0==r.length&&r.push(r[0]),this.max=r[r.length-1].value;for(let t=0;t<r.length-1;t++)if(o=s.CC.color(r[t].color),l=s.CC.color(r[t+1].color),f=r[t].opacity,p=r[t+1].opacity,(n=Math.floor(256*(r[t].value-this.min)/(this.max-this.min)))!=(a=Math.floor(256*(r[t+1].value-this.min)/(this.max-this.min)))){h=GLVolumetricRender.interpolateArray([255*o.r,255*l.r],a-n),c=GLVolumetricRender.interpolateArray([255*o.g,255*l.g],a-n),d=GLVolumetricRender.interpolateArray([255*o.b,255*l.b],a-n),u=GLVolumetricRender.interpolateArray([255*f,255*p],a-n);for(let t=0;t<h.length;t++)this.transferfunctionbuffer.push(h[t]),this.transferfunctionbuffer.push(c[t]),this.transferfunctionbuffer.push(d[t]),this.transferfunctionbuffer.push(u[t])}if(this.transferfunctionbuffer=new Uint8ClampedArray(this.transferfunctionbuffer),t.matrix){let e=new $.Vector3(0,0,0),i=new $.Vector3(t.size.x,t.size.y,t.size.z),r=new $.Vector3(1,1,1);e.applyMatrix4(t.matrix),i.applyMatrix4(t.matrix),r.applyMatrix4(t.matrix).sub(e),this.extent=[[e.x,e.y,e.z],[i.x,i.y,i.z]];for(let e=1;e<7;e++)i.x=1&e?t.size.x:0,i.y=2&e?t.size.y:0,i.z=4&e?t.size.z:0,i.applyMatrix4(t.matrix),this.extent[0][0]=Math.min(this.extent[0][0],i.x),this.extent[0][1]=Math.min(this.extent[0][1],i.y),this.extent[0][2]=Math.min(this.extent[0][2],i.z),this.extent[1][0]=Math.max(this.extent[1][0],i.x),this.extent[1][1]=Math.max(this.extent[1][1],i.y),this.extent[1][2]=Math.max(this.extent[1][2],i.z);let s=i.x-e.x,n=i.y-e.y,a=i.z-e.z;this.maxdepth=Math.sqrt(s*s+n*n+a*a),this.minunit=Math.min(Math.min(r.x,r.y),r.z),this.texmatrix=(new $.Matrix4).identity().scale({x:t.size.x,y:t.size.y,z:t.size.z}),this.texmatrix=this.texmatrix.multiplyMatrices(t.matrix,this.texmatrix),this.texmatrix=this.texmatrix.getInverse(this.texmatrix)}else{this.texmatrix=(new $.Matrix4).identity();let e=t.unit.x*t.size.x,i=t.unit.y*t.size.y,r=t.unit.z*t.size.z;this.texmatrix.makeTranslation(-t.origin.x/e,-t.origin.y/i,-t.origin.z/r),this.texmatrix.scale({x:1/e,y:1/i,z:1/r}),this.minunit=Math.min(Math.min(t.unit.x,t.unit.y),t.unit.z),this.extent=[[t.origin.x,t.origin.y,t.origin.z],[t.origin.x+e,t.origin.y+i,t.origin.z+r]],this.maxdepth=Math.sqrt(e*e+i*i+r*r)}var g=new GLShape({});if(g.addBox({corner:{x:this.extent[0][0],y:this.extent[0][1],z:this.extent[0][2]},dimensions:{w:this.extent[1][0]-this.extent[0][0],h:this.extent[1][1]-this.extent[0][1],d:this.extent[1][2]-this.extent[0][2]}}),this.geo=g.finalize(),this.boundingSphere.center=new $.Vector3((this.extent[0][0]+this.extent[1][0])/2,(this.extent[0][1]+this.extent[1][1])/2,(this.extent[0][2]+this.extent[1][2])/2),this.boundingSphere.radius=this.maxdepth/2,void 0===e.coords&&void 0!==e.selection&&(i?e.coords=i.selectedAtoms(e.selection):console.log("Need to provide viewer to volumetric renderer if selection specified.")),void 0!==e.coords&&void 0!==e.seldist){let i=new Uint8Array(t.data.length),r=e.seldist,s=r*r;for(let n=0,a=e.coords.length;n<a;n++){let a=e.coords[n],o=a.x-r,l=a.y-r,h=a.z-r,c=a.x+r,d=a.y+r,u=a.z+r;if(t.getIndex(o,l,h)>=0||t.getIndex(c,d,u)>=0)for(let e=o;e<c;e+=this.minunit)for(let r=l;r<d;r+=this.minunit)for(let n=h;n<u;n+=this.minunit){let o=t.getIndex(e,r,n);if(o>=0&&!i[o]){(e-a.x)*(e-a.x)+(r-a.y)*(r-a.y)+(n-a.z)*(n-a.z)<s&&(i[o]=1)}}}for(let e=0,r=t.data.length;e<r;e++)0==i[e]&&(t.data[e]=1/0)}this.data=t}globj(t){if(this.renderedShapeObj&&(t.remove(this.renderedShapeObj),this.renderedShapeObj=null),!this.hidden){this.shapeObj=new n.Object3D;var e,i=new n.Texture(this.data,!0),r=new n.Texture(this.transferfunctionbuffer,!1);i.needsUpdate=!0,r.needsUpdate=!0,r.flipY=!1,e=new n.VolumetricMaterial({transferfn:r,transfermin:this.min,transfermax:this.max,map:i,extent:this.extent,maxdepth:this.maxdepth,texmatrix:this.texmatrix,unit:this.minunit,subsamples:this.subsamples});var s=new n.Mesh(this.geo,e);this.shapeObj.add(s),this.renderedShapeObj=this.shapeObj.clone(),t.add(this.renderedShapeObj)}}removegl(t){this.renderedShapeObj&&(void 0!==this.renderedShapeObj.geometry&&this.renderedShapeObj.geometry.dispose(),void 0!==this.renderedShapeObj.material&&this.renderedShapeObj.material.dispose(),t.remove(this.renderedShapeObj),this.renderedShapeObj=null),this.shapeObj=null}get position(){return this.boundingSphere.center}get x(){return this.boundingSphere.center.x}get y(){return this.boundingSphere.center.y}get z(){return this.boundingSphere.center.z}}i(620);function At(t,e="utf8"){return new TextDecoder(e).decode(t)}const Ct=new TextEncoder;const St=(()=>{const t=new Uint8Array(4);return!((new Uint32Array(t.buffer)[0]=1)&t[0])})(),Mt={int8:globalThis.Int8Array,uint8:globalThis.Uint8Array,int16:globalThis.Int16Array,uint16:globalThis.Uint16Array,int32:globalThis.Int32Array,uint32:globalThis.Uint32Array,uint64:globalThis.BigUint64Array,int64:globalThis.BigInt64Array,float32:globalThis.Float32Array,float64:globalThis.Float64Array};class IOBuffer{constructor(t=8192,e={}){let i=!1;"number"==typeof t?t=new ArrayBuffer(t):(i=!0,this.lastWrittenByte=t.byteLength);const r=e.offset?e.offset>>>0:0,s=t.byteLength-r;let n=r;(ArrayBuffer.isView(t)||t instanceof IOBuffer)&&(t.byteLength!==t.buffer.byteLength&&(n=t.byteOffset+r),t=t.buffer),this.lastWrittenByte=i?s:0,this.buffer=t,this.length=s,this.byteLength=s,this.byteOffset=n,this.offset=0,this.littleEndian=!0,this._data=new DataView(this.buffer,n,s),this._mark=0,this._marks=[]}available(t=1){return this.offset+t<=this.length}isLittleEndian(){return this.littleEndian}setLittleEndian(){return this.littleEndian=!0,this}isBigEndian(){return!this.littleEndian}setBigEndian(){return this.littleEndian=!1,this}skip(t=1){return this.offset+=t,this}back(t=1){return this.offset-=t,this}seek(t){return this.offset=t,this}mark(){return this._mark=this.offset,this}reset(){return this.offset=this._mark,this}pushMark(){return this._marks.push(this.offset),this}popMark(){const t=this._marks.pop();if(void 0===t)throw new Error("Mark stack empty");return this.seek(t),this}rewind(){return this.offset=0,this}ensureAvailable(t=1){if(!this.available(t)){const e=2*(this.offset+t),i=new Uint8Array(e);i.set(new Uint8Array(this.buffer)),this.buffer=i.buffer,this.length=this.byteLength=e,this._data=new DataView(this.buffer)}return this}readBoolean(){return 0!==this.readUint8()}readInt8(){return this._data.getInt8(this.offset++)}readUint8(){return this._data.getUint8(this.offset++)}readByte(){return this.readUint8()}readBytes(t=1){return this.readArray(t,"uint8")}readArray(t,e){const i=Mt[e].BYTES_PER_ELEMENT*t,r=this.byteOffset+this.offset,s=this.buffer.slice(r,r+i);if(this.littleEndian===St&&"uint8"!==e&&"int8"!==e){const t=new Uint8Array(this.buffer.slice(r,r+i));t.reverse();const s=new Mt[e](t.buffer);return this.offset+=i,s.reverse(),s}const n=new Mt[e](s);return this.offset+=i,n}readInt16(){const t=this._data.getInt16(this.offset,this.littleEndian);return this.offset+=2,t}readUint16(){const t=this._data.getUint16(this.offset,this.littleEndian);return this.offset+=2,t}readInt32(){const t=this._data.getInt32(this.offset,this.littleEndian);return this.offset+=4,t}readUint32(){const t=this._data.getUint32(this.offset,this.littleEndian);return this.offset+=4,t}readFloat32(){const t=this._data.getFloat32(this.offset,this.littleEndian);return this.offset+=4,t}readFloat64(){const t=this._data.getFloat64(this.offset,this.littleEndian);return this.offset+=8,t}readBigInt64(){const t=this._data.getBigInt64(this.offset,this.littleEndian);return this.offset+=8,t}readBigUint64(){const t=this._data.getBigUint64(this.offset,this.littleEndian);return this.offset+=8,t}readChar(){return String.fromCharCode(this.readInt8())}readChars(t=1){let e="";for(let i=0;i<t;i++)e+=this.readChar();return e}readUtf8(t=1){return At(this.readBytes(t))}decodeText(t=1,e="utf-8"){return At(this.readBytes(t),e)}writeBoolean(t){return this.writeUint8(t?255:0),this}writeInt8(t){return this.ensureAvailable(1),this._data.setInt8(this.offset++,t),this._updateLastWrittenByte(),this}writeUint8(t){return this.ensureAvailable(1),this._data.setUint8(this.offset++,t),this._updateLastWrittenByte(),this}writeByte(t){return this.writeUint8(t)}writeBytes(t){this.ensureAvailable(t.length);for(let e=0;e<t.length;e++)this._data.setUint8(this.offset++,t[e]);return this._updateLastWrittenByte(),this}writeInt16(t){return this.ensureAvailable(2),this._data.setInt16(this.offset,t,this.littleEndian),this.offset+=2,this._updateLastWrittenByte(),this}writeUint16(t){return this.ensureAvailable(2),this._data.setUint16(this.offset,t,this.littleEndian),this.offset+=2,this._updateLastWrittenByte(),this}writeInt32(t){return this.ensureAvailable(4),this._data.setInt32(this.offset,t,this.littleEndian),this.offset+=4,this._updateLastWrittenByte(),this}writeUint32(t){return this.ensureAvailable(4),this._data.setUint32(this.offset,t,this.littleEndian),this.offset+=4,this._updateLastWrittenByte(),this}writeFloat32(t){return this.ensureAvailable(4),this._data.setFloat32(this.offset,t,this.littleEndian),this.offset+=4,this._updateLastWrittenByte(),this}writeFloat64(t){return this.ensureAvailable(8),this._data.setFloat64(this.offset,t,this.littleEndian),this.offset+=8,this._updateLastWrittenByte(),this}writeBigInt64(t){return this.ensureAvailable(8),this._data.setBigInt64(this.offset,t,this.littleEndian),this.offset+=8,this._updateLastWrittenByte(),this}writeBigUint64(t){return this.ensureAvailable(8),this._data.setBigUint64(this.offset,t,this.littleEndian),this.offset+=8,this._updateLastWrittenByte(),this}writeChar(t){return this.writeUint8(t.charCodeAt(0))}writeChars(t){for(let e=0;e<t.length;e++)this.writeUint8(t.charCodeAt(e));return this}writeUtf8(t){return this.writeBytes(function(t){return Ct.encode(t)}(t))}toArray(){return new Uint8Array(this.buffer,this.byteOffset,this.lastWrittenByte)}_updateLastWrittenByte(){this.offset>this.lastWrittenByte&&(this.lastWrittenByte=this.offset)}}const zt=1,Tt=2,Et=3,Lt=4,Ft=5,It=6;function Ot(t){switch(Number(t)){case zt:return"byte";case Tt:return"char";case Et:return"short";case Lt:return"int";case Ft:return"float";case It:return"double";default:return"undefined"}}function Dt(t){switch(Number(t)){case zt:case Tt:return 1;case Et:return 2;case Lt:case Ft:return 4;case It:return 8;default:return-1}}function kt(t){switch(String(t)){case"byte":return zt;case"char":return Tt;case"short":return Et;case"int":return Lt;case"float":return Ft;case"double":return It;default:return-1}}function Rt(t,e){if(1!==t){const i=new Array(t);for(let r=0;r<t;r++)i[r]=e();return i}return e()}function Pt(t,e,i){switch(e){case zt:return Array.from(t.readBytes(i));case Tt:return function(t){if(0===t.charCodeAt(t.length-1))return t.substring(0,t.length-1);return t}(t.readChars(i));case Et:return Rt(i,t.readInt16.bind(t));case Lt:return Rt(i,t.readInt32.bind(t));case Ft:return Rt(i,t.readFloat32.bind(t));case It:return Rt(i,t.readFloat64.bind(t));default:throw new Error(`non valid type ${e}`)}}function Ut(t,e){if(t)throw new TypeError(`Not a valid NetCDF v3.x file: ${e}`)}function Bt(t){t.offset%4!=0&&t.skip(4-t.offset%4)}function Nt(t){const e=t.readUint32(),i=t.readChars(e);return Bt(t),i}function Gt(t,e){const i={version:e},r={length:t.readUint32()},s=function(t){const e={};let i,r;const s=t.readUint32();let n;if(0===s)return Ut(0!==t.readUint32(),"wrong empty tag for list of dimensions"),[];{Ut(10!==s,"wrong tag for list of dimensions");const e=t.readUint32();n=new Array(e);for(let s=0;s<e;s++){const e=Nt(t),a=t.readUint32();0===a&&(i=s,r=e),n[s]={name:e,size:a}}}void 0!==i&&(e.recordId=i);void 0!==r&&(e.recordName=r);return e.dimensions=n,e}(t);Array.isArray(s)||(r.id=s.recordId,r.name=s.recordName,i.dimensions=s.dimensions),i.globalAttributes=Vt(t);const n=function(t,e,i){const r=t.readUint32();let s,n=0;if(0===r)return Ut(0!==t.readUint32(),"wrong empty tag for list of variables"),[];{Ut(11!==r,"wrong tag for list of variables");const a=t.readUint32();s=new Array(a);for(let r=0;r<a;r++){const a=Nt(t),o=t.readUint32(),l=new Array(o);for(let e=0;e<o;e++)l[e]=t.readUint32();const h=Vt(t),c=t.readUint32();Ut(c<1&&c>6,`non valid type ${c}`);const d=t.readUint32();let u=t.readUint32();2===i&&(Ut(u>0,"offsets larger than 4GB not supported"),u=t.readUint32());let f=!1;void 0!==e&&l[0]===e&&(n+=d,f=!0),s[r]={name:a,dimensions:l,attributes:h,type:Ot(c),size:d,offset:u,record:f}}}return{variables:s,recordStep:n}}(t,r?.id,e);return Array.isArray(n)||(i.variables=n.variables,r.recordStep=n.recordStep),i.recordDimension=r,i}function Vt(t){const e=t.readUint32();let i;if(0===e)return Ut(0!==t.readUint32(),"wrong empty tag for list of attributes"),[];{Ut(12!==e,"wrong tag for list of attributes");const r=t.readUint32();i=new Array(r);for(let e=0;e<r;e++){const r=Nt(t),s=t.readUint32();Ut(s<1||s>6,`non valid type ${s}`);const n=t.readUint32(),a=Pt(t,s,n);Bt(t),i[e]={name:r,type:Ot(s),value:a}}}return i}function jt(){const t=[];t.push("DIMENSIONS");for(const e of this.dimensions)t.push(`  ${e.name.padEnd(30)} = size: ${e.size}`);t.push(""),t.push("GLOBAL ATTRIBUTES");for(const e of this.globalAttributes)t.push(`  ${e.name.padEnd(30)} = ${e.value}`);const e=JSON.parse(JSON.stringify(this.variables));t.push(""),t.push("VARIABLES:");for(const i of e){i.value=this.getDataVariable(i);let e=JSON.stringify(i.value);e.length>50&&(e=e.substring(0,50)),isNaN(i.value.length)||(e+=` (length: ${i.value.length})`),t.push(`  ${i.name.padEnd(30)} = ${e}`)}return t.join("\n")}class NetCDFReader{constructor(t){this.toString=jt;const e=new IOBuffer(t);e.setBigEndian(),Ut("CDF"!==e.readChars(3),"should start with CDF");const i=e.readByte();Ut(i>2,"unknown version"),this.header=Gt(e,i),this.buffer=e}get version(){return 1===this.header.version?"classic format":"64-bit offset format"}get recordDimension(){return this.header.recordDimension}get dimensions(){return this.header.dimensions}get globalAttributes(){return this.header.globalAttributes}getAttribute(t){const e=this.globalAttributes.find((e=>e.name===t));return e?e.value:null}getDataVariableAsString(t){const e=this.getDataVariable(t);return e?e.join(""):null}get variables(){return this.header.variables}getDataVariable(t){let e;if(e="string"==typeof t?this.header.variables.find((e=>e.name===t)):t,void 0===e)throw new Error("Not a valid NetCDF v3.x file: variable not found");return this.buffer.seek(e.offset),e.record?function(t,e,i){const r=kt(e.type),s=e.size?e.size/Dt(r):1,n=i.length,a=new Array(n),o=i.recordStep;if(!o)throw new Error("recordDimension.recordStep is undefined");for(let e=0;e<n;e++){const i=t.offset;a[e]=Pt(t,r,s),t.seek(i+o)}return a}(this.buffer,e,this.header.recordDimension):function(t,e){const i=kt(e.type),r=e.size/Dt(i),s=new Array(r);for(let e=0;e<r;e++)s[e]=Pt(t,i,1);return s}(this.buffer,e)}dataVariableExists(t){return void 0!==this.header.variables.find((e=>e.name===t))}attributeExists(t){return void 0!==this.globalAttributes.find((e=>e.name===t))}}class GLModel{static sameObj(t,e){return t&&e?JSON.stringify(t)==JSON.stringify(e):t==e}constructor(t,e){this.atoms=[],this.frames=[],this.box=null,this.atomdfs=null,this.id=0,this.hidden=!1,this.molObj=null,this.renderedMolObj=null,this.lastColors=null,this.modelData={},this.modelDatas=null,this.idMatrix=new $.Matrix4,this.dontDuplicateAtoms=!0,this.defaultColor=s.elementColors.defaultColor,this.defaultStickRadius=.25,this.options=e||{},this.ElementColors=this.options.defaultcolors?this.options.defaultcolors:s.elementColors.defaultColors,this.defaultSphereRadius=this.options.defaultSphereRadius?this.options.defaultSphereRadius:1.5,this.defaultCartoonQuality=this.options.cartoonQuality?this.options.cartoonQuality:10,this.id=t}getRadiusFromStyle(t,e){var i=this.defaultSphereRadius;if(void 0!==e.radius)i=e.radius;else if(GLModel.vdwRadii[t.elem])i=GLModel.vdwRadii[t.elem];else if(t.elem.length>1){let e=t.elem;e=e[0].toUpperCase()+e[1].toLowerCase(),GLModel.vdwRadii[e]&&(i=GLModel.vdwRadii[e])}return void 0!==e.scale&&(i*=e.scale),i}drawAtomCross(t,e){if(t.style.cross){var i=t.style.cross;if(!i.hidden){var r=i.linewidth||GLModel.defaultlineWidth;e[r]||(e[r]=new n.Geometry);var s=e[r].updateGeoGroup(6),a=this.getRadiusFromStyle(t,i),o=[[a,0,0],[-a,0,0],[0,a,0],[0,-a,0],[0,0,a],[0,0,-a]],l=t.clickable||t.hoverable;l&&void 0===t.intersectionShape&&(t.intersectionShape={sphere:[],cylinder:[],line:[]});for(var h=(0,L.getColorFromStyle)(t,i),c=s.vertexArray,d=s.colorArray,u=0;u<6;u++){var f=3*s.vertices;if(s.vertices++,c[f]=t.x+o[u][0],c[f+1]=t.y+o[u][1],c[f+2]=t.z+o[u][2],d[f]=h.r,d[f+1]=h.g,d[f+2]=h.b,l){var p=new $.Vector3(o[u][0],o[u][1],o[u][2]);p.multiplyScalar(.1),p.set(p.x+t.x,p.y+t.y,p.z+t.z),t.intersectionShape.line.push(p)}}}}}getGoodCross(t,e,i,r){for(var s=null,n=-1,a=0,o=t.bonds.length;a<o;a++)if(t.bonds[a]!=e.index){let e=t.bonds[a],o=this.atoms[e],h=new $.Vector3(o.x,o.y,o.z).clone();h.sub(i);let c=h.clone();c.cross(r);var l=c.lengthSq();if(l>n&&(s=c,(n=l)>.1))return s}return s}getSideBondV(t,e,i){var r,s,n,a,o=new $.Vector3(t.x,t.y,t.z),l=new $.Vector3(e.x,e.y,e.z).clone(),h=null;if(l.sub(o),1===t.bonds.length)1===e.bonds.length?(h=l.clone(),Math.abs(h.x)>1e-4?h.y+=1:h.x+=1):(r=(i+1)%e.bonds.length,s=e.bonds[r],(n=this.atoms[s]).index==t.index&&(r=(r+1)%e.bonds.length,s=e.bonds[r],n=this.atoms[s]),(a=new $.Vector3(n.x,n.y,n.z).clone()).sub(o),(h=a.clone()).cross(l));else if((h=this.getGoodCross(t,e,o,l)).lengthSq()<.01){var c=this.getGoodCross(e,t,o,l);null!=c&&(h=c)}return h.lengthSq()<.01&&(h=l.clone(),Math.abs(h.x)>1e-4?h.y+=1:h.x+=1),h.cross(l),h.normalize(),h}addLine(t,e,i,r,s,n){t[i]=r.x,t[i+1]=r.y,t[i+2]=r.z,e[i]=n.r,e[i+1]=n.g,e[i+2]=n.b,t[i+3]=s.x,t[i+4]=s.y,t[i+5]=s.z,e[i+3]=n.r,e[i+4]=n.g,e[i+5]=n.b}drawBondLines(t,e,i){if(t.style.line){var r=t.style.line;if(!r.hidden){var a,o,l,h,c=r.linewidth||GLModel.defaultlineWidth;i[c]||(i[c]=new n.Geometry);for(var d=i[c].updateGeoGroup(6*t.bonds.length),u=d.vertexArray,f=d.colorArray,p=0;p<t.bonds.length;p++){var g=e[t.bonds[p]];if(g.style.line&&!(t.index>=g.index)){var m=new $.Vector3(t.x,t.y,t.z),v=new $.Vector3(g.x,g.y,g.z),_=m.clone().add(v).multiplyScalar(.5),y=!1,b=t.clickable||t.hoverable,x=g.clickable||g.hoverable;(b||x)&&(b&&(void 0===t.intersectionShape&&(t.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),t.intersectionShape.line.push(m),t.intersectionShape.line.push(_)),x&&(void 0===g.intersectionShape&&(g.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),g.intersectionShape.line.push(_),g.intersectionShape.line.push(v)));var w=(0,L.getColorFromStyle)(t,t.style.line),A=(0,L.getColorFromStyle)(g,g.style.line);if(t.bondStyles&&t.bondStyles[p]){var C=t.bondStyles[p];if(!C.iswire)continue;C.singleBond&&(y=!0),void 0!==C.color1&&(w=s.CC.color(C.color1)),void 0!==C.color2&&(A=s.CC.color(C.color2))}var S,M,z=3*d.vertices;if(t.bondOrder[p]>1&&t.bondOrder[p]<4&&!y){var T=this.getSideBondV(t,g,p),E=v.clone();E.sub(m),2==t.bondOrder[p]?(T.multiplyScalar(.1),(a=m.clone()).add(T),(o=m.clone()).sub(T),(l=a.clone()).add(E),(h=o.clone()).add(E),w==A?(d.vertices+=4,this.addLine(u,f,z,a,l,w),this.addLine(u,f,z+6,o,h,w)):(d.vertices+=8,E.multiplyScalar(.5),(S=a.clone()).add(E),(M=o.clone()).add(E),this.addLine(u,f,z,a,S,w),this.addLine(u,f,z+6,S,l,A),this.addLine(u,f,z+12,o,M,w),this.addLine(u,f,z+18,M,h,A))):3==t.bondOrder[p]&&(T.multiplyScalar(.1),(a=m.clone()).add(T),(o=m.clone()).sub(T),(l=a.clone()).add(E),(h=o.clone()).add(E),w==A?(d.vertices+=6,this.addLine(u,f,z,m,v,w),this.addLine(u,f,z+6,a,l,w),this.addLine(u,f,z+12,o,h,w)):(d.vertices+=12,E.multiplyScalar(.5),(S=a.clone()).add(E),(M=o.clone()).add(E),this.addLine(u,f,z,m,_,w),this.addLine(u,f,z+6,_,v,A),this.addLine(u,f,z+12,a,S,w),this.addLine(u,f,z+18,S,l,A),this.addLine(u,f,z+24,o,M,w),this.addLine(u,f,z+30,M,h,A)))}else w==A?(d.vertices+=2,this.addLine(u,f,z,m,v,w)):(d.vertices+=4,this.addLine(u,f,z,m,_,w),this.addLine(u,f,z+6,_,v,A))}}}}}drawAtomSphere(t,e){if(t.style.sphere){var i=t.style.sphere;if(!i.hidden){var r=(0,L.getColorFromStyle)(t,i),s=this.getRadiusFromStyle(t,i);if((!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var n=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(n,s))}rt.drawSphere(e,t,s,r)}}}drawAtomClickSphere(t){if(t.style.clicksphere){var e=t.style.clicksphere;if(!e.hidden){var i=this.getRadiusFromStyle(t,e);if((!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var r=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(r,i))}}}}drawAtomInstanced(t,e){if(t.style.sphere){var i=t.style.sphere;if(!i.hidden){var r=this.getRadiusFromStyle(t,i),s=(0,L.getColorFromStyle)(t,i),n=e.updateGeoGroup(1),a=n.vertices,o=3*a,l=n.vertexArray,h=n.colorArray,c=n.radiusArray;if(l[o]=t.x,l[o+1]=t.y,l[o+2]=t.z,h[o]=s.r,h[o+1]=s.g,h[o+2]=s.b,c[a]=r,(!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var d=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(d,r))}n.vertices+=1}}}drawSphereImposter(t,e,i,r){var s,n=t.updateGeoGroup(4),a=n.vertices,o=3*a,l=n.vertexArray,h=n.colorArray;for(s=0;s<4;s++)l[o+3*s]=e.x,l[o+3*s+1]=e.y,l[o+3*s+2]=e.z;var c=n.normalArray;for(s=0;s<4;s++)h[o+3*s]=r.r,h[o+3*s+1]=r.g,h[o+3*s+2]=r.b;c[o+0]=-i,c[o+1]=i,c[o+2]=0,c[o+3]=-i,c[o+4]=-i,c[o+5]=0,c[o+6]=i,c[o+7]=-i,c[o+8]=0,c[o+9]=i,c[o+10]=i,c[o+11]=0,n.vertices+=4;var d=n.faceArray,u=n.faceidx;d[u+0]=a,d[u+1]=a+1,d[u+2]=a+2,d[u+3]=a+2,d[u+4]=a+3,d[u+5]=a,n.faceidx+=6}drawAtomImposter(t,e){if(t.style.sphere){var i=t.style.sphere;if(!i.hidden){var r=this.getRadiusFromStyle(t,i),s=(0,L.getColorFromStyle)(t,i);if((!0===t.clickable||t.hoverable)&&void 0!==t.intersectionShape){var n=new $.Vector3(t.x,t.y,t.z);t.intersectionShape.sphere.push(new J.Sphere(n,r))}this.drawSphereImposter(e,t,r,s)}}}calculateDashes(t,e,i,r,s){var n=Math.sqrt(Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2)+Math.pow(t.z-e.z,2));i=Math.max(i,0),s=Math.max(s,0)+2*i,(r=Math.max(r,.001))+s>n&&(r=n,s=0);var a,o=Math.floor((n-r)/(r+s))+1;s=(n-o*r)/o;for(var l=new $.Vector3(t.x,t.y,t.z),h=new $.Vector3((e.x-t.x)/(n/s),(e.y-t.y)/(n/s),(e.z-t.z)/(n/s)),c=new $.Vector3((e.x-t.x)/(n/r),(e.y-t.y)/(n/r),(e.z-t.z)/(n/r)),d=[],u=0;u<o;u++)a=new $.Vector3(l.x+c.x,l.y+c.y,l.z+c.z),d.push({from:l,to:a}),l=new $.Vector3(a.x+h.x,a.y+h.y,a.z+h.z);return d}static drawStickImposter(t,e,i,r,s,n=0,a=0){for(var o,l=t.updateGeoGroup(4),h=l.vertices,c=3*h,d=l.vertexArray,u=l.colorArray,f=l.radiusArray,p=l.normalArray,g=s.r,m=s.g,v=s.b,_=c,y=0;y<4;y++)d[_]=e.x,p[_]=i.x,u[_]=g,d[++_]=e.y,p[_]=i.y,u[_]=m,d[++_]=e.z,p[_]=i.z,u[_]=y<2?v:(o=void 0,0==(o=-v)&&(o=-1e-4),o),_++;l.vertices+=4,f[h]=-r,f[h+1]=r,f[h+2]=-r,f[h+3]=r;var b=l.faceArray,x=l.faceidx;b[x+0]=h,b[x+1]=h+1,b[x+2]=h+2,b[x+3]=h+2,b[x+4]=h+3,b[x+5]=h,l.faceidx+=6}drawBondSticks(t,e,i){var r,n;if(t.style.stick){var a=t.style.stick;if(!a.hidden){var o,l,h,c,d,u,f,p,g,m,v,_,y,b,x=a.radius||this.defaultStickRadius,w=a.doubleBondScaling||.4,A=a.tripleBondScaling||.25,C=(null===(r=a.dashedBondConfig)||void 0===r?void 0:r.dashLength)||.1,S=(null===(n=a.dashedBondConfig)||void 0===n?void 0:n.gapLength)||.25,M=x,z=a.singleBonds||!1,T=a.dashedBonds||!1,E=0,F=0,I=(0,L.getColorFromStyle)(t,a);!t.capDrawn&&t.bonds.length<4&&(E=2);var O=t=>{var e=i.imposter?GLModel.drawStickImposter:rt.drawCylinder;return!T&&t>=1?e:(t,i,r,s,n,a=0,o=0,l=.1,h=.25)=>{this.calculateDashes(i,r,s,l,h).forEach((i=>{e(t,i.from,i.to,s,n,a,o)}))}};for(h=0;h<t.bonds.length;h++){var D=O(t.bondOrder[h]),k=e[t.bonds[h]];if(_=y=b=null,t.index<k.index){var R=k.style;if(!R.stick||R.stick.hidden)continue;var P=(0,L.getColorFromStyle)(k,R.stick);if(M=x,c=z,t.bondStyles&&t.bondStyles[h]){if((d=t.bondStyles[h]).iswire)continue;d.radius&&(M=d.radius),d.singleBond&&(c=!0),void 0!==d.color1&&(I=s.CC.color(d.color1)),void 0!==d.color2&&(P=s.CC.color(d.color2))}var U=new $.Vector3(t.x,t.y,t.z),B=new $.Vector3(k.x,k.y,k.z);if(t.bondOrder[h]<=1||c||t.bondOrder[h]>3){if(t.bondOrder[h]<1&&(M*=t.bondOrder[h]),!k.capDrawn&&k.bonds.length<4&&(F=2),I!=P?(_=(new $.Vector3).addVectors(U,B).multiplyScalar(.5),D(i,U,_,M,I,E,0,C,S),D(i,_,B,M,P,0,F,C,S)):D(i,U,B,M,I,E,F,C,S),o=t.clickable||t.hoverable,l=k.clickable||k.hoverable,o||l){if(_||(_=(new $.Vector3).addVectors(U,B).multiplyScalar(.5)),o){var N=new J.Cylinder(U,_,M),G=new J.Sphere(U,M);t.intersectionShape.cylinder.push(N),t.intersectionShape.sphere.push(G)}if(l){var V=new J.Cylinder(B,_,M),j=new J.Sphere(B,M);k.intersectionShape.cylinder.push(V),k.intersectionShape.sphere.push(j)}}}else if(t.bondOrder[h]>1){var H=0,W=0;M!=x&&(H=2,W=2);var q,Y,Z,X,K,Q=B.clone(),tt=null;Q.sub(U),tt=this.getSideBondV(t,k,h),2==t.bondOrder[h]?(q=M*w,tt.multiplyScalar(1.5*q),(Y=U.clone()).add(tt),(Z=U.clone()).sub(tt),(X=Y.clone()).add(Q),(K=Z.clone()).add(Q),I!=P?(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5),y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5),D(i,Y,_,q,I,H,0),D(i,_,X,q,P,0,W),D(i,Z,y,q,I,H,0),D(i,y,K,q,P,0,W)):(D(i,Y,X,q,I,H,W),D(i,Z,K,q,I,H,W)),o=t.clickable||t.hoverable,l=k.clickable||k.hoverable,(o||l)&&(_||(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5)),y||(y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5)),o&&(u=new J.Cylinder(Y,_,q),f=new J.Cylinder(Z,y,q),t.intersectionShape.cylinder.push(u),t.intersectionShape.cylinder.push(f)),l&&(g=new J.Cylinder(X,_,q),m=new J.Cylinder(K,y,q),k.intersectionShape.cylinder.push(g),k.intersectionShape.cylinder.push(m)))):3==t.bondOrder[h]&&(q=M*A,tt.cross(Q),tt.normalize(),tt.multiplyScalar(3*q),(Y=U.clone()).add(tt),(Z=U.clone()).sub(tt),(X=Y.clone()).add(Q),(K=Z.clone()).add(Q),I!=P?(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5),y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5),b=(new $.Vector3).addVectors(U,B).multiplyScalar(.5),D(i,Y,_,q,I,H,0),D(i,_,X,q,P,0,W),D(i,U,b,q,I,E,0),D(i,b,B,q,P,0,F),D(i,Z,y,q,I,H,0),D(i,y,K,q,P,0,W)):(D(i,Y,X,q,I,H,W),D(i,U,B,q,I,E,F),D(i,Z,K,q,I,H,W)),o=t.clickable||t.hoverable,l=k.clickable||k.hoverable,(o||l)&&(_||(_=(new $.Vector3).addVectors(Y,X).multiplyScalar(.5)),y||(y=(new $.Vector3).addVectors(Z,K).multiplyScalar(.5)),b||(b=(new $.Vector3).addVectors(U,B).multiplyScalar(.5)),o&&(u=new J.Cylinder(Y.clone(),_.clone(),q),f=new J.Cylinder(Z.clone(),y.clone(),q),p=new J.Cylinder(U.clone(),b.clone(),q),t.intersectionShape.cylinder.push(u),t.intersectionShape.cylinder.push(f),t.intersectionShape.cylinder.push(p)),l&&(g=new J.Cylinder(X.clone(),_.clone(),q),m=new J.Cylinder(K.clone(),y.clone(),q),v=new J.Cylinder(B.clone(),b.clone(),q),k.intersectionShape.cylinder.push(g),k.intersectionShape.cylinder.push(m),k.intersectionShape.cylinder.push(v))))}}}var et=!1,it=0,st=!1;for(h=0;h<t.bonds.length;h++)c=z,t.bondStyles&&t.bondStyles[h]&&((d=t.bondStyles[h]).singleBond&&(c=!0),d.radius&&d.radius!=x&&(st=!0)),(c||1==t.bondOrder[h])&&it++;st?it>0&&(et=!0):0==it&&(t.bonds.length>0||a.showNonBonded)&&(et=!0),et&&(M=x,i.imposter?this.drawSphereImposter(i.sphereGeometry,t,M,I):rt.drawSphere(i,t,M,I))}}}createMolObj(t,e){e=e||{};var i,r,a,o,l=new n.Object3D,h=[],c={},d={},u=this.drawAtomSphere,f=null,p=null;e.supportsImposters?(u=this.drawAtomImposter,(f=new n.Geometry(!0)).imposter=!0,(p=new n.Geometry(!0,!0)).imposter=!0,p.sphereGeometry=new n.Geometry(!0),p.sphereGeometry.imposter=!0,p.drawnCaps={}):e.supportsAIA?(u=this.drawAtomInstanced,(f=new n.Geometry(!1,!0,!0)).instanced=!0,p=new n.Geometry(!0)):(f=new n.Geometry(!0),p=new n.Geometry(!0));var g,m={},v=[Number.POSITIVE_INFINITY,Number.NEGATIVE_INFINITY];for(i=0,a=t.length;i<a;i++){var _=t[i];if(_&&_.style){for(r in(_.clickable||_.hoverable)&&void 0===_.intersectionShape&&(_.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),o={line:void 0,cross:void 0,stick:void 0,sphere:void 0})_.style[r]?_.style[r].opacity?o[r]=parseFloat(_.style[r].opacity):o[r]=1:o[r]=void 0,m[r]?null!=o[r]&&m[r]!=o[r]&&(console.log("Warning: "+r+" opacity is ambiguous"),m[r]=1):m[r]=o[r];u.call(this,_,f),this.drawAtomClickSphere(_),this.drawAtomCross(_,d),this.drawBondLines(_,t,c),this.drawBondSticks(_,t,p),void 0===_.style.cartoon||_.style.cartoon.hidden||("spectrum"!==_.style.cartoon.color||"number"!=typeof _.resi||_.hetflag||(_.resi<v[0]&&(v[0]=_.resi),_.resi>v[1]&&(v[1]=_.resi)),h.push(_))}}if(h.length>0&&xt(l,h,v,this.defaultCartoonQuality),f&&f.vertices>0){f.initTypedArrays();var y=null,b=null;f.imposter?y=new n.SphereImposterMaterial({ambient:0,vertexColors:!0,reflectivity:0}):f.instanced?(b=new n.Geometry(!0),rt.drawSphere(b,{x:0,y:0,z:0},1,new s.Color(.5,.5,.5)),b.initTypedArrays(),y=new n.InstancedMaterial({sphereMaterial:new n.MeshLambertMaterial({ambient:0,vertexColors:!0,reflectivity:0}),sphere:b})):y=new n.MeshLambertMaterial({ambient:0,vertexColors:!0,reflectivity:0}),m.sphere<1&&m.sphere>=0&&(y.transparent=!0,y.opacity=m.sphere),b=new n.Mesh(f,y),l.add(b)}if(p.vertices>0){var x=null,w=null,A=p.sphereGeometry;A&&void 0!==A.vertices&&0!=A.vertices||(A=null),p.initTypedArrays(),A&&A.initTypedArrays();var C={ambient:0,vertexColors:!0,reflectivity:0};p.imposter?(x=new n.StickImposterMaterial(C),w=new n.SphereImposterMaterial(C)):(x=new n.MeshLambertMaterial(C),w=new n.MeshLambertMaterial(C),x.wireframe&&(p.setUpWireframe(),A&&A.setUpWireframe())),m.stick<1&&m.stick>=0&&(x.transparent=!0,x.opacity=m.stick,w.transparent=!0,w.opacity=m.stick);var S=new n.Mesh(p,x);if(l.add(S),A){var M=new n.Mesh(A,w);l.add(M)}}for(i in c)if(c.hasOwnProperty(i)){g=i;var z=new n.LineBasicMaterial({linewidth:g,vertexColors:!0});m.line<1&&m.line>=0&&(z.transparent=!0,z.opacity=m.line),c[i].initTypedArrays();var T=new n.Line(c[i],z,n.LineStyle.LinePieces);l.add(T)}for(i in d)if(d.hasOwnProperty(i)){g=i;var E=new n.LineBasicMaterial({linewidth:g,vertexColors:!0});m.cross<1&&m.cross>=0&&(E.transparent=!0,E.opacity=m.cross),d[i].initTypedArrays();var L=new n.Line(d[i],E,n.LineStyle.LinePieces);l.add(L)}if(this.dontDuplicateAtoms&&this.modelData.symmetries&&this.modelData.symmetries.length>0){var F,I=new n.Object3D;for(F=0;F<this.modelData.symmetries.length;F++){var O=new n.Object3D;(O=l.clone()).matrix.copy(this.modelData.symmetries[F]),O.matrixAutoUpdate=!1,I.add(O)}return I}return l}getInternalState(){return{atoms:this.atoms,frames:this.frames}}setInternalState(t){this.atoms=t.atoms,this.frames=t.frames,this.molObj=null}getCrystData(){if(this.modelData.cryst){if(!this.modelData.cryst.matrix){const t=this.modelData.cryst;this.modelData.cryst.matrix=(0,$.conversionMatrix3)(t.a,t.b,t.c,t.alpha,t.beta,t.gamma)}return this.modelData.cryst}return null}setCrystData(t,e,i,r,s,n){t=t||1,e=e||1,i=i||1,r=r||90,s=s||90,n=n||90;const a=(0,$.conversionMatrix3)(t,e,i,r,s,n);this.modelData.cryst={a:t,b:e,c:i,alpha:r,beta:s,gamma:n,matrix:a}}setCrystMatrix(t){t=t||new $.Matrix3(1,0,0,0,1,0,0,0,1),this.modelData.cryst={matrix:t}}getSymmetries(){return void 0===this.modelData.symmetries&&(this.modelData.symmetries=[this.idMatrix]),this.modelData.symmetries}setSymmetries(t){this.modelData.symmetries=void 0===t?[this.idMatrix]:t}getID(){return this.id}getNumFrames(){return null!=this.frames.numFrames?this.frames.numFrames:this.frames.length}adjustCoord(t,e,i,r){var s=e-t;return s<-i?e+r:s>i?e-r:e}adjustCoordinatesToBox(){if(this.box&&this.atomdfs)for(var t=this.box[0],e=this.box[1],i=this.box[2],r=.9*t,s=.9*e,n=.9*i,a=0;a<this.atomdfs.length;a++)for(var o=this.atomdfs[a],l=1;l<o.length;l++){var h=this.atoms[o[l][0]],c=this.atoms[o[l][1]];h.x=this.adjustCoord(c.x,h.x,r,t),h.y=this.adjustCoord(c.y,h.y,s,e),h.z=this.adjustCoord(c.z,h.z,n,i)}}setFrame(t,e){var i=this.getNumFrames();let r=this;return new Promise((function(s,n){if(0==i&&s(),(t<0||t>=i)&&(t=i-1),null!=r.frames.url){var a=r.frames.url;(0,L.getbin)(a+"/traj/frame/"+t+"/"+r.frames.path,void 0,"POST",void 0).then((function(t){for(var e=new Float32Array(t,44),i=0,n=0;n<r.atoms.length;n++)r.atoms[n].x=e[i++],r.atoms[n].y=e[i++],r.atoms[n].z=e[i++];r.box&&r.atomdfs&&r.adjustCoordinatesToBox(),s()})).catch(n)}else r.atoms=r.frames[t],s();r.molObj=null,r.modelDatas&&t<r.modelDatas.length&&(r.modelData=r.modelDatas[t],r.unitCellObjects&&e&&(e.removeUnitCell(r),e.addUnitCell(r)))}))}addFrame(t){this.frames.push(t)}vibrate(t=10,e=1,i=!1,r,s){var n=0,a=t;i&&(n=-t,a=t),void 0!==this.frames&&void 0!==this.frames.origIndex?this.setFrame(this.frames.origIndex):this.setFrame(0),n<a&&(this.frames=[]),i&&(this.frames.origIndex=t);for(var o=n;o<a;o++){var l=[],h=this.frames.length;if(0!=o||s){for(var c=0;c<this.atoms.length;c++){var d=(0,L.getAtomProperty)(this.atoms[c],"dx"),u=(0,L.getAtomProperty)(this.atoms[c],"dy"),f=(0,L.getAtomProperty)(this.atoms[c],"dz"),p=new $.Vector3(d,u,f),g=new $.Vector3(this.atoms[c].x,this.atoms[c].y,this.atoms[c].z),m=o*e/t;p.multiplyScalar(m),g.add(p);var v={};for(var _ in this.atoms[c])v[_]=this.atoms[c][_];if(v.x=g.x,v.y=g.y,v.z=g.z,l.push(v),r&&s){var y=(0,L.extend)({},s),b=new $.Vector3(d,u,f);if(b.multiplyScalar(e),b.add(g),y.start=g,y.end=b,y.frame=h,!y.color){var x=v.style.sphere;x||(x=v.style.stick),x||(x=v.style.line),y.color=(0,L.getColorFromStyle)(v,x)}r.addArrow(y)}}this.frames.push(l)}else this.frames.push(this.atoms)}}setAtomDefaults(t){for(let e=0;e<t.length;e++){let i=t[e];i&&(i.style=i.style||(0,L.deepCopy)(GLModel.defaultAtomStyle),i.color=i.color||this.ElementColors[i.elem]||this.defaultColor,i.model=this.id,(i.clickable||i.hoverable)&&(i.intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}))}}addMolData(t,e,i={}){var r=GLModel.parseMolData(t,e,i);this.dontDuplicateAtoms=!i.duplicateAssemblyAtoms;var s=r.modelData;if(s&&(Array.isArray(s)?(this.modelData=s[0],i.frames&&(this.modelDatas=s)):this.modelData=s),r.box?this.box=r.box:this.box=null,0==this.frames.length){for(let t=0;t<r.length;t++)0!=r[t].length&&this.frames.push(r[t]);this.frames[0]&&(this.atoms=this.frames[0])}else if(i.frames)for(let t=0;t<r.length;t++)this.frames.push(r[t]);else for(var n=0;n<r.length;n++)this.addAtoms(r[n]);for(let t=0;t<this.frames.length;t++)this.setAtomDefaults(this.frames[t]);i.vibrate&&i.vibrate.frames&&i.vibrate.amplitude&&this.vibrate(i.vibrate.frames,i.vibrate.amplitude),i.style&&this.setStyle({},i.style)}setDontDuplicateAtoms(t){this.dontDuplicateAtoms=t}setModelData(t){this.modelData=t}propertyMatches(t,e){if(t==e)return!0;if("string"==typeof e&&"number"==typeof t){var i=e.match(/(-?\d+)\s*-\s*(-?\d+)/);if(i){var r=parseInt(i[1]),s=parseInt(i[2]);if(i&&t>=r&&t<=s)return!0}}return!1}static deepCopyAndCache(t,e){if("object"!=typeof t||null==t)return t;if(t.__cache_created)return t;const i={};for(const r in t){const s=t[r];if(Array.isArray(s)){i[r]=[];for(let t=0;t<s.length;t++)i[r].push(GLModel.deepCopyAndCache(s[t],e))}else i[r]="object"==typeof s&&"properties"!=r&&"model"!=r?GLModel.deepCopyAndCache(s,e):s;if("and"==r||"or"==r){const t=[];for(const s of i[r]){const i=new Set;for(const t of e.selectedAtoms(s))i.add(t.index);t.push(i)}if("and"==r){const e=function(t,e){const i=new Set;for(const r of e)t.has(r)&&i.add(r);return i};let s=new Set(t[0]);for(const i of t.splice(1))s=e(s,i);i[r].__cached_results=s}else if("or"==r){const e=new Set;for(const i of t)for(const t of i)e.add(t);i[r].__cached_results=e}}}return i.__cache_created=!0,i}atomIsSelected(t,e){if(void 0===e)return!0;var i=!!e.invert,r=!0;for(var s in e)if("and"==s||"or"==s||"not"==s){if("not"==s){if(this.atomIsSelected(t,e[s])){r=!1;break}}else if(void 0===e[s].__cached_results&&(e=GLModel.deepCopyAndCache(e,this)),!(r=e[s].__cached_results.has(t.index)))break}else if("predicate"===s){if(!e.predicate(t)){r=!1;break}}else if("properties"==s&&t[s]){for(var n in e.properties)if(!n.startsWith("__cache")){if(void 0===t.properties[n]){r=!1;break}if(t.properties[n]!=e.properties[n]){r=!1;break}}}else if(e.hasOwnProperty(s)&&!GLModel.ignoredKeys.has(s)&&!s.startsWith("__cache")){if(void 0===t[s]){r=!1;break}var a=!1;if("bonds"===s){if(e[s]!=t.bonds.length){r=!1;break}}else if(Array.isArray(e[s])){var o=e[s],l=t[s];for(let t=0;t<o.length;t++)if(this.propertyMatches(l,o[t])){a=!0;break}if(!a){r=!1;break}}else{let i=e[s];if(!this.propertyMatches(t[s],i)){r=!1;break}}}return i?!r:r}static squaredDistance(t,e){var i=e.x-t.x,r=e.y-t.y,s=e.z-t.z;return i*i+r*r+s*s}expandAtomList(t,e){if(e<=0)return t;for(var i=(0,L.getExtent)(t,void 0),r=[[],[],[]],s=0;s<3;s++)r[0][s]=i[0][s]-e,r[1][s]=i[1][s]+e,r[2][s]=i[2][s];var n=[];for(let t=0;t<this.atoms.length;t++){var a=this.atoms[t].x,o=this.atoms[t].y,l=this.atoms[t].z;a>=r[0][0]&&a<=r[1][0]&&o>=r[0][1]&&o<=r[1][1]&&l>=r[0][2]&&l<=r[1][2]&&(a>=i[0][0]&&a<=i[1][0]&&o>=i[0][1]&&o<=i[1][1]&&l>=i[0][2]&&l<=i[1][2]||n.push(this.atoms[t]))}return n}static getFloat(t){return"number"==typeof t?t:parseFloat(t)}selectedAtoms(t,e){var i=[];t=GLModel.deepCopyAndCache(t||{},this),e||(e=this.atoms);for(var r=e.length,s=0;s<r;s++){var n=e[s];n&&this.atomIsSelected(n,t)&&i.push(n)}if(t.hasOwnProperty("expand")){const e=GLModel.getFloat(t.expand);let r=this.expandAtomList(i,e),s=i.length;const n=e*e;for(let t=0;t<r.length;t++)for(let e=0;e<s;e++){var a=GLModel.squaredDistance(r[t],i[e]);a<n&&a>0&&i.push(r[t])}}if(t.hasOwnProperty("within")&&t.within.hasOwnProperty("sel")&&t.within.hasOwnProperty("distance")){var o=this.selectedAtoms(t.within.sel,this.atoms),l={};const e=GLModel.getFloat(t.within.distance),r=e*e;for(let t=0;t<o.length;t++)for(let e=0;e<i.length;e++){let s=GLModel.squaredDistance(o[t],i[e]);s<r&&s>0&&(l[e]=1)}var h=[];if(t.within.invert)for(let t=0;t<i.length;t++)l[t]||h.push(i[t]);else for(let t in l)h.push(i[t]);i=h}if(t.hasOwnProperty("byres")){var c={},d=[],u=[];for(let t=0;t<i.length;t++){let e=i[t];var f=e.chain,p=e.resi;if(void 0===c[f]&&(c[f]={}),e.hasOwnProperty("resi")&&void 0===c[f][p])for(c[f][p]=!0,u.push(e);u.length>0;)if(e=u.pop(),f=e.chain,p=e.resi,void 0===d[e.index]){d[e.index]=!0;for(var g=0;g<e.bonds.length;g++){var m=this.atoms[e.bonds[g]];void 0===d[m.index]&&m.hasOwnProperty("resi")&&m.chain==f&&m.resi==p&&(u.push(m),i.push(m))}}}}return i}addAtoms(t){this.molObj=null;var e,i=this.atoms.length,r=[];for(e=0;e<t.length;e++)void 0===t[e].index&&(t[e].index=e),void 0===t[e].serial&&(t[e].serial=e),r[t[e].index]=i+e;for(e=0;e<t.length;e++){var s=t[e],n=r[s.index],a=(0,L.extend)({},s);a.index=n,a.bonds=[],a.bondOrder=[],a.model=this.id,a.style=a.style||(0,L.deepCopy)(GLModel.defaultAtomStyle),void 0===a.color&&(a.color=this.ElementColors[a.elem]||this.defaultColor);for(var o=s.bonds?s.bonds.length:0,l=0;l<o;l++){var h=r[s.bonds[l]];void 0!==h&&(a.bonds.push(h),a.bondOrder.push(s.bondOrder?s.bondOrder[l]:1))}this.atoms.push(a)}}assignBonds(){(0,u.assignBonds)(this.atoms,{assignBonds:!0})}removeAtoms(t){this.molObj=null;var e,i=[];for(e=0;e<t.length;e++)i[t[e].index]=!0;var r=[];for(e=0;e<this.atoms.length;e++){var s=this.atoms[e];i[s.index]||r.push(s)}this.atoms=[],this.addAtoms(r)}setStyle(t,e,i){void 0===e&&void 0===i&&(e=t,t={}),"string"==typeof e&&(e=(0,L.specStringToObject)(e));var r=!1,s=this,n=function(n){var a=s.selectedAtoms(t,n);for(let t=0;t<n.length;t++)n[t]&&(n[t].capDrawn=!1);for(let t=0;t<a.length;t++){r=!0,(a[t].clickable||a[t].hoverable)&&(a[t].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]}),i||(a[t].style={});for(let i in e)e.hasOwnProperty(i)&&(a[t].style[i]=a[t].style[i]||{},Object.assign(a[t].style[i],e[i]))}};if(void 0!==t.frame&&t.frame<this.frames.length){let e=t.frame;e<0&&(e=this.frames.length+e),n(this.frames[e])}else{n(this.atoms);for(var a=0;a<this.frames.length;a++)this.frames[a]!==this.atoms&&n(this.frames[a])}r&&(this.molObj=null)}setClickable(t,e,i){if(e=!!e,null!==(i=(0,L.makeFunction)(i))){var r=this.selectedAtoms(t,this.atoms),s=r.length;for(let t=0;t<s;t++)r[t].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},r[t].clickable=e,i&&(r[t].callback=i);s>0&&(this.molObj=null)}else console.log("Callback is not a function")}setHoverable(t,e,i,r){if(e=!!e,i=(0,L.makeFunction)(i),r=(0,L.makeFunction)(r),null!==i)if(null!==r){var s=this.selectedAtoms(t,this.atoms),n=s.length;for(let t=0;t<n;t++)s[t].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},s[t].hoverable=e,i&&(s[t].hover_callback=i),r&&(s[t].unhover_callback=r);n>0&&(this.molObj=null)}else console.log("Unhover_callback is not a function");else console.log("Hover_callback is not a function")}enableContextMenu(t,e){var i;e=!!e;var r=this.selectedAtoms(t,this.atoms),s=r.length;for(i=0;i<s;i++)r[i].intersectionShape={sphere:[],cylinder:[],line:[],triangle:[]},r[i].contextMenuEnabled=e;s>0&&(this.molObj=null)}setColorByElement(t,e){if(null===this.molObj||!GLModel.sameObj(e,this.lastColors)){this.lastColors=e;var i=this.selectedAtoms(t,i);i.length>0&&(this.molObj=null);for(var r=0;r<i.length;r++){var s=i[r];void 0!==e[s.elem]&&(s.color=e[s.elem])}}}setColorByProperty(t,e,i,s){var n,a,o=this.selectedAtoms(t,o);for(this.lastColors=null,o.length>0&&(this.molObj=null),"string"==typeof i&&void 0!==r.Gradient.builtinGradients[i]&&(i=new r.Gradient.builtinGradients[i]),s||(s=i.range()),s||(s=(0,L.getPropertyRange)(o,e)),n=0;n<o.length;n++){a=o[n],null!=(0,L.getAtomProperty)(a,e)&&(a.color=i.valueToHex(parseFloat(a.properties[e]),s))}}setColorByFunction(t,e){var i=this.selectedAtoms(t,i);if("function"==typeof e){this.lastColors=null,i.length>0&&(this.molObj=null);for(let t=0;t<i.length;t++){let r=i[t];r.color=e(r)}}}toCDObject(t=!1){var e={a:[],b:[]};t&&(e.s=[]);for(let r=0;r<this.atoms.length;r++){let s={},n=this.atoms[r];if(s.x=n.x,s.y=n.y,s.z=n.z,"C"!=n.elem&&(s.l=n.elem),t){for(var i=0;i<e.s.length&&JSON.stringify(n.style)!==JSON.stringify(e.s[i]);)i++;i===e.s.length&&e.s.push(n.style),0!==i&&(s.s=i)}e.a.push(s);for(let t=0;t<n.bonds.length;t++){let i=r,s=n.bonds[t];if(i>=s)continue;let a={b:i,e:s},o=n.bondOrder[t];1!=o&&(a.o=o),e.b.push(a)}}return e}globj(t,e){(null===this.molObj||e.regen)&&(this.molObj=this.createMolObj(this.atoms,e),this.renderedMolObj&&(t.remove(this.renderedMolObj),this.renderedMolObj=null),this.renderedMolObj=this.molObj.clone(),this.hidden&&(this.renderedMolObj.setVisible(!1),this.molObj.setVisible(!1)),t.add(this.renderedMolObj))}exportVRML(){return this.createMolObj(this.atoms,{supportsImposters:!1,supportsAIA:!1}).vrml()}removegl(t){this.renderedMolObj&&(void 0!==this.renderedMolObj.geometry&&this.renderedMolObj.geometry.dispose(),void 0!==this.renderedMolObj.material&&this.renderedMolObj.material.dispose(),t.remove(this.renderedMolObj),this.renderedMolObj=null),this.molObj=null}hide(){this.hidden=!0,this.renderedMolObj&&this.renderedMolObj.setVisible(!1),this.molObj&&this.molObj.setVisible(!1)}show(){this.hidden=!1,this.renderedMolObj&&this.renderedMolObj.setVisible(!0),this.molObj&&this.molObj.setVisible(!0)}addPropertyLabels(t,e,i,r){for(var s=this.selectedAtoms(e,s),n=(0,L.deepCopy)(r),a=0;a<s.length;a++){var o=s[a],l=null;void 0!==o[t]?l=String(o[t]):void 0!==o.properties[t]&&(l=String(o.properties[t])),null!=l&&(n.position=o,i.addLabel(l,n))}}addResLabels(t,e,i,r=!1){var s=[],n=function(r,n){for(var a=r.selectedAtoms(t,a),o={},l=0;l<a.length;l++){var h=a[l],c=h.chain,d=h.resn+""+h.resi;o[c]||(o[c]={}),o[c][d]||(o[c][d]=[]),o[c][d].push(h)}var u=(0,L.deepCopy)(i);for(let t in o)if(o.hasOwnProperty(t)){var f=o[t];for(let t in f)if(f.hasOwnProperty(t)){let i=f[t],r=new $.Vector3(0,0,0);for(let t=0;t<i.length;t++){let e=i[t];r.x+=e.x,r.y+=e.y,r.z+=e.z}r.divideScalar(i.length),u.position=r,u.frame=n;let a=e.addLabel(t,u,void 0,!0);s.push(a)}}};if(r){var a=this.getNumFrames();let t=this.atoms;for(let t=0;t<a;t++)this.frames[t]&&(this.atoms=this.frames[t],n(this,t));this.atoms=t}else n(this);return s}setupDFS(){this.atomdfs=[];var t=this,e=new Int8Array(this.atoms.length);e.fill(0);for(var i=function(r,s,n){n.push([r,s]);var a=t.atoms[r];e[r]=1;for(var o=0;o<a.bonds.length;o++){var l=a.bonds[o];t.atoms[l]&&!e[l]&&i(l,r,n)}},r=0;r<this.atoms.length;r++){if(this.atoms[r]&&!e[r]){var s=[];i(r,-1,s),this.atomdfs.push(s)}}}setCoordinatesFromURL(t,e){this.frames=[];var i=this;return this.box&&this.setupDFS(),t.startsWith("http")||(t="http://"+t),(0,L.get)(t+"/traj/numframes/"+e,(function(r){if(!isNaN(parseInt(r)))return i.frames.push(i.atoms),i.frames.numFrames=r,i.frames.url=t,i.frames.path=e,i.setFrame(0)}))}setCoordinates(t,e){if(!t)return[];if(/\.gz$/.test(e=e||"")){e=e.replace(/\.gz$/,"");try{t=(0,L.inflateString)(t)}catch(t){console.log(t)}}if({mdcrd:"",inpcrd:"",pdb:"",netcdf:"",array:""}.hasOwnProperty(e)){this.frames=[];for(var i=this.atoms.length,r=GLModel.parseCrd(t,e),s=0;s<r.length;){for(var n=[],a=0;a<i;a++){var o={};for(var l in this.atoms[a])o[l]=this.atoms[a][l];n[a]=o,n[a].x=r[s++],n[a].y=r[s++],n[a].z=r[s++]}this.frames.push(n)}return this.atoms=this.frames[0],this.frames}return[]}addAtomSpecs(t){}static parseCrd(t,e){var i=[],r=0;if("pdb"==e)for(var s=t.indexOf("\nATOM");-1!=s;){for(;"\nATOM"==t.slice(s,s+5)||"\nHETATM"==t.slice(s,s+7);)i[r++]=parseFloat(t.slice(s+31,s+39)),i[r++]=parseFloat(t.slice(s+39,s+47)),i[r++]=parseFloat(t.slice(s+47,s+55)),s=t.indexOf("\n",s+54),"\nTER"==t.slice(s,s+4)&&(s=t.indexOf("\n",s+5));s=t.indexOf("\nATOM",s)}else if("netcdf"==e){var n=new NetCDFReader(t);i=[].concat.apply([],n.getDataVariable("coordinates"))}else{if("array"==e||Array.isArray(t))return t.flat(2);{let r=t.indexOf("\n");"inpcrd"==e&&(r=t.indexOf("\n",r+1)),i=(t=t.slice(r+1)).match(/\S+/g).map(parseFloat)}}return i}static parseMolData(t,e="",i){if(!t)return[];if(/\.gz$/.test(e)){e=e.replace(/\.gz$/,"");try{t=e.match(/bcif/i)?(0,L.inflateString)(t,!1):(0,L.inflateString)(t)}catch(t){console.log(t)}}return void 0===K[e]&&(e=e.split(".").pop(),void 0===K[e]&&(console.log("Unknown format: "+e),e=t instanceof Uint8Array?"bcif":t.match(/^@<TRIPOS>MOLECULE/gm)?"mol2":t.match(/^data_/gm)&&t.match(/^loop_/gm)?"cif":t.match(/^HETATM/gm)||t.match(/^ATOM/gm)?"pdb":t.match(/ITEM: TIMESTEP/gm)?"lammpstrj":t.match(/^.*\n.*\n.\s*(\d+)\s+(\d+)/gm)?"sdf":t.match(/^%VERSION\s+VERSION_STAMP/gm)?"prmtop":"xyz",console.log("Best guess: "+e))),(0,K[e])(t,i)}}GLModel.defaultAtomStyle={line:{}},GLModel.defaultlineWidth=1,GLModel.vdwRadii={H:1.2,He:1.4,Li:1.82,Be:1.53,B:1.92,C:1.7,N:1.55,O:1.52,F:1.47,Ne:1.54,Na:2.27,Mg:1.73,Al:1.84,Si:2.1,P:1.8,S:1.8,Cl:1.75,Ar:1.88,K:2.75,Ca:2.31,Ni:1.63,Cu:1.4,Zn:1.39,Ga:1.87,Ge:2.11,As:1.85,Se:1.9,Br:1.85,Kr:2.02,Rb:3.03,Sr:2.49,Pd:1.63,Ag:1.72,Cd:1.58,In:1.93,Sn:2.17,Sb:2.06,Te:2.06,I:1.98,Xe:2.16,Cs:3.43,Ba:2.68,Pt:1.75,Au:1.66,Hg:1.55,Tl:1.96,Pb:2.02,Bi:2.07,Po:1.97,At:2.02,Rn:2.2,Fr:3.48,Ra:2.83,U:1.86},GLModel.ignoredKeys=new Set(["props","invert","model","frame","byres","expand","within","and","or","not"]);var Ht=i(111);const Wt=16;class GLViewer{getWidth(){let t=this.container,e=t.offsetWidth;if(0==e&&"none"===t.style.display){let i=t.style.position,r=t.style.visibility;t.style.display="block",t.style.visibility="hidden",t.style.position="absolute",e=t.offsetWidth,t.style.display="none",t.style.visibility=r,t.style.position=i}return e}getHeight(){let t=this.container,e=t.offsetHeight;if(0==e&&"none"===t.style.display){let i=t.style.position,r=t.style.visibility;t.style.display="block",t.style.visibility="hidden",t.style.position="absolute",e=t.offsetHeight,t.style.display="none",t.style.visibility=r,t.style.position=i}return e}setupRenderer(){this.renderer=new n.Renderer({antialias:this.config.antialias,preserveDrawingBuffer:!0,premultipliedAlpha:!1,id:this.config.id,row:this.config.row,col:this.config.col,rows:this.config.rows,cols:this.config.cols,canvas:this.config.canvas,containerWidth:this.WIDTH,containerHeight:this.HEIGHT,ambientOcclusion:this.config.ambientOcclusion,outline:this.config.outline}),this.renderer.domElement.style.width="100%",this.renderer.domElement.style.height="100%",this.renderer.domElement.style.padding="0",this.renderer.domElement.style.position="absolute",this.renderer.domElement.style.top="0px",this.renderer.domElement.style.left="0px",this.renderer.domElement.style.zIndex="0"}initializeScene(){this.scene=new n.Scene,this.scene.fog=new n.Fog(this.bgColor,100,200),this.modelGroup=new n.Object3D,this.rotationGroup=new n.Object3D,this.rotationGroup.useQuaternion=!0,this.rotationGroup.quaternion=new $.Quaternion(0,0,0,1),this.rotationGroup.add(this.modelGroup),this.scene.add(this.rotationGroup);var t=new n.Light(16777215);t.position=new $.Vector3(.2,.2,1).normalize(),t.intensity=1,this.scene.add(t)}_handleLostContext(t){const e=function(t){const e=t.getBoundingClientRect();return!(e.right<0||e.bottom<0||e.top>(window.innerHeight||document.documentElement.clientHeight)||e.left>(window.innerWidth||document.documentElement.clientWidth))};if(e(this.container)){let t=0;for(let i of document.getElementsByTagName("canvas"))if(e(i)&&null!=i._3dmol_viewer&&(i._3dmol_viewer.resize(),t+=1,t>=Wt))break}}initContainer(t){this.container=t,this.WIDTH=this.getWidth(),this.HEIGHT=this.getHeight(),this.ASPECT=this.renderer.getAspect(this.WIDTH,this.HEIGHT),this.renderer.setSize(this.WIDTH,this.HEIGHT),this.container.append(this.renderer.domElement),this.glDOM=this.renderer.domElement,this.glDOM._3dmol_viewer=this,this.glDOM.addEventListener("webglcontextlost",this._handleLostContext.bind(this)),this.nomouse||(this.glDOM.addEventListener("mousedown",this._handleMouseDown.bind(this),{passive:!1}),this.glDOM.addEventListener("touchstart",this._handleMouseDown.bind(this),{passive:!1}),this.glDOM.addEventListener("wheel",this._handleMouseScroll.bind(this),{passive:!1}),this.glDOM.addEventListener("mousemove",this._handleMouseMove.bind(this),{passive:!1}),this.glDOM.addEventListener("touchmove",this._handleMouseMove.bind(this),{passive:!1}),this.glDOM.addEventListener("contextmenu",this._handleContextMenu.bind(this),{passive:!1}))}decAnim(){this.animated--,this.animated<0&&(this.animated=0)}incAnim(){this.animated++}nextSurfID(){var t=0;for(let i in this.surfaces)if(this.surfaces.hasOwnProperty(i)){var e=parseInt(i);isNaN(e)||e>t&&(t=e)}return t+1}setSlabAndFog(){let t=this.camera.position.z-this.rotationGroup.position.z;t<1&&(t=1),this.camera.near=t+this.slabNear,!this.camera.ortho&&this.camera.near<1&&(this.camera.near=1),this.camera.far=t+this.slabFar,this.camera.near+1>this.camera.far&&(this.camera.far=this.camera.near+1),this.camera.fov=this.fov,this.camera.right=t*Math.tan(Math.PI/180*this.fov),this.camera.left=-this.camera.right,this.camera.top=this.camera.right/this.ASPECT,this.camera.bottom=-this.camera.top,this.camera.updateProjectionMatrix(),this.scene.fog.near=this.camera.near+this.fogStart*(this.camera.far-this.camera.near),this.scene.fog.far=this.camera.far,this.config.disableFog&&(this.scene.fog.near=this.scene.fog.far)}show(t){if(this.renderer.setViewport(),this.scene&&(this.setSlabAndFog(),this.renderer.render(this.scene,this.camera),this.viewChangeCallback&&this.viewChangeCallback(this._viewer.getView()),!t&&this.linkedViewers.length>0))for(var e=this._viewer.getView(),i=0;i<this.linkedViewers.length;i++){this.linkedViewers[i].setView(e,!0)}}updateClickables(){this.clickables.splice(0,this.clickables.length),this.hoverables.splice(0,this.hoverables.length),this.contextMenuEnabledObjects.splice(0,this.contextMenuEnabledObjects.length);for(let t=0,e=this.models.length;t<e;t++){let e=this.models[t];if(e){let t=e.selectedAtoms({clickable:!0}),i=e.selectedAtoms({hoverable:!0}),r=e.selectedAtoms({contextMenuEnabled:!0});for(let t=0;t<i.length;t++)this.hoverables.push(i[t]);for(let e=0;e<t.length;e++)this.clickables.push(t[e]);for(let t=0;t<r.length;t++)this.contextMenuEnabledObjects.push(r[t])}}for(let t=0,e=this.shapes.length;t<e;t++){let e=this.shapes[t];e&&e.clickable&&this.clickables.push(e),e&&e.hoverable&&this.hoverables.push(e),e&&e.contextMenuEnabled&&this.contextMenuEnabledObjects.push(e)}}handleClickSelection(t,e,i){let r=this.targetedObjects(t,e,this.clickables);if(r.length){var s=r[0].clickable;if(void 0!==s.callback&&("function"!=typeof s.callback&&(s.callback=(0,L.makeFunction)(s.callback)),"function"==typeof s.callback)){3===this.mouseButton&&this.contextMenuEnabledObjects.includes(s)&&this.userContextMenuHandler||s.callback(s,this._viewer,i,this.container,r)}}}canvasOffset(){let t=this.glDOM,e=t.getBoundingClientRect(),i=t.ownerDocument,r=i.documentElement,s=i.defaultView;return{top:e.top+s.pageYOffset-r.clientTop,left:e.left+s.pageXOffset-r.clientLeft}}setHover(t,e,i){this.current_hover!=t&&(this.current_hover&&("function"!=typeof this.current_hover.unhover_callback&&(this.current_hover.unhover_callback=(0,L.makeFunction)(this.current_hover.unhover_callback)),this.current_hover.unhover_callback(this.current_hover,this._viewer,e,this.container,i)),this.current_hover=t,t&&void 0!==t.hover_callback&&("function"!=typeof t.hover_callback&&(t.hover_callback=(0,L.makeFunction)(t.hover_callback)),"function"==typeof t.hover_callback&&t.hover_callback(t,this._viewer,e,this.container,i)))}handleHoverSelection(t,e,i){if(0==this.hoverables.length)return;let r=this.targetedObjects(t,e,this.hoverables);if(r.length){var s=r[0].clickable;this.setHover(s,i,r),this.current_hover=s}else this.setHover(null)}handleHoverContinue(t,e){let i=this.targetedObjects(t,e,this.hoverables);0!=i.length&&void 0!==i[0]||this.setHover(null),void 0!==i[0]&&i[0].clickable!==this.current_hover&&this.setHover(null)}closeEnoughForClick(t,{allowTolerance:e=t.targetTouches,tolerance:i=5}={}){const r=this.getX(t),s=this.getY(t);if(e){const t=Math.abs(r-this.mouseStartX),e=Math.abs(s-this.mouseStartY);return t<=i&&e<=i}return r===this.mouseStartX&&s===this.mouseStartY}calcTouchDistance(t){var e=t.targetTouches[0].pageX-t.targetTouches[1].pageX,i=t.targetTouches[0].pageY-t.targetTouches[1].pageY;return Math.hypot(e,i)}getX(t){var e=t.pageX;return null==e&&(e=t.pageX),t.targetTouches&&t.targetTouches[0]?e=t.targetTouches[0].pageX:t.changedTouches&&t.changedTouches[0]&&(e=t.changedTouches[0].pageX),e}getY(t){var e=t.pageY;return null==e&&(e=t.pageY),t.targetTouches&&t.targetTouches[0]?e=t.targetTouches[0].pageY:t.changedTouches&&t.changedTouches[0]&&(e=t.changedTouches[0].pageY),e}isInViewer(t,e){if(null!=this.viewers){var i=this.WIDTH/this.cols,r=this.HEIGHT/this.rows,s=this.canvasOffset(),n=t-s.left,a=e-s.top,o=this.rows-Math.floor(a/r)-1,l=Math.floor(n/i);if(o!=this.row||l!=this.col)return!1}return!0}adjustZoomToLimits(t){if(this.config.lowerZoomLimit&&this.config.lowerZoomLimit>0){let e=this.CAMERA_Z-this.config.lowerZoomLimit;t>e&&(t=e)}if(this.config.upperZoomLimit&&this.config.upperZoomLimit>0){let e=this.CAMERA_Z-this.config.upperZoomLimit;t<e&&(t=e)}return t>this.CAMERA_Z-1&&(t=this.CAMERA_Z-1),t}static slerp(t,e,i){if(1==i)return e.clone();if(0==i)return t.clone();let r=t.x*e.x+t.y*e.y+t.z*e.z+t.w*e.w;if(r>.9995){let r=new $.Quaternion(t.x+i*(e.x-t.x),t.y+i*(e.y-t.y),t.z+i*(e.z-t.z),t.w+i*(e.w-t.w));return r.normalize(),r}r<0&&(e=e.clone().multiplyScalar(-1),r=-r),r>1?r=1:r<-1&&(r=-1);var s=Math.acos(r)*i,n=e.clone();n.sub(t.clone().multiplyScalar(r)),n.normalize();var a=Math.cos(s),o=Math.sin(s),l=new $.Quaternion(t.x*a+n.x*o,t.y*a+n.y*o,t.z*a+n.z*o,t.w*a+n.w*o);return l.normalize(),l}constructor(t,e={}){if(this.nomouse=!1,this.glDOM=null,this.models=[],this.surfaces={},this.shapes=[],this.labels=[],this.clickables=[],this.hoverables=[],this.contextMenuEnabledObjects=[],this.current_hover=null,this.hoverDuration=500,this.longTouchDuration=1e3,this.viewer_frame=0,this.viewChangeCallback=null,this.stateChangeCallback=null,this.NEAR=1,this.FAR=800,this.CAMERA_Z=150,this.fov=20,this.linkedViewers=[],this.renderer=null,this.control_all=!1,this.scene=null,this.rotationGroup=null,this.modelGroup=null,this.fogStart=.4,this.slabNear=-50,this.slabFar=50,this.cq=new $.Quaternion(0,0,0,1),this.dq=new $.Quaternion(0,0,0,1),this.animated=0,this.animationTimers=new Set,this.isDragging=!1,this.mouseStartX=0,this.mouseStartY=0,this.touchDistanceStart=0,this.touchHold=!1,this.currentModelPos=0,this.cz=0,this.cslabNear=0,this.cslabFar=0,this.userContextMenuHandler=null,this.config=e,this.callback=this.config.callback,this.defaultcolors=this.config.defaultcolors,this.defaultcolors||(this.defaultcolors=s.elementColors.defaultColors),this.nomouse=Boolean(this.config.nomouse),this.bgColor=0,this.config.backgroundColor=this.config.backgroundColor||"#ffffff",void 0!==this.config.backgroundColor&&(this.bgColor=s.CC.color(this.config.backgroundColor).getHex()),this.config.backgroundAlpha=null==this.config.backgroundAlpha?1:this.config.backgroundAlpha,this.camerax=0,void 0!==this.config.camerax&&(this.camerax="string"==typeof this.config.camerax?parseFloat(this.config.camerax):this.config.camerax),this._viewer=this,this.container=t,null!=this.config.hoverDuration&&(this.hoverDuration=this.config.hoverDuration),void 0===this.config.antialias&&(this.config.antialias=!0),void 0===this.config.cartoonQuality&&(this.config.cartoonQuality=10),this.WIDTH=this.getWidth(),this.HEIGHT=this.getHeight(),this.setupRenderer(),this.row=null==this.config.row?0:this.config.row,this.col=null==this.config.col?0:this.config.col,this.cols=this.config.cols,this.rows=this.config.rows,this.viewers=this.config.viewers,this.control_all=this.config.control_all,this.ASPECT=this.renderer.getAspect(this.WIDTH,this.HEIGHT),this.camera=new n.Camera(this.fov,this.ASPECT,this.NEAR,this.FAR,this.config.orthographic),this.camera.position=new $.Vector3(this.camerax,0,this.CAMERA_Z),this.lookingAt=new $.Vector3,this.camera.lookAt(this.lookingAt),this.raycaster=new n.Raycaster(new $.Vector3(0,0,0),new $.Vector3(0,0,0)),this.projector=new n.Projector,this.initializeScene(),this.renderer.setClearColorHex(this.bgColor,this.config.backgroundAlpha),this.scene.fog.color=s.CC.color(this.bgColor),document.body.addEventListener("mouseup",this._handleMouseUp.bind(this)),document.body.addEventListener("touchend",this._handleMouseUp.bind(this)),this.initContainer(this.container),this.config.style&&this.setViewStyle(this.config),window.addEventListener("resize",this.resize.bind(this)),void 0!==window.ResizeObserver&&(this.divwatcher=new window.ResizeObserver(this.resize.bind(this)),this.divwatcher.observe(this.container)),void 0!==window.IntersectionObserver){let t=(t,e)=>{t.forEach((t=>{t.isIntersecting&&this.resize()}))};this.intwatcher=new window.IntersectionObserver(t),this.intwatcher.observe(this.container)}try{"function"==typeof this.callback&&this.callback(this)}catch(t){console.log("error with glviewer callback: "+t)}}targetedObjects(t,e,i){var r={x:t,y:e,z:-1};return Array.isArray(i)||(i=this.selectedAtoms(i)),0==i.length?[]:(this.raycaster.setFromCamera(r,this.camera),this.raycaster.intersectObjects(this.modelGroup,i))}modelToScreen(t){let e=!1;Array.isArray(t)||(t=[t],e=!0);let i=this.renderer.getXRatio(),r=this.renderer.getYRatio(),s=this.col,n=this.row,a=s*(this.WIDTH/i),o=(r-n-1)*(this.HEIGHT/r),l=[],h=this.canvasOffset();return t.forEach((t=>{let e=new $.Vector3(t.x,t.y,t.z);e.applyMatrix4(this.modelGroup.matrixWorld),this.projector.projectVector(e,this.camera);let s=this.WIDTH/i*(e.x+1)/2+h.left+a,n=-this.HEIGHT/r*(e.y-1)/2+h.top+o;l.push({x:s,y:n})})),e&&(l=l[0]),l}screenOffsetToModel(t,e,i){var r=t/this.WIDTH,s=e/this.HEIGHT,n=void 0===i?this.rotationGroup.position.z:i,a=this.rotationGroup.quaternion,o=new $.Vector3(0,0,n);return this.projector.projectVector(o,this.camera),o.x+=2*r,o.y-=2*s,this.projector.unprojectVector(o,this.camera),o.z=0,o.applyQuaternion(a),o}screenToModelDistance(t,e){let i=this.canvasOffset(),r=new $.Vector3(e.x,e.y,e.z);r.applyMatrix4(this.modelGroup.matrixWorld);let s=r.clone();this.projector.projectVector(r,this.camera);let n=new $.Vector3(2*(t.x-i.left)/this.WIDTH-1,2*(t.y-i.top)/-this.HEIGHT+1,r.z);return this.projector.unprojectVector(n,this.camera),n.distanceTo(s)}setViewChangeCallback(t){"function"!=typeof t&&null!=t||(this.viewChangeCallback=t)}setStateChangeCallback(t){"function"!=typeof t&&null!=t||(this.stateChangeCallback=t)}getConfig(){return this.config}setConfig(t){this.config=t,t.ambientOcclusion&&this.renderer.enableAmbientOcclusion(t.ambientOcclusion)}getInternalState(){var t={models:[],surfaces:[],shapes:[],labels:[]};for(let e=0;e<this.models.length;e++)this.models[e]&&(t.models[e]=this.models[e].getInternalState());return t}setInternalState(t){this.clear();var e=t.models;for(let t=0;t<e.length;t++)e[t]&&(this.models[t]=new GLModel(t),this.models[t].setInternalState(e[t]));this.render()}setZoomLimits(t,e){void 0!==t&&(this.config.lowerZoomLimit=t),e&&(this.config.upperZoomLimit=e),this.rotationGroup.position.z=this.adjustZoomToLimits(this.rotationGroup.position.z),this.show()}setCameraParameters(t){void 0!==t.fov&&(this.fov=t.fov,this.camera.fov=this.fov),void 0!==t.z&&(this.CAMERA_Z=t.z,this.camera.z=this.CAMERA_Z),void 0!==t.orthographic&&(this.camera.ortho=t.orthographic),this.setSlabAndFog()}_handleMouseDown(t){if(t.preventDefault(),!this.scene)return;var e=this.getX(t),i=this.getY(t);if(void 0===e)return;this.isDragging=!0,this.mouseButton=t.which,this.mouseStartX=e,this.mouseStartY=i,this.touchHold=!0,this.touchDistanceStart=0,t.targetTouches&&2==t.targetTouches.length&&(this.touchDistanceStart=this.calcTouchDistance(t)),this.cq=this.rotationGroup.quaternion.clone(),this.cz=this.rotationGroup.position.z,this.currentModelPos=this.modelGroup.position.clone(),this.cslabNear=this.slabNear,this.cslabFar=this.slabFar;let r=this;t.targetTouches&&1===t.targetTouches.length&&(this.longTouchTimeout=setTimeout((function(){if(1==r.touchHold){r.glDOM=r.renderer.domElement;const e=t.targetTouches[0],i=new PointerEvent("contextmenu",Object.assign(Object.assign({},t),{pageX:e.pageX,pageY:e.pageY,screenX:e.screenX,screenY:e.screenY,clientX:e.clientX,clientY:e.clientY}));r.glDOM.dispatchEvent(i)}}),this.longTouchDuration))}_handleMouseUp(t){if(this.touchHold=!1,this.isDragging&&this.scene){var e=this.getX(t),i=this.getY(t);if(this.closeEnoughForClick(t)&&this.isInViewer(e,i)){let r=this.mouseXY(e,i);this.handleClickSelection(r.x,r.y,t)}}this.isDragging=!1}_handleMouseScroll(t){if(t.preventDefault(),this.scene){var e=this.getX(t),i=this.getY(t);if(void 0!==e&&(this.control_all||this.isInViewer(e,i))){var r=.85*(this.CAMERA_Z-this.rotationGroup.position.z),s=1;if(t.ctrlKey&&(s=-1),t.detail)this.rotationGroup.position.z+=s*r*t.detail/10;else if(t.wheelDelta){let e=600*t.wheelDelta/(t.wheelDelta+600);this.rotationGroup.position.z-=s*r*e/400}this.rotationGroup.position.z=this.adjustZoomToLimits(this.rotationGroup.position.z),this.show()}}}pngURI(){return this.getCanvas().toDataURL("image/png")}apngURI(t){let e=this;return t=t||1,new Promise((function(i){let r=0,s=e.viewChangeCallback,n=[],a=[],o=Date.now();e.viewChangeCallback=function(){a.push(Date.now()-o),o=Date.now(),n.push(new Promise((t=>{e.getCanvas().toBlob((function(e){e.arrayBuffer().then(t)}),"image/png")}))),r+=1,r==t&&(e.viewChangeCallback=s,Promise.all(n).then((t=>{let r=[];for(let e=0;e<t.length;e++){let i=(0,Ht.decode)(t[e]);r.push((0,Ht.toRGBA8)(i)[0])}let s=e.getCanvas().width,n=e.getCanvas().height,o=(0,Ht.encode)(r,s,n,0,a),l=new Blob([o],{type:"image/png"}),h=new FileReader;h.onload=function(t){i(t.target.result)},h.readAsDataURL(l)})))}}))}getCanvas(){return this.glDOM}getRenderer(){return this.renderer}setHoverDuration(t){this.hoverDuration=t}mouseXY(t,e){let i=this.canvasOffset(),r=this.renderer.getXRatio(),s=this.renderer.getYRatio(),n=this.col,a=this.row,o=n*(this.WIDTH/r),l=(s-a-1)*(this.HEIGHT/s);return{x:(t-i.left-o)/(this.WIDTH/r)*2-1,y:-(e-i.top-l)/(this.HEIGHT/s)*2+1}}_handleMouseMove(t){clearTimeout(this.hoverTimeout),t.preventDefault();let e=this.getX(t),i=this.getY(t);if(void 0===e)return;let r=this.renderer.getXRatio(),s=this.renderer.getYRatio(),n=this.mouseXY(e,i),a=this;null!==this.current_hover&&this.handleHoverContinue(n.x,n.y);var o=0;if((this.control_all||this.isInViewer(e,i))&&this.scene&&(this.hoverables.length>0&&(this.hoverTimeout=setTimeout((function(){a.handleHoverSelection(n.x,n.y,t)}),this.hoverDuration)),this.isDragging)){t.targetTouches&&(t.targetTouches.length>1||1===t.targetTouches.length&&!this.closeEnoughForClick(t))&&clearTimeout(this.longTouchTimeout);var l=(e-this.mouseStartX)/this.WIDTH,h=(i-this.mouseStartY)/this.HEIGHT;if(0!=this.touchDistanceStart&&t.targetTouches&&2==t.targetTouches.length)o=2,h=2*(this.calcTouchDistance(t)-this.touchDistanceStart)/(this.WIDTH+this.HEIGHT);else t.targetTouches&&3==t.targetTouches.length&&(o=1);l*=r,h*=s;var c,d=Math.hypot(l,h);if(3==o||3==this.mouseButton&&t.ctrlKey)this.slabNear=this.cslabNear+100*l,this.slabFar=this.cslabFar-100*h;else if(2==o||3==this.mouseButton||t.shiftKey)(c=.85*(this.CAMERA_Z-this.rotationGroup.position.z))<80&&(c=80),this.rotationGroup.position.z=this.cz+h*c,this.rotationGroup.position.z=this.adjustZoomToLimits(this.rotationGroup.position.z);else if(1==o||2==this.mouseButton||t.ctrlKey){var u=this.screenOffsetToModel(r*(e-this.mouseStartX),s*(i-this.mouseStartY));this.modelGroup.position.addVectors(this.currentModelPos,u)}else if((0===o||1==this.mouseButton)&&0!==d){var f=Math.sin(d*Math.PI)/d;this.dq.x=Math.cos(d*Math.PI),this.dq.y=0,this.dq.z=f*l,this.dq.w=-f*h,this.rotationGroup.quaternion.set(1,0,0,0),this.rotationGroup.quaternion.multiply(this.dq),this.rotationGroup.quaternion.multiply(this.cq)}this.show()}}_handleContextMenu(t){if(t.preventDefault(),this.closeEnoughForClick(t)){var e=this.mouseStartX,i=this.mouseStartY,r=this.canvasOffset();let n=this.mouseXY(e,i),a=n.x,o=n.y,l=this.targetedObjects(a,o,this.contextMenuEnabledObjects);var s=null;l.length&&(s=l[0].clickable);r=this.canvasOffset(),e=this.mouseStartX-r.left,i=this.mouseStartY-r.top;this.userContextMenuHandler&&(this.userContextMenuHandler(s,e,i,l,t),this.isDragging=!1)}}setContainer(t){let e=(0,L.getElement)(t)||this.container;return this.initContainer(e),this}setBackgroundColor(t,e){(void 0===e||e<0||e>1)&&(e=1);var i=s.CC.color(t);return this.scene.fog.color=i,this.bgColor=i.getHex(),this.renderer.setClearColorHex(i.getHex(),e),this.show(),this}setProjection(t){this.camera.ortho="orthographic"===t,this.setSlabAndFog()}setViewStyle(t){if((t=t||{}).style=t.style||"",t.style.includes("outline")?this.renderer.enableOutline(t):this.renderer.disableOutline(),t.style.includes("ambientOcclusion")){var e={};t.strength&&(e.strength=t.strength),t.radius&&(e.radius=t.radius),this.renderer.enableAmbientOcclusion(e)}else this.renderer.disableAmbientOcclusion();return this}updateSize(){this.renderer.setSize(this.WIDTH,this.HEIGHT),this.ASPECT=this.renderer.getAspect(this.WIDTH,this.HEIGHT),this.renderer.setSize(this.WIDTH,this.HEIGHT),this.camera.aspect=this.ASPECT,this.camera.updateProjectionMatrix()}setWidth(t){return this.WIDTH=t||this.WIDTH,this.updateSize(),this}setHeight(t){return this.HEIGHT=t||this.HEIGHT,this.updateSize(),this}resize(){this.WIDTH=this.getWidth(),this.HEIGHT=this.getHeight();let t=!1;if(this.renderer.isLost()&&this.WIDTH>0&&this.HEIGHT>0){let e=!1,i=this.container.querySelector("canvas");i&&i!=this.renderer.getCanvas()?this.config.canvas=i:(i.remove(),this.config&&null!=this.config.canvas&&(delete this.config.canvas,e=!0)),this.setupRenderer(),this.initContainer(this.container),this.renderer.setClearColorHex(this.bgColor,this.config.backgroundAlpha),t=!0,e&&(this.config.canvas=this.renderer.getCanvas())}if(0==this.WIDTH||0==this.HEIGHT?this.animated&&this._viewer.pauseAnimate():this.animated&&this._viewer.resumeAnimate(),this.updateSize(),t){let t=this.renderer.supportedExtensions();if(t.regen=!0,this.viewers)for(let e=0,i=this.viewers.length;e<i;e++)for(let i=0,r=this.viewers[e].length;i<r;i++)this.viewers[e][i].render(null,t);this._viewer.render(null,t)}else this.show();return this}getModel(t){return void 0===t?0==this.models.length?null:this.models[this.models.length-1]:t instanceof GLModel?t:t in this.models?this.models[t]:0==this.models.length?null:this.models[this.models.length-1]}spin(t,e=1,i=!1){if(clearInterval(this.spinInterval),void 0===t&&(t="y"),"boolean"==typeof t){if(!t)return;t="y"}Array.isArray(t)&&(t={x:t[0],y:t[1],z:t[2]});var r=this;this.spinInterval=setInterval((function(){!r.getCanvas().isConnected&&r.renderer.isLost()&&clearInterval(r.spinInterval),(!i||r.container.checkVisibility&&r.container.checkVisibility())&&r.rotate(1*e,t)}),25)}animateMotion(t,e,i,r,s,n){var a=Math.ceil(t/20);a<1&&(a=1),this.incAnim();var o={mpos:this.modelGroup.position.clone(),rz:this.rotationGroup.position.z,rot:this.rotationGroup.quaternion.clone(),cam:this.lookingAt.clone()};if(e){let t=new Array(a);for(let e=0;e<a;e++){let l=(e+1)/a,h={mpos:o.mpos,rz:o.rz,rot:o.rot};h.mpos=i.clone().sub(o.mpos).multiplyScalar(l).add(o.mpos),h.rz=o.rz+l*(r-o.rz),h.rot=GLViewer.slerp(o.rot,s,l),h.cam=n.clone().sub(o.cam).multiplyScalar(l).add(o.cam),t[e]=h}let e=0,l=this,h=function(){var i=t[e];e+=1,l.modelGroup.position=i.mpos,l.rotationGroup.position.z=i.rz,l.rotationGroup.quaternion=i.rot,l.camera.lookAt(i.cam),e<t.length?setTimeout(h,20):l.decAnim(),l.show()};setTimeout(h,20)}else{var l={};let t=1/a;if(i&&(l.mpos=i.clone().sub(o.mpos).multiplyScalar(t)),void 0!==r&&null!=r&&(l.rz=t*(r-o.rz)),s){var h=GLViewer.slerp(o.rot,s,t);l.rot=o.rot.clone().inverse().multiply(h)}n&&(l.cam=n.clone().sub(o.cam).multiplyScalar(t));let e=0,c=this,d=function(){e+=1,l.mpos&&c.modelGroup.position.add(l.mpos),l.rz&&(c.rotationGroup.position.z+=l.rz),l.rot&&c.rotationGroup.quaternion.multiply(l.rot),l.cam&&(c.lookingAt.add(l.cam),c.camera.lookAt(c.lookingAt)),e<a?setTimeout(d,20):c.decAnim(),c.show()};setTimeout(d,20)}}rotate(t,e="y",i=0,r=!1){if("x"==e?e={x:1,y:0,z:0}:"y"==e?e={x:0,y:1,z:0}:"z"==e&&(e={x:0,y:0,z:1}),"vx"==e?e={vx:1,vy:0,vz:0}:"vy"==e?e={vx:0,vy:1,vz:0}:"vz"==e&&(e={vx:0,vy:0,vz:1}),void 0!==e.vx){var s=new $.Vector3(e.vx,e.vy,e.vz);s.applyQuaternion(this.rotationGroup.quaternion),e={x:s.x,y:s.y,z:s.z}}var n=function(t){var i,r,s,n=Math.sin(t/2),a=Math.cos(t/2);return i=e.x*n,r=e.y*n,s=e.z*n,new $.Quaternion(i,r,s,a).normalize()}(Math.PI*t/180);if(i){var a=(new $.Quaternion).copy(this.rotationGroup.quaternion).multiply(n);this.animateMotion(i,r,this.modelGroup.position,this.rotationGroup.position.z,a,this.lookingAt)}else this.rotationGroup.quaternion.multiply(n),this.show();return this}surfacesFinished(){for(var t in this.surfaces)if(!this.surfaces[t][0].done)return!1;return!0}getView(){if(!this.modelGroup)return[0,0,0,0,0,0,0,1];var t=this.modelGroup.position,e=this.rotationGroup.quaternion;return[t.x,t.y,t.z,this.rotationGroup.position.z,e.x,e.y,e.z,e.w]}setView(t,e){return void 0!==t&&(t instanceof Array||8!==t.length)&&this.modelGroup&&this.rotationGroup?(this.modelGroup.position.x=t[0],this.modelGroup.position.y=t[1],this.modelGroup.position.z=t[2],this.rotationGroup.position.z=t[3],this.rotationGroup.quaternion.x=t[4],this.rotationGroup.quaternion.y=t[5],this.rotationGroup.quaternion.z=t[6],this.rotationGroup.quaternion.w=t[7],void 0!==t[8]&&(this.rotationGroup.position.x=t[8],this.rotationGroup.position.y=t[9]),this.show(e),this):this}render(t,e){this.renderer.setViewport(),this.updateClickables();var i,r,s=this.getView();for(this.stateChangeCallback&&this.stateChangeCallback(this.getInternalState()),e||(e=this.renderer.supportedExtensions()),i=0;i<this.models.length;i++)this.models[i]&&this.models[i].globj(this.modelGroup,e);for(i=0;i<this.shapes.length;i++)this.shapes[i]&&(void 0===this.shapes[i].frame||this.viewer_frame<0||this.shapes[i].frame<0||this.shapes[i].frame==this.viewer_frame?this.shapes[i].globj(this.modelGroup,e):this.shapes[i].removegl(this.modelGroup));for(i=0;i<this.labels.length;i++)e.regen&&(this.labels[i].dispose(),this.modelGroup.remove(this.labels[i].sprite),this.labels[i].setContext(),this.modelGroup.add(this.labels[i].sprite)),this.labels[i]&&void 0!==this.labels[i].frame&&this.labels[i].frame>=0&&(this.modelGroup.remove(this.labels[i].sprite),(this.viewer_frame<0||this.labels[i].frame==this.viewer_frame)&&this.modelGroup.add(this.labels[i].sprite));for(i in this.surfaces)if(this.surfaces.hasOwnProperty(i)){var a=this.surfaces[i];for(r=0;r<a.length;r++)if(a.hasOwnProperty(r)){var o=a[r].geo;if(!a[r].finished||e.regen){o.verticesNeedUpdate=!0,o.elementsNeedUpdate=!0,o.normalsNeedUpdate=!0,o.colorsNeedUpdate=!0,o.buffersNeedUpdate=!0,a[r].mat.needsUpdate=!0,a[r].done&&(a[r].finished=!0),a[r].lastGL&&this.modelGroup.remove(a[r].lastGL);var l=null;if(l=a[r].mat instanceof n.LineBasicMaterial?new n.Line(o,a[r].mat):new n.Mesh(o,a[r].mat),a[r].mat.transparent&&0==a[r].mat.opacity?l.visible=!1:l.visible=!0,a[r].symmetries.length>1||1==a[r].symmetries.length&&!a[r].symmetries[r].isIdentity()){var h,c=new n.Object3D;for(h=0;h<a[r].symmetries.length;h++){var d=l.clone();d.matrix=a[r].symmetries[h],d.matrixAutoUpdate=!1,c.add(d)}a[r].lastGL=c,this.modelGroup.add(c)}else a[r].lastGL=l,this.modelGroup.add(l)}}}return this.setView(s),"function"==typeof t&&t(this),this}getModelList(t){let e=[];if(void 0===t||void 0===t.model)for(let t=0;t<this.models.length;t++)this.models[t]&&e.push(this.models[t]);else{let r=t.model;Array.isArray(r)||(r=[r]);for(let t=0;t<r.length;t++)if("number"==typeof r[t]){var i=r[t];i<0&&(i+=this.models.length),e.push(this.models[i])}else e.push(r[t])}return e}getAtomsFromSel(t){var e=[];void 0===t&&(t={});var i=this.getModelList(t);for(let r=0;r<i.length;r++)e=e.concat(i[r].selectedAtoms(t));return e}atomIsSelected(t,e){void 0===e&&(e={});for(var i=this.getModelList(e),r=0;r<i.length;r++)if(i[r].atomIsSelected(t,e))return!0;return!1}selectedAtoms(t){return this.getAtomsFromSel(t)}getUniqueValues(t,e){void 0===e&&(e={});var i=this.getAtomsFromSel(e),r={};for(var s in i){if(i[s].hasOwnProperty(t))r[i[s][t]]=!0}return Object.keys(r)}pdbData(t){for(var e=this.getAtomsFromSel(t),i="",r=0,s=e.length;r<s;++r)i+=e[r].pdbline+"\n";return i}zoom(t=2,e=0,i=!1){var r=(this.CAMERA_Z-this.rotationGroup.position.z)/t,s=this.CAMERA_Z-r;return e>0?this.animateMotion(e,i,this.modelGroup.position,this.adjustZoomToLimits(s),this.rotationGroup.quaternion,this.lookingAt):(this.rotationGroup.position.z=this.adjustZoomToLimits(s),this.show()),this}translate(t,e,i=0,r=!1){var s=t/this.WIDTH,n=e/this.HEIGHT,a=new $.Vector3(0,0,-this.CAMERA_Z);this.projector.projectVector(a,this.camera),a.x-=s,a.y-=n,this.projector.unprojectVector(a,this.camera),a.z=0;var o=this.lookingAt.clone().add(a);return i>0?this.animateMotion(i,r,this.modelGroup.position,this.rotationGroup.position.z,this.rotationGroup.quaternion,o):(this.lookingAt=o,this.camera.lookAt(this.lookingAt),this.show()),this}translateScene(t,e,i=0,r=!1){var s=this.screenOffsetToModel(t,e),n=this.modelGroup.position.clone().add(s);return i>0?this.animateMotion(i,r,this.modelGroup.position,this.rotationGroup.position.z,this.rotationGroup.quaternion,this.lookingAt):(this.modelGroup.position=n,this.show()),this}fitSlab(t){t=t||{};var e=this.getAtomsFromSel(t),i=(0,L.getExtent)(e),r=i[1][0]-i[0][0],s=i[1][1]-i[0][1],n=i[1][2]-i[0][2],a=Math.hypot(r,s,n);return a<5&&(a=5),this.slabNear=-a/1.9,this.slabFar=a/2,this}center(t={},e=0,i=!1){var r,s,n=this.getAtomsFromSel(t),a=(0,L.getExtent)(n);(0,L.isEmptyObject)(t)?(this.shapes.forEach((t=>{if(t&&t.boundingSphere&&t.boundingSphere.center){var e=t.boundingSphere.center,i=t.boundingSphere.radius;i>0?(n.push(new $.Vector3(e.x+i,e.y,e.z)),n.push(new $.Vector3(e.x-i,e.y,e.z)),n.push(new $.Vector3(e.x,e.y+i,e.z)),n.push(new $.Vector3(e.x,e.y-i,e.z)),n.push(new $.Vector3(e.x,e.y,e.z+i)),n.push(new $.Vector3(e.x,e.y,e.z-i))):n.push(e)}})),a=(0,L.getExtent)(n),r=n,s=a):(r=this.getAtomsFromSel({}),s=(0,L.getExtent)(r));var o=new $.Vector3(a[2][0],a[2][1],a[2][2]),l=s[1][0]-s[0][0],h=s[1][1]-s[0][1],c=s[1][2]-s[0][2],d=Math.hypot(l,h,c);d<5&&(d=5),this.slabNear=-d/1.9,this.slabFar=d/2,l=a[1][0]-a[0][0],h=a[1][1]-a[0][1],c=a[1][2]-a[0][2],(d=Math.hypot(l,h,c))<5&&(d=5);for(var u=25,f=0;f<n.length;f++)if(n[f]){var p=o.distanceToSquared(n[f]);p>u&&(u=p)}d=2*Math.sqrt(u);var g=o.clone().multiplyScalar(-1);return e>0?this.animateMotion(e,i,g,this.rotationGroup.position.z,this.rotationGroup.quaternion,this.lookingAt):(this.modelGroup.position=g,this.show()),this}zoomTo(t={},e=0,i=!1){let r=this.getAtomsFromSel(t),s=(0,L.getExtent)(r),n=s;if((0,L.isEmptyObject)(t)){let t=r&&r.length;if(this.shapes.forEach((t=>{if(t&&t.boundingSphere)if(t.boundingSphere.box){let e=t.boundingSphere.box;r.push(new $.Vector3(e.min.x,e.min.y,e.min.z)),r.push(new $.Vector3(e.max.x,e.max.y,e.max.z))}else if(t.boundingSphere.center){var e=t.boundingSphere.center,i=t.boundingSphere.radius;i>0?(r.push(new $.Vector3(e.x+i,e.y,e.z)),r.push(new $.Vector3(e.x-i,e.y,e.z)),r.push(new $.Vector3(e.x,e.y+i,e.z)),r.push(new $.Vector3(e.x,e.y-i,e.z)),r.push(new $.Vector3(e.x,e.y,e.z+i)),r.push(new $.Vector3(e.x,e.y,e.z-i))):r.push(e)}})),n=(0,L.getExtent)(r),!t)for(let t=0;t<3;t++)s[2][t]=(n[0][t]+n[1][t])/2}else{let t=this.getAtomsFromSel({});n=(0,L.getExtent)(t)}var a=new $.Vector3(s[2][0],s[2][1],s[2][2]),o=n[1][0]-n[0][0],l=n[1][1]-n[0][1],h=n[1][2]-n[0][2],c=Math.hypot(o,l,h);c<5&&(c=5),this.slabNear=-c/1.9,this.slabFar=c/2,0===Object.keys(t).length&&(this.slabNear=Math.min(2*-c,-50),this.slabFar=Math.max(2*c,50));var d=this.config.minimumZoomToDistance||5;o=s[1][0]-s[0][0],l=s[1][1]-s[0][1],h=s[1][2]-s[0][2],(c=Math.hypot(o,l,h))<d&&(c=d);for(var u=d*d,f=0;f<r.length;f++)if(r[f]){var p=a.distanceToSquared(r[f]);p>u&&(u=p)}c=2*Math.sqrt(u);var g=a.clone().multiplyScalar(-1),m=-(.5*c/Math.tan(Math.PI/180*this.camera.fov/2)-this.CAMERA_Z);return m=this.adjustZoomToLimits(m),e>0?this.animateMotion(e,i,g,m,this.rotationGroup.quaternion,this.lookingAt):(this.modelGroup.position=g,this.rotationGroup.position.z=m,this.show()),this}setSlab(t,e){this.slabNear=t,this.slabFar=e}getSlab(){return{near:this.slabNear,far:this.slabFar}}addLabel(t,e={},i,r=!1){if(i){var s=(0,L.getExtent)(this.getAtomsFromSel(i));e.position={x:s[2][0],y:s[2][1],z:s[2][2]}}var n=new Label(t,e);return n.setContext(),this.modelGroup.add(n.sprite),this.labels.push(n),r||this.show(),n}addResLabels(t,e,i=!1){let r=this.labels.length;return this.applyToModels("addResLabels",t,this,e,i),this.show(),this.labels.slice(r)}addPropertyLabels(t,e,i){return this.applyToModels("addPropertyLabels",t,e,this,i),this.show(),this}removeLabel(t){for(var e=0;e<this.labels.length;e++)if(this.labels[e]==t){this.labels.splice(e,1),t.dispose(),this.modelGroup.remove(t.sprite);break}return this.show(),this}removeAllLabels(){for(var t=0;t<this.labels.length;t++)this.labels[t]&&this.labels[t].sprite&&this.modelGroup.remove(this.labels[t].sprite);return this.labels.splice(0,this.labels.length),this.show(),this}hideAllLabels(){for(var t=0;t<this.labels.length;t++)this.labels[t]&&this.labels[t].hide();return this.show(),this}showAllLabels(){for(var t=0;t<this.labels.length;t++)this.labels[t]&&this.labels[t].show();return this.show(),this}setLabelStyle(t,e){return this.modelGroup.remove(t.sprite),t.dispose(),t.stylespec=e,t.setContext(),this.modelGroup.add(t.sprite),this.show(),t}setLabelText(t,e){return this.modelGroup.remove(t.sprite),t.dispose(),t.text=e,t.setContext(),this.modelGroup.add(t.sprite),this.show(),t}addShape(t){var e=new GLShape(t=t||{});return e.shapePosition=this.shapes.length,this.shapes.push(e),e}removeShape(t){if(!t)return this;for(t.removegl(this.modelGroup),delete this.shapes[t.shapePosition];this.shapes.length>0&&void 0===this.shapes[this.shapes.length-1];)this.shapes.pop();return this}removeAllShapes(){for(var t=0;t<this.shapes.length;t++){var e=this.shapes[t];e&&e.removegl(this.modelGroup)}return this.shapes.splice(0,this.shapes.length),this}getSelectionCenter(t){if(t.hasOwnProperty("x")&&t.hasOwnProperty("y")&&t.hasOwnProperty("z"))return t;var e=this.getAtomsFromSel(t);if(0==e.length)return{x:0,y:0,z:0};var i=(0,L.getExtent)(e);return{x:i[0][0]+(i[1][0]-i[0][0])/2,y:i[0][1]+(i[1][1]-i[0][1])/2,z:i[0][2]+(i[1][2]-i[0][2])/2}}addSphere(t){(t=t||{}).center=this.getSelectionCenter(t.center);var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addSphere(t),this.shapes.push(e),e.finalize(),e}addBox(t={}){null!=t.corner&&(t.corner=this.getSelectionCenter(t.corner)),null!=t.center&&(t.center=this.getSelectionCenter(t.center));var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addBox(t),this.shapes.push(e),e.finalize(),e}addArrow(t={}){t.start=this.getSelectionCenter(t.start),t.end=this.getSelectionCenter(t.end);var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addArrow(t),this.shapes.push(e),e.finalize(),e}addCylinder(t={}){t.start=this.getSelectionCenter(t.start),t.end=this.getSelectionCenter(t.end);var e=new GLShape(t);return e.shapePosition=this.shapes.length,t.dashed?e.addDashedCylinder(t):e.addCylinder(t),this.shapes.push(e),e.finalize(),e}addCurve(t={}){var e=new GLShape(t);return e.shapePosition=this.shapes.length,e.addCurve(t),this.shapes.push(e),e.finalize(),e}addLine(t={}){t.start=this.getSelectionCenter(t.start),t.end=this.getSelectionCenter(t.end),t.wireframe=!0;var e=new GLShape(t);return e.shapePosition=this.shapes.length,t.dashed?e=this.addLineDashed(t,e):e.addLine(t),this.shapes.push(e),e.finalize(),e}addUnitCell(t,e){t=this.getModel(t),(e=e||{alabel:"a",blabel:"b",clabel:"c"}).box=e.box||{},e.astyle=e.astyle||{color:"red",radius:.1,midpos:-1},e.bstyle=e.bstyle||{color:"green",radius:.1,midpos:-1},e.cstyle=e.cstyle||{color:"blue",radius:.1,midpos:-1},e.alabelstyle=e.alabelstyle||{fontColor:"red",showBackground:!1,alignment:"center",inFront:!1},e.blabelstyle=e.blabelstyle||{fontColor:"green",showBackground:!1,alignment:"center",inFront:!1},e.clabelstyle=e.clabelstyle||{fontColor:"blue",showBackground:!1,alignment:"center",inFront:!1},t.unitCellObjects&&this.removeUnitCell(t),t.unitCellObjects={shapes:[],labels:[]};var i=t.getCrystData(),r=null;if(i){if(i.matrix)r=i.matrix;else{var s,n,a,o=i.a,l=i.b,h=i.c,c=i.alpha,d=i.beta,u=i.gamma;c=c*Math.PI/180,d=d*Math.PI/180,u=u*Math.PI/180,s=Math.cos(d),n=(Math.cos(c)-Math.cos(d)*Math.cos(u))/Math.sin(u),a=Math.sqrt(Math.max(0,1-s*s-n*n)),r=new $.Matrix3(o,l*Math.cos(u),h*s,0,l*Math.sin(u),h*n,0,0,h*a)}var f=[new $.Vector3(0,0,0),new $.Vector3(1,0,0),new $.Vector3(0,1,0),new $.Vector3(0,0,1),new $.Vector3(1,1,0),new $.Vector3(0,1,1),new $.Vector3(1,0,1),new $.Vector3(1,1,1)];if(i.matrix4)for(let t=0;t<f.length;t++)i.size&&f[t].multiplyVectors(f[t],i.size),f[t]=f[t].applyMatrix4(i.matrix4);else for(let t=0;t<f.length;t++)f[t]=f[t].applyMatrix3(r);if(e.box&&!e.box.hidden){e.box.wireframe=!0;var p=new GLShape(e.box);p.shapePosition=this.shapes.length,p.addLine({start:f[0],end:f[1]}),p.addLine({start:f[0],end:f[2]}),p.addLine({start:f[1],end:f[4]}),p.addLine({start:f[2],end:f[4]}),p.addLine({start:f[0],end:f[3]}),p.addLine({start:f[3],end:f[5]}),p.addLine({start:f[2],end:f[5]}),p.addLine({start:f[1],end:f[6]}),p.addLine({start:f[4],end:f[7]}),p.addLine({start:f[6],end:f[7]}),p.addLine({start:f[3],end:f[6]}),p.addLine({start:f[5],end:f[7]}),this.shapes.push(p),t.unitCellObjects.shapes.push(p),p.finalize()}if(!e.astyle.hidden){e.astyle.start=f[0],e.astyle.end=f[1];let i=this.addArrow(e.astyle);t.unitCellObjects.shapes.push(i)}if(!e.bstyle.hidden){e.bstyle.start=f[0],e.bstyle.end=f[2];let i=this.addArrow(e.bstyle);t.unitCellObjects.shapes.push(i)}if(!e.cstyle.hidden){e.cstyle.start=f[0],e.cstyle.end=f[3];let i=this.addArrow(e.cstyle);t.unitCellObjects.shapes.push(i)}if(e.alabel){e.alabelstyle.position=f[1];let i=this.addLabel(e.alabel,e.alabelstyle);t.unitCellObjects.labels.push(i)}if(e.blabel){e.blabelstyle.position=f[2];let i=this.addLabel(e.blabel,e.blabelstyle);t.unitCellObjects.labels.push(i)}if(e.clabel){e.clabelstyle.position=f[3];let i=this.addLabel(e.clabel,e.clabelstyle);t.unitCellObjects.labels.push(i)}}}removeUnitCell(t){if((t=this.getModel(t)).unitCellObjects){let e=this;t.unitCellObjects.shapes.forEach((function(t){e.removeShape(t)})),t.unitCellObjects.labels.forEach((function(t){e.removeLabel(t)}))}delete t.unitCellObjects}replicateUnitCell(t=3,e=t,i=e,r,s,n){let a=(r=this.getModel(r)).getCrystData();if(a){const o=r.selectedAtoms({}),l=a.matrix;let h=function(t){return t%2==0?-t/2:Math.ceil(t/2)};t<=1&&e<=1&&i<=1&&(n=!0,t=e=i=3);let c=function(t,e,i){return!1};if(n){const t=(new $.Matrix3).getInverse3(l);c=function(e,i,r){let s=new $.Vector3(e,i,r).applyMatrix3(t);return!(s.x>-1e-4&&s.x<1.0001&&s.y>-1e-4&&s.y<1.0001&&s.z>-1e-4&&s.z<1.0001)}}for(let s=0;s<t;s++)for(let t=0;t<e;t++)for(let e=0;e<i;e++){if(0==s&&0==t&&0==e)continue;let i=new $.Vector3(h(s),h(t),h(e));i.applyMatrix3(l);let n=[];for(let t=0;t<o.length;t++){let e=o[t].x+i.x,r=o[t].y+i.y,s=o[t].z+i.z;if(c(e,r,s))continue;let a={};for(let e in o[t])a[e]=o[t][e];a.x=e,a.y=r,a.z=s,n.push(a)}r.addAtoms(n)}s&&r.assignBonds()}}addLineDashed(t,e){var i,r;t.dashLength=t.dashLength||.5,t.gapLength=t.gapLength||.5,i=t.start?new $.Vector3(t.start.x||0,t.start.y||0,t.start.z||0):new $.Vector3(0,0,0),r=t.end?new $.Vector3(t.end.x,t.end.y||0,t.end.z||0):new $.Vector3(0,0,0);var s,n,a,o=new $.Vector3,l=new $.Vector3,h=new $.Vector3,c=i.clone(),d=0;for(o.subVectors(r,i),s=o.length(),o.normalize(),l=o.clone(),h=o.clone(),l.multiplyScalar(t.dashLength),h.multiplyScalar(t.gapLength),n=l.length(),a=h.length();d<s;){if(d+n>s){t.start=i,t.end=r,e.addLine(t);break}c.addVectors(i,l),t.start=i,t.end=c,e.addLine(t),i=c.clone(),d+=n,c.addVectors(i,h),i=c.clone(),d+=a}return e.finalize(),e}addCustom(t){var e=new GLShape(t=t||{});return e.shapePosition=this.shapes.length,e.addCustom(t),this.shapes.push(e),e.finalize(),e}addVolumetricData(t,e,i={}){var r=new st.VolumeData(t,e);return i.hasOwnProperty("transferfn")?this.addVolumetricRender(r,i):this.addIsosurface(r,i)}addIsosurface(t,e={},i){var r=new GLShape(e);return r.shapePosition=this.shapes.length,r.addIsosurface(t,e,i,this),this.shapes.push(r),r}addVolumetricRender(t,e){var i=new GLVolumetricRender(t,e=e||{},this);return i.shapePosition=this.shapes.length,this.shapes.push(i),i}hasVolumetricRender(){return this.renderer.supportsVolumetric()}enableFog(t){t?this.scene.fog=new n.Fog(this.bgColor,100,200):(this.config.disableFog=!0,this.show())}setFrame(t){this.viewer_frame=t;let e=this;return new Promise((function(i){var r=e.models.map((function(i){return i.setFrame(t,e)}));Promise.all(r).then((function(){i()}))}))}getFrame(){return this.viewer_frame}getNumFrames(){var t=0;for(let e=0;e<this.models.length;e++)this.models[e].getNumFrames()>t&&(t=this.models[e].getNumFrames());for(let e=0;e<this.shapes.length;e++)this.shapes[e].frame&&this.shapes[e].frame>=t&&(t=this.shapes[e].frame+1);for(let e=0;e<this.labels.length;e++)this.labels[e].frame&&this.labels[e].frame>=t&&(t=this.labels[e].frame+1);return t}animate(t){this.incAnim();var e=100,i="forward",r=1/0;(t=t||{}).interval&&(e=t.interval),t.loop&&(i=t.loop),t.reps&&(r=t.reps);var s=this.getNumFrames(),n=this,a=0;t.startFrame&&(a=t.startFrame%s);var o=1;t.step&&(r/=o=t.step);var l,h,c=0,d=s*r,u=new Date,f=function(t){u=new Date,"forward"==t?n.setFrame(a).then((function(){a=(a+o)%s,l()})):"backward"==t?n.setFrame(s-1-a).then((function(){a=(a+o)%s,l()})):n.setFrame(a).then((function(){o*=(a+=o)%(s-1)==0?-1:1,l()}))};return l=function(){if(n.render(),n.getCanvas().isConnected)if(++c>=d||!n.isAnimated())h.cancel(),n.animationTimers.delete(h),n.decAnim();else{var t=e-((new Date).getTime()-u.getTime());t=t>0?t:0,n.animationTimers.delete(h),h=new L.PausableTimer(f,t,i),n.animationTimers.add(h)}else n.stopAnimate()},h=new L.PausableTimer(f,0,i),this.animationTimers.add(h),this}stopAnimate(){return this.animated=0,this.animationTimers.forEach((function(t){t.cancel()})),this.animationTimers=new Set,this}pauseAnimate(){return this.animationTimers.forEach((function(t){t.pause()})),this}resumeAnimate(){return this.animationTimers.forEach((function(t){t.resume()})),this}isAnimated(){return this.animated>0}getModelOpt(t){return t&&!t.defaultcolors?(t.defaultcolors=this.defaultcolors,t.cartoonQuality=t.cartoonQuality||this.config.cartoonQuality):void 0===t&&(t={defaultcolors:this.defaultcolors,cartoonQuality:this.config.cartoonQuality}),t}addModel(t,e="",i){i=this.getModelOpt(i);var r=new GLModel(this.models.length,i);return r.addMolData(t,e,i),this.models.push(r),r}addModels(t,e,i){(i=this.getModelOpt(i)).multimodel=!0,i.frames=!0;for(var r=GLModel.parseMolData(t,e,i),s=0;s<r.length;s++){var n=new GLModel(this.models.length,i);n.setAtomDefaults(r[s]),n.addFrame(r[s]),n.setFrame(0),r.modelData&&n.setModelData(r.modelData[s]),n.setDontDuplicateAtoms(!i.duplicateAssemblyAtoms),this.models.push(n)}return this.models}addModelsAsFrames(t,e,i){(i=this.getModelOpt(i)).multimodel=!0,i.frames=!0;var r=new GLModel(this.models.length,i);return r.addMolData(t,e,i),this.models.push(r),r}addAsOneMolecule(t,e,i){(i=this.getModelOpt(i)).multimodel=!0,i.onemol=!0;var r=new GLModel(this.models.length,i);return r.addMolData(t,e,i),this.models.push(r),r}removeModel(t){if(t=this.getModel(t)){for(t.removegl(this.modelGroup),delete this.models[t.getID()];this.models.length>0&&void 0===this.models[this.models.length-1];)this.models.pop();return this}}removeAllModels(){for(var t=0;t<this.models.length;t++){var e=this.models[t];e&&e.removegl(this.modelGroup)}return this.models.splice(0,this.models.length),this}exportJSON(t,e){var i={};return i.m=void 0===e?this.models.map((function(e){return e.toCDObject(t)})):[this.models[e].toCDObject()],JSON.stringify(i)}exportVRML(){var t=this.modelGroup;this.applyToModels("removegl",this.modelGroup),this.modelGroup=new n.Object3D,this.render(null,{supportsImposters:!1,supportsAIA:!1,regen:!0});var e="#VRML V2.0 utf8\n"+this.modelGroup.vrml()+"\n";return this.applyToModels("removegl",this.modelGroup),this.modelGroup=t,e}createModelFrom(t,e=!1){for(var i=new GLModel(this.models.length,this.defaultcolors),r=0;r<this.models.length;r++)if(this.models[r]){var s=this.models[r].selectedAtoms(t);i.addAtoms(s),e&&this.models[r].removeAtoms(s)}return this.models.push(i),i}applyToModels(t,e,i,r,s,n,a){for(var o=this.getModelList(e),l=0;l<o.length;l++)o[l][t](e,i,r,s,n,a)}setStyle(t,e){return void 0===e&&(e=t,t={}),this.applyToModels("setStyle",t,e,!1),this}addStyle(t,e){return void 0===e&&(e=t,t={}),this.applyToModels("setStyle",t,e,!0),this}setClickable(t,e,i){return this.applyToModels("setClickable",t,e,i),this}setHoverable(t,e,i,r){return this.applyToModels("setHoverable",t,e,i,r),this}enableContextMenu(t,e){return this.applyToModels("enableContextMenu",t,e),this}vibrate(t,e,i,r){return this.applyToModels("vibrate",t,e,i,this,r),this}setColorByProperty(t,e,i,r){return this.applyToModels("setColorByProperty",t,e,i,r),this}setColorByElement(t,e){return this.applyToModels("setColorByElement",t,e),this}static getAtomsWithin(t,e){var i=[];for(let s=0;s<t.length;s++){var r=t[s];void 0!==r&&(r.x<e[0][0]||r.x>e[1][0]||r.y<e[0][1]||r.y>e[1][1]||r.z<e[0][2]||r.z>e[1][2]||i.push(r))}return i}static volume(t){return(t[1][0]-t[0][0])*(t[1][1]-t[0][1])*(t[1][2]-t[0][2])}carveUpExtent(t,e,i){let r=[],s={};for(let t=0,i=e.length;t<i;t++)s[e[t].index]=t;let n=function(t){let e=[];for(let i=0,r=t.length;i<r;i++)t[i].index in s&&e.push(s[t[i].index]);return e},a=function(t){let e=[];return e[0]=[t[0][0],t[0][1],t[0][2]],e[1]=[t[1][0],t[1][1],t[1][2]],e},o=function(t){if(GLViewer.volume(t)<GLViewer.maxVolume)return[t];var e,i=t[1][0]-t[0][0],r=t[1][1]-t[0][1],s=t[1][2]-t[0][2];e=i>r&&i>s?0:r>i&&r>s?1:2;var n=a(t),l=a(t),h=(t[1][e]-t[0][e])/2+t[0][e];n[1][e]=h,l[0][e]=h;var c=o(n),d=o(l);return c.concat(d)},l=o(t);for(let t=0,s=l.length;t<s;t++){let s=a(l[t]);s[0][0]-=6,s[0][1]-=6,s[0][2]-=6,s[1][0]+=6,s[1][1]+=6,s[1][2]+=6;let o=GLViewer.getAtomsWithin(e,s),h=GLViewer.getAtomsWithin(i,l[t]);r.push({extent:l[t],atoms:n(o),toshow:n(h)})}return r}static generateSurfaceMesh(t,e,i){var r=new n.Geometry(!0),a=r.updateGeoGroup(0),o=[];for(let e=0,i=t.length;e<i;e++){var l=t[e];l&&(void 0!==l.surfaceColor?o[e]=l.surfaceColor:l.color&&(o[e]=s.CC.color(l.color)))}var h=a.vertexArray,c=e.vertices;for(let t=0,e=c.length;t<e;t++){let e=3*a.vertices;h[e]=c[t].x,h[e+1]=c[t].y,h[e+2]=c[t].z,a.vertices++}var d=a.colorArray;let u=a.atomArray;if(i.voldata&&i.volscheme){var f=i.volscheme,p=i.voldata,g=f.range()||[-1,1];for(let e=0,i=c.length;e<i;e++){let i=c[e].atomid,r=p.getVal(c[e].x,c[e].y,c[e].z),n=s.CC.color(f.valueToHex(r,g)),a=3*e;d[a]=n.r,d[a+1]=n.g,d[a+2]=n.b,u[e]=t[i]}}else if(o.length>0)for(let e=0,i=c.length;e<i;e++){let i=c[e].atomid,r=3*e;d[r]=o[i].r,d[r+1]=o[i].g,d[r+2]=o[i].b,u[e]=t[i]}var m=e.faces;a.faceidx=m.length,r.initTypedArrays();var v,_,y,b,x=a.vertexArray,w=a.normalArray;for(let t=0,e=m.length;t<e;t+=3){var A=3*m[t],C=3*m[t+1],S=3*m[t+2];v=new $.Vector3(x[A],x[A+1],x[A+2]),_=new $.Vector3(x[C],x[C+1],x[C+2]),(y=new $.Vector3(x[S],x[S+1],x[S+2])).subVectors(y,_),v.subVectors(v,_),y.cross(v),(b=y).normalize(),w[A]+=b.x,w[C]+=b.x,w[S]+=b.x,w[A+1]+=b.y,w[C+1]+=b.y,w[S+1]+=b.y,w[A+2]+=b.z,w[C+2]+=b.z,w[S+2]+=b.z}return a.faceArray=new Uint16Array(m),new n.Mesh(r,i)}static generateMeshSyncHelper(t,e,i,r,s,n){var a=new ProteinSurface;return a.initparm(e,1!==t,n),a.fillvoxels(s,i),a.buildboundary(),t!=Q.SES&&t!=Q.MS||(a.fastdistancemap(),a.boundingatom(!1),a.fillvoxelswaals(s,i)),a.marchingcube(t),a.getFacesAndVertices(r)}static getMatWithStyle(t){let e=null;for(var i in e=t.onesided?new n.MeshLambertMaterial:new n.MeshDoubleLambertMaterial,e.vertexColors=n.Coloring.VertexColors,t)"color"===i||"map"===i||t.hasOwnProperty(i)&&(e[i]=t[i]);return void 0!==t.opacity&&(1===t.opacity?e.transparent=!1:e.transparent=!0),e}addMesh(t){var e={geo:t.geometry,mat:t.material,done:!0,finished:!1},i=this.nextSurfID();return this.surfaces[i]=[e],i}static shallowCopy(t){var e=[];let i=t.length;for(let r=0;r<i;r++)e[r]=(0,L.extend)({},t[r]);return e}addSurface(t,e={},i={},s,a,o){let l=this.nextSurfID(),h=null,c=this,d=Q.VDW;"string"==typeof t?void 0!==GLViewer.surfaceTypeMap[t.toUpperCase()]?d=GLViewer.surfaceTypeMap[t]:console.log("Surface type : "+t+" is not recognized"):"number"==typeof t&&(d=t);var u=null,f=null,p=GLViewer.shallowCopy(this.getAtomsFromSel(i));u=s?GLViewer.shallowCopy(this.getAtomsFromSel(s)):p,(0,L.adjustVolumeStyle)(e);var g,m=!1;for(g=0;g<this.models.length;g++)if(this.models[g]){var v=this.models[g].getSymmetries();if(v.length>1||1==v.length&&!v[0].isIdentity()){m=!0;break}}var _=function(t,i,s){var n;f=a?GLViewer.shallowCopy(c.getAtomsFromSel(a)):s;var o=(0,L.getExtent)(s,!0);if(e.map&&e.map.prop){var u=e.map.prop;let t=(0,r.getGradient)(e.map.scheme||e.map.gradient||new r.Gradient.RWB),i=t.range();i||(i=(0,L.getPropertyRange)(s,u)),e.colorscheme={prop:u,gradient:t}}for(let t=0,r=i.length;t<r;t++)(n=i[t]).surfaceColor=(0,L.getColorFromStyle)(n,e);var p=GLViewer.volume(o),g=c.carveUpExtent(o,i,s);if(f&&f.length&&f.length>0){var m=(0,L.getExtent)(f,!0);g.sort((function(t,e){var i=function(t,e){var i=t.extent,r=i[1][0]-i[0][0],s=i[1][1]-i[0][1],n=i[1][2]-i[0][2],a=r-e[2][0];a*=a;var o=s-e[2][1];o*=o;var l=n-e[2][2];return a+o+(l*=l)};return i(t,m)-i(e,m)}))}var v=[];for(let t=0,e=i.length;t<e;t++)n=i[t],v[t]={x:n.x,y:n.y,z:n.z,serial:t,elem:n.elem};if(!!tt){var _=function(e){return new Promise((function(r){for(var s=GLViewer.generateMeshSyncHelper(d,g[e].extent,g[e].atoms,g[e].toshow,v,p),n=wt({vertexArr:s.vertices,faceArr:s.faces}),a=0,o=n.length;a<o;a++){s={vertices:n[a].vertexArr,faces:n[a].faceArr};var l=GLViewer.generateSurfaceMesh(i,s,h);(0,L.mergeGeos)(t.geo,l)}c.render(),r()}))},y=[];for(let t=0;t<g.length;t++)y.push(_(t));return Promise.all(y).then((function(){return t.done=!0,Promise.resolve(l)}))}var b=[];d<0&&(d=0);for(let t=0,e=GLViewer.numWorkers;t<e;t++){var x=new Worker($3Dmol.SurfaceWorker);b.push(x),x.postMessage({type:-1,atoms:v,volume:p})}return new Promise((function(e,r){var s=0,n=function(){b&&b.length&&b.forEach((function(t){t&&t.terminate&&t.terminate()}))},a=function(r){for(var a=wt({vertexArr:r.data.vertices,faceArr:r.data.faces}),o=0,d=a.length;o<d;o++){var u={vertices:a[o].vertexArr,faces:a[o].faceArr},f=GLViewer.generateSurfaceMesh(i,u,h);(0,L.mergeGeos)(t.geo,f)}c.render(),++s==g.length&&(t.done=!0,n(),e(l))},o=function(t){n(),console.log(t.message+" ("+t.filename+":"+t.lineno+")"),r(t)};for(let t=0;t<g.length;t++){var u=b[t%b.length];u.onmessage=a,u.onerror=o,u.postMessage({type:d,expandedExtent:g[t].extent,extendedAtoms:g[t].atoms,atomsToShow:g[t].toshow})}}))};e=e||{},h=GLViewer.getMatWithStyle(e);var y=[];y.style=e,y.atomsel=i,y.allsel=s,y.focus=a;var b=null;if(m){var x={},w={};for(g=0;g<this.models.length;g++)x[g]=[],w[g]=[];for(g=0;g<u.length;g++)x[u[g].model].push(u[g]);for(g=0;g<p.length;g++)w[p[g].model].push(p[g]);var A=[];for(g=0;g<this.models.length;g++)w[g].length>0&&(y.push({geo:new n.Geometry(!0),mat:h,done:!1,finished:!1,symmetries:this.models[g].getSymmetries()}),A.push(_(y[y.length-1],x[g],w[g])));b=Promise.all(A)}else y.push({geo:new n.Geometry(!0),mat:h,done:!1,finished:!1,symmetries:[new $.Matrix4]}),b=_(y[y.length-1],u,p);return this.surfaces[l]=y,b.surfid=l,o&&"function"==typeof o?(b.then((function(t){o(t)})),l):b}setSurfaceMaterialStyle(t,e){if((0,L.adjustVolumeStyle)(e),this.surfaces[t]){var i=this.surfaces[t];for(let t=0;t<i.length;t++){var r=i[t].mat=GLViewer.getMatWithStyle(e);if(i[t].mat.side=n.FrontSide,e.color){i[t].mat.color=s.CC.color(e.color),i[t].geo.colorsNeedUpdate=!0;const r=s.CC.color(e.color);i[t].geo.setColor(r)}else if(r.voldata&&r.volscheme){const e=r.volscheme,n=r.voldata,a=s.CC,o=e.range()||[-1,1];i[t].geo.setColors((function(t,i,r){let s=n.getVal(t,i,r);return a.color(e.valueToHex(s,o))}))}else{i[t].geo.colorsNeedUpdate=!0;for(let r of i[t].geo.geometryGroups)for(let t=0;t<r.vertices;t++){let i=(0,L.getColorFromStyle)(r.atomArray[t],e),s=3*t;r.colorArray[s]=i.r,r.colorArray[s+1]=i.g,r.colorArray[s+2]=i.b}}i[t].finished=!1}}return this}getSurface(t){return this.surfaces[t]}removeSurface(t){for(var e=this.surfaces[t],i=0;i<e.length;i++)e[i]&&e[i].lastGL&&(void 0!==e[i].geo&&e[i].geo.dispose(),void 0!==e[i].mat&&e[i].mat.dispose(),this.modelGroup.remove(e[i].lastGL));return delete this.surfaces[t],this.show(),this}removeAllSurfaces(){for(var t in this.surfaces)if(this.surfaces.hasOwnProperty(t)){for(var e=this.surfaces[t],i=0;i<e.length;i++)e[i]&&e[i].lastGL&&(void 0!==e[i].geo&&e[i].geo.dispose(),void 0!==e[i].mat&&e[i].mat.dispose(),this.modelGroup.remove(e[i].lastGL));delete this.surfaces[t]}return this.show(),this}jmolMoveTo(){var t=this.modelGroup.position,e="center { "+-t.x+" "+-t.y+" "+-t.z+" }; ",i=this.rotationGroup.quaternion;return e+="moveto .5 quaternion { "+i.x+" "+i.y+" "+i.z+" "+i.w+" };"}clear(){return this.removeAllSurfaces(),this.removeAllModels(),this.removeAllLabels(),this.removeAllShapes(),this.show(),this}mapAtomProperties(t,e){e=e||{};var i=this.getAtomsFromSel(e);if("function"==typeof t)for(let e=0,r=i.length;e<r;e++){t(i[e])}else for(let e=0,n=i.length;e<n;e++){var r=i[e];for(let e=0,i=t.length;e<i;e++){let i=t[e];if(i.props)for(var s in i.props)i.props.hasOwnProperty(s)&&this.atomIsSelected(r,i)&&(r.properties||(r.properties={}),r.properties[s]=i.props[s])}}return this}linkViewer(t){return this.linkedViewers.push(t),this}getPerceivedDistance(){return this.CAMERA_Z-this.rotationGroup.position.z}setPerceivedDistance(t){this.rotationGroup.position.z=this.CAMERA_Z-t}setAutoEyeSeparation(t,e){var i=this.getPerceivedDistance();return e||(e=5),t||this.camera.position.x>0?this.camera.position.x=i*Math.tan(Math.PI/180*e):this.camera.position.x=-i*Math.tan(Math.PI/180*e),this.camera.lookAt(new $.Vector3(0,0,this.rotationGroup.position.z)),this.camera.position.x}setDefaultCartoonQuality(t){this.config.cartoonQuality=t}}function qt(t,e){if(t=(0,L.getElement)(t)){e=e||{};try{return new GLViewer(t,e)}catch(t){throw"error creating viewer: "+t}}}function Yt(t,e={},i={}){if(t=(0,L.getElement)(t)){var r=[],s=document.createElement("canvas");i.rows=e.rows,i.cols=e.cols,i.control_all=null!=e.control_all&&e.control_all,t.appendChild(s);try{for(var n=0;n<e.rows;n++){for(var a=[],o=0;o<e.cols;o++){i.row=n,i.col=o,i.canvas=s,i.viewers=r,i.control_all=e.control_all;var l=qt(t,(0,L.extend)({},i));a.push(l)}r.unshift(a)}}catch(t){throw"error creating viewer grid: "+t}return r}}function Zt(t){var e=this;if(t=(0,L.getElement)(t)){var i=Yt(t,{rows:1,cols:2,control_all:!0});this.glviewer1=i[0][0],this.glviewer2=i[0][1],this.glviewer1.setAutoEyeSeparation(!1),this.glviewer2.setAutoEyeSeparation(!0),this.glviewer1.linkViewer(this.glviewer2),this.glviewer2.linkViewer(this.glviewer1);for(var r=Object.getOwnPropertyNames(this.glviewer1.__proto__).filter((function(t){return"function"==typeof e.glviewer1[t]})),s=0;s<r.length;s++)this[r[s]]=function(t){return function(){return[this.glviewer1[t].apply(this.glviewer1,arguments),this.glviewer2[t].apply(this.glviewer2,arguments)]}}(r[s]);this.setCoordinates=function(t,e,i){for(var r=0;r<t.length;r++)t[r].setCoordinates(e,i)},this.surfacesFinished=function(){return this.glviewer1.surfacesFinished()&&this.glviewer2.surfacesFinished()},this.isAnimated=function(){return this.glviewer1.isAnimated()||this.glviewer2.isAnimated()},this.render=function(t){this.glviewer1.render(),this.glviewer2.render(),t&&t(this)},this.getCanvas=function(){return this.glviewer1.getCanvas()}}}GLViewer.numWorkers=4,GLViewer.maxVolume=64e3,GLViewer.surfaceTypeMap={VDW:Q.VDW,MS:Q.MS,SAS:Q.SAS,SES:Q.SES};var Xt=!1,Kt=!1,Qt={};function $t(t,e){var i,r,n;if(null!=document.querySelector(".viewer_3Dmoljs")&&(Xt=!0),Xt){Kt=!0,t=null!=t?t:null;var a=0;document.querySelectorAll(".viewer_3Dmoljs").forEach((o=>{var l=[],h=[],c="";"static"==o.style.position&&(o.style.position="relative");var d=null;if(n=null,o.dataset.pdb)l.push("https://files.rcsb.org/view/"+o.dataset.pdb+".pdb"),h.push("pdb");else if(o.dataset.cid)h.push("sdf"),l.push("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/"+o.dataset.cid+"/SDF?record_type=3d");else if(o.dataset.href||o.dataset.url){if(c=o.dataset.href?o.dataset.href:o.dataset.url,l.push(c),"gz"==(n=c.substring(c.lastIndexOf(".")+1))){let t=c.substring(0,c.lastIndexOf(".")).lastIndexOf(".");n=c.substring(t+1)}h.push(n);var u=c.substring(c.lastIndexOf("/")+1,c.lastIndexOf("."));"/"==u&&(u=c.substring(c.lastIndexOf("/")+1)),o.dataset[h[h.length-1]]=u}let f=o.dataset;for(i in f)"pdb"===i.substring(0,3)&&"pdb"!==i?(l.push("https://files.rcsb.org/view/"+f[i]+".pdb"),h.push("pdb")):"href"===i.substring(0,4)&&"href"!==i?(c=f[i],l.push(c),h.push(c.substring(c.lastIndexOf(".")+1))):"cid"===i.substring(0,3)&&"cid"!==i&&(l.push("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/"+f[i]+"/SDF?record_type=3d"),h.push("sdf"));var p={};o.dataset.options&&(p=(0,L.specStringToObject)(o.dataset.options));var g=s.CC.color(o.dataset.backgroundcolor),m=o.dataset.backgroundalpha;m=null==m?1:parseFloat(m);var v={line:{}};o.dataset.style&&(v=(0,L.specStringToObject)(o.dataset.style));var _={};o.dataset.select&&(_=(0,L.specStringToObject)(o.dataset.select));var y=[],b=[],x=[],w={},A=null,C=o.dataset,S=/style(.+)/,M=/surface(.*)/,z=/labelres(.*)/,T=[];for(r in C)Object.prototype.hasOwnProperty.call(C,r)&&T.push(r);for(T.sort(),i=0;i<T.length;i++){r=T[i];var E,F,I,O=S.exec(r);O&&(E="select"+O[1],F=(0,L.specStringToObject)(C[E]),I=(0,L.specStringToObject)(C[r]),y.push([F,I])),(O=M.exec(r))&&(E="select"+O[1],F=(0,L.specStringToObject)(C[E]),I=(0,L.specStringToObject)(C[r]),b.push([F,I])),(O=z.exec(r))&&(E="select"+O[1],F=(0,L.specStringToObject)(C[E]),I=(0,L.specStringToObject)(C[r]),x.push([F,I])),"zoomto"==r&&(w=(0,L.specStringToObject)(C[r])),"spin"==r&&(A=(0,L.specStringToObject)(C[r]))}var D=function(t){for(t.setStyle(_,v),d&&d.createSelectionAndStyle(_,v),i=0;i<y.length;i++){let e=y[i][0]||{},r=y[i][1]||{line:{}};t.setStyle(e,r),d&&d.createSelectionAndStyle(_,v)}for(i=0;i<b.length;i++){let e=b[i][0]||{},r=b[i][1]||{};d?t.addSurface(Q.VDW,r,e,e).then((t=>{d.loadSurface("VDW",e,r,t)})):t.addSurface(Q.VDW,r,e,e)}for(i=0;i<x.length;i++){let e=x[i][0]||{},r=x[i][1]||{};t.addResLabels(e,r)}t.render(),t.zoomTo(w),A&&t.spin(A.axis,A.speed)};let k=t;try{var R=(0,L.specStringToObject)(o.dataset.config)||{};void 0===R.backgroundColor&&(R.backgroundColor=g),void 0===R.backgroundAlpha&&(R.backgroundAlpha=m),null==k?k=Qt[o.id||a++]=qt(o,R):(k.setBackgroundColor(g,m),k.setConfig(R),d&&d.initiateUI()),o.dataset.ui&&$3Dmol.StateManager&&(d=new $3Dmol.StateManager(k))}catch(t){console.log(t),o.textContent="WebGL appears to be disabled."}if(0!=l.length){let t=0,i=((r,s)=>function(n){c=l[t];var a=r.dataset.type||r.dataset.datatype||h[t];if(s.addModel(n,a,p),d){var 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y=3*p;l[M=p<<2]=e[y],l[M+1]=e[y+1],l[M+2]=e[y+2],l[M+3]=255,-1!=m&&e[y]==m&&e[y+1]==v&&e[y+2]==_&&(l[M+3]=0)}if(16==d)for(p=0;p<n;p++)y=6*p,l[M=p<<2]=e[y],l[M+1]=e[y+2],l[M+2]=e[y+4],l[M+3]=255,-1!=m&&u(e,y)==m&&u(e,y+2)==v&&u(e,y+4)==_&&(l[M+3]=0)}else if(3==c){var b=s.tabs.PLTE,x=s.tabs.tRNS,w=x?x.length:0;if(1==d)for(var A=0;A<r;A++){var C=A*o,S=A*i;for(p=0;p<i;p++){var M=S+p<<2,z=3*(T=e[C+(p>>3)]>>7-(7&p)&1);l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255}}if(2==d)for(A=0;A<r;A++)for(C=A*o,S=A*i,p=0;p<i;p++)M=S+p<<2,z=3*(T=e[C+(p>>2)]>>6-((3&p)<<1)&3),l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255;if(4==d)for(A=0;A<r;A++)for(C=A*o,S=A*i,p=0;p<i;p++)M=S+p<<2,z=3*(T=e[C+(p>>1)]>>4-((1&p)<<2)&15),l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255;if(8==d)for(p=0;p<n;p++){var T;M=p<<2,z=3*(T=e[p]),l[M]=b[z],l[M+1]=b[z+1],l[M+2]=b[z+2],l[M+3]=T<w?x[T]:255}}else if(4==c){if(8==d)for(p=0;p<n;p++){M=p<<2;var E=e[L=p<<1];l[M]=E,l[M+1]=E,l[M+2]=E,l[M+3]=e[L+1]}if(16==d)for(p=0;p<n;p++){var L;M=p<<2,E=e[L=p<<2],l[M]=E,l[M+1]=E,l[M+2]=E,l[M+3]=e[L+2]}}else if(0==c){if(m=s.tabs.tRNS?s.tabs.tRNS:-1,1==d)for(p=0;p<n;p++){var F=(E=255*(e[p>>3]>>7-(7&p)&1))==255*m?0:255;h[p]=F<<24|E<<16|E<<8|E}if(2==d)for(p=0;p<n;p++)F=(E=85*(e[p>>2]>>6-((3&p)<<1)&3))==85*m?0:255,h[p]=F<<24|E<<16|E<<8|E;if(4==d)for(p=0;p<n;p++)F=(E=17*(e[p>>1]>>4-((1&p)<<2)&15))==17*m?0:255,h[p]=F<<24|E<<16|E<<8|E;if(8==d)for(p=0;p<n;p++)F=(E=e[p])==m?0:255,h[p]=F<<24|E<<16|E<<8|E;if(16==d)for(p=0;p<n;p++)E=e[p<<1],F=u(e,p<<1)==m?0:255,h[p]=F<<24|E<<16|E<<8|E}return l},t.decode=function(e){for(var i,r=new Uint8Array(e),s=8,n=t._bin,a=n.readUshort,o=n.readUint,l={tabs:{},frames:[]},h=new Uint8Array(r.length),c=0,d=0,u=[137,80,78,71,13,10,26,10],f=0;f<8;f++)if(r[f]!=u[f])throw"The input is not a PNG file!";for(;s<r.length;){var p=n.readUint(r,s);s+=4;var g=n.readASCII(r,s,4);if(s+=4,"IHDR"==g)t.decode._IHDR(r,s,l);else if("IDAT"==g){for(f=0;f<p;f++)h[c+f]=r[s+f];c+=p}else if("acTL"==g)l.tabs[g]={num_frames:o(r,s),num_plays:o(r,s+4)},i=new Uint8Array(r.length);else if("fcTL"==g){0!=d&&((C=l.frames[l.frames.length-1]).data=t.decode._decompress(l,i.slice(0,d),C.rect.width,C.rect.height),d=0);var m={x:o(r,s+12),y:o(r,s+16),width:o(r,s+4),height:o(r,s+8)},v=a(r,s+22);v=a(r,s+20)/(0==v?100:v);var _={rect:m,delay:Math.round(1e3*v),dispose:r[s+24],blend:r[s+25]};l.frames.push(_)}else if("fdAT"==g){for(f=0;f<p-4;f++)i[d+f]=r[s+f+4];d+=p-4}else if("pHYs"==g)l.tabs[g]=[n.readUint(r,s),n.readUint(r,s+4),r[s+8]];else if("cHRM"==g)for(l.tabs[g]=[],f=0;f<8;f++)l.tabs[g].push(n.readUint(r,s+4*f));else if("tEXt"==g){null==l.tabs[g]&&(l.tabs[g]={});var y=n.nextZero(r,s),b=n.readASCII(r,s,y-s),x=n.readASCII(r,y+1,s+p-y-1);l.tabs[g][b]=x}else if("iTXt"==g){null==l.tabs[g]&&(l.tabs[g]={}),y=0;var w=s;y=n.nextZero(r,w),b=n.readASCII(r,w,y-w),r[w=y+1],r[w+1],w+=2,y=n.nextZero(r,w),n.readASCII(r,w,y-w),w=y+1,y=n.nextZero(r,w),n.readUTF8(r,w,y-w),w=y+1,x=n.readUTF8(r,w,p-(w-s)),l.tabs[g][b]=x}else if("PLTE"==g)l.tabs[g]=n.readBytes(r,s,p);else if("hIST"==g){var A=l.tabs.PLTE.length/3;for(l.tabs[g]=[],f=0;f<A;f++)l.tabs[g].push(a(r,s+2*f))}else if("tRNS"==g)3==l.ctype?l.tabs[g]=n.readBytes(r,s,p):0==l.ctype?l.tabs[g]=a(r,s):2==l.ctype&&(l.tabs[g]=[a(r,s),a(r,s+2),a(r,s+4)]);else if("gAMA"==g)l.tabs[g]=n.readUint(r,s)/1e5;else if("sRGB"==g)l.tabs[g]=r[s];else if("bKGD"==g)0==l.ctype||4==l.ctype?l.tabs[g]=[a(r,s)]:2==l.ctype||6==l.ctype?l.tabs[g]=[a(r,s),a(r,s+2),a(r,s+4)]:3==l.ctype&&(l.tabs[g]=r[s]);else if("IEND"==g){var C;0!=d&&((C=l.frames[l.frames.length-1]).data=t.decode._decompress(l,i.slice(0,d),C.rect.width,C.rect.height),d=0),l.data=t.decode._decompress(l,h,l.width,l.height);break}s+=p,n.readUint(r,s),s+=4}return delete l.compress,delete l.interlace,delete l.filter,l},t.decode._decompress=function(e,i,r,s){return 0==e.compress&&(i=t.decode._inflate(i)),0==e.interlace?i=t.decode._filterZero(i,e,0,r,s):1==e.interlace&&(i=t.decode._readInterlace(i,e)),i},t.decode._inflate=function(t){return e.inflate(t)},t.decode._readInterlace=function(e,i){for(var r=i.width,s=i.height,n=t.decode._getBPP(i),a=n>>3,o=Math.ceil(r*n/8),l=new Uint8Array(s*o),h=0,c=[0,0,4,0,2,0,1],d=[0,4,0,2,0,1,0],u=[8,8,8,4,4,2,2],f=[8,8,4,4,2,2,1],p=0;p<7;){for(var g=u[p],m=f[p],v=0,_=0,y=c[p];y<s;)y+=g,_++;for(var b=d[p];b<r;)b+=m,v++;var x=Math.ceil(v*n/8);t.decode._filterZero(e,i,h,v,_);for(var w=0,A=c[p];A<s;){for(var C=d[p],S=h+w*x<<3;C<r;){var M;if(1==n&&(M=(M=e[S>>3])>>7-(7&S)&1,l[A*o+(C>>3)]|=M<<7-(3&C)),2==n&&(M=(M=e[S>>3])>>6-(7&S)&3,l[A*o+(C>>2)]|=M<<6-((3&C)<<1)),4==n&&(M=(M=e[S>>3])>>4-(7&S)&15,l[A*o+(C>>1)]|=M<<4-((1&C)<<2)),n>=8)for(var z=A*o+C*a,T=0;T<a;T++)l[z+T]=e[(S>>3)+T];S+=n,C+=m}w++,A+=g}v*_!=0&&(h+=_*(1+x)),p+=1}return l},t.decode._getBPP=function(t){return[1,null,3,1,2,null,4][t.ctype]*t.depth},t.decode._filterZero=function(e,i,r,s,n){var a=t.decode._getBPP(i),o=Math.ceil(s*a/8),l=t.decode._paeth;a=Math.ceil(a/8);for(var h=0;h<n;h++){var c=r+h*o,d=c+h+1,u=e[d-1];if(0==u)for(var f=0;f<o;f++)e[c+f]=e[d+f];else if(1==u){for(f=0;f<a;f++)e[c+f]=e[d+f];for(f=a;f<o;f++)e[c+f]=e[d+f]+e[c+f-a]&255}else if(0==h){for(f=0;f<a;f++)e[c+f]=e[d+f];if(2==u)for(f=a;f<o;f++)e[c+f]=255&e[d+f];if(3==u)for(f=a;f<o;f++)e[c+f]=e[d+f]+(e[c+f-a]>>1)&255;if(4==u)for(f=a;f<o;f++)e[c+f]=e[d+f]+l(e[c+f-a],0,0)&255}else{if(2==u)for(f=0;f<o;f++)e[c+f]=e[d+f]+e[c+f-o]&255;if(3==u){for(f=0;f<a;f++)e[c+f]=e[d+f]+(e[c+f-o]>>1)&255;for(f=a;f<o;f++)e[c+f]=e[d+f]+(e[c+f-o]+e[c+f-a]>>1)&255}if(4==u){for(f=0;f<a;f++)e[c+f]=e[d+f]+l(0,e[c+f-o],0)&255;for(f=a;f<o;f++)e[c+f]=e[d+f]+l(e[c+f-a],e[c+f-o],e[c+f-a-o])&255}}}return e},t.decode._paeth=function(t,e,i){var r=t+e-i,s=Math.abs(r-t),n=Math.abs(r-e),a=Math.abs(r-i);return s<=n&&s<=a?t:n<=a?e:i},t.decode._IHDR=function(e,i,r){var s=t._bin;r.width=s.readUint(e,i),i+=4,r.height=s.readUint(e,i),i+=4,r.depth=e[i],i++,r.ctype=e[i],i++,r.compress=e[i],i++,r.filter=e[i],i++,r.interlace=e[i],i++},t._bin={nextZero:function(t,e){for(;0!=t[e];)e++;return e},readUshort:function(t,e){return t[e]<<8|t[e+1]},writeUshort:function(t,e,i){t[e]=i>>8&255,t[e+1]=255&i},readUint:function(t,e){return 16777216*t[e]+(t[e+1]<<16|t[e+2]<<8|t[e+3])},writeUint:function(t,e,i){t[e]=i>>24&255,t[e+1]=i>>16&255,t[e+2]=i>>8&255,t[e+3]=255&i},readASCII:function(t,e,i){for(var r="",s=0;s<i;s++)r+=String.fromCharCode(t[e+s]);return r},writeASCII:function(t,e,i){for(var r=0;r<i.length;r++)t[e+r]=i.charCodeAt(r)},readBytes:function(t,e,i){for(var r=[],s=0;s<i;s++)r.push(t[e+s]);return r},pad:function(t){return t.length<2?"0"+t:t},readUTF8:function(e,i,r){for(var s,n="",a=0;a<r;a++)n+="%"+t._bin.pad(e[i+a].toString(16));try{s=decodeURIComponent(n)}catch(s){return t._bin.readASCII(e,i,r)}return s}},t._copyTile=function(t,e,i,r,s,n,a,o,l){for(var h=Math.min(e,s),c=Math.min(i,n),d=0,u=0,f=0;f<c;f++)for(var p=0;p<h;p++)if(a>=0&&o>=0?(d=f*e+p<<2,u=(o+f)*s+a+p<<2):(d=(-o+f)*e-a+p<<2,u=f*s+p<<2),0==l)r[u]=t[d],r[u+1]=t[d+1],r[u+2]=t[d+2],r[u+3]=t[d+3];else if(1==l){var g=t[d+3]*(1/255),m=t[d]*g,v=t[d+1]*g,_=t[d+2]*g,y=r[u+3]*(1/255),b=r[u]*y,x=r[u+1]*y,w=r[u+2]*y,A=1-g,C=g+y*A,S=0==C?0:1/C;r[u+3]=255*C,r[u+0]=(m+b*A)*S,r[u+1]=(v+x*A)*S,r[u+2]=(_+w*A)*S}else if(2==l)g=t[d+3],m=t[d],v=t[d+1],_=t[d+2],y=r[u+3],b=r[u],x=r[u+1],w=r[u+2],g==y&&m==b&&v==x&&_==w?(r[u]=0,r[u+1]=0,r[u+2]=0,r[u+3]=0):(r[u]=m,r[u+1]=v,r[u+2]=_,r[u+3]=g);else if(3==l){if(g=t[d+3],m=t[d],v=t[d+1],_=t[d+2],y=r[u+3],b=r[u],x=r[u+1],w=r[u+2],g==y&&m==b&&v==x&&_==w)continue;if(g<220&&y>20)return!1}return!0},t.encode=function(e,i,r,s,n,a){null==s&&(s=0),null==a&&(a=!1);for(var o=new Uint8Array(e[0].byteLength*e.length+100),l=[137,80,78,71,13,10,26,10],h=0;h<8;h++)o[h]=l[h];var c=8,d=t._bin,u=t.crc.crc,f=d.writeUint,p=d.writeUshort,g=d.writeASCII,m=t.encode.compressPNG(e,i,r,s,a);f(o,c,13),g(o,c+=4,"IHDR"),f(o,c+=4,i),f(o,c+=4,r),o[c+=4]=m.depth,o[++c]=m.ctype,o[++c]=0,o[++c]=0,o[++c]=0,f(o,++c,u(o,c-17,17)),f(o,c+=4,1),g(o,c+=4,"sRGB"),o[c+=4]=1,f(o,++c,u(o,c-5,5)),c+=4;var v=e.length>1;if(v&&(f(o,c,8),g(o,c+=4,"acTL"),f(o,c+=4,e.length),f(o,c+=4,0),f(o,c+=4,u(o,c-12,12)),c+=4),3==m.ctype){for(f(o,c,3*(M=m.plte.length)),g(o,c+=4,"PLTE"),c+=4,h=0;h<M;h++){var _=3*h,y=m.plte[h],b=255&y,x=y>>8&255,w=y>>16&255;o[c+_+0]=b,o[c+_+1]=x,o[c+_+2]=w}if(f(o,c+=3*M,u(o,c-3*M-4,3*M+4)),c+=4,m.gotAlpha){for(f(o,c,M),g(o,c+=4,"tRNS"),c+=4,h=0;h<M;h++)o[c+h]=m.plte[h]>>24&255;f(o,c+=M,u(o,c-M-4,M+4)),c+=4}}for(var A=0,C=0;C<m.frames.length;C++){var S=m.frames[C];v&&(f(o,c,26),g(o,c+=4,"fcTL"),f(o,c+=4,A++),f(o,c+=4,S.rect.width),f(o,c+=4,S.rect.height),f(o,c+=4,S.rect.x),f(o,c+=4,S.rect.y),p(o,c+=4,n[C]),p(o,c+=2,1e3),o[c+=2]=S.dispose,o[++c]=S.blend,f(o,++c,u(o,c-30,30)),c+=4);var M,z=S.cimg;f(o,c,(M=z.length)+(0==C?0:4));var T=c+=4;for(g(o,c,0==C?"IDAT":"fdAT"),c+=4,0!=C&&(f(o,c,A++),c+=4),h=0;h<M;h++)o[c+h]=z[h];f(o,c+=M,u(o,T,c-T)),c+=4}return f(o,c,0),g(o,c+=4,"IEND"),f(o,c+=4,u(o,c-4,4)),c+=4,o.buffer.slice(0,c)},t.encode.compressPNG=function(e,i,r,s,n){for(var a=t.encode.compress(e,i,r,s,!1,n),o=0;o<e.length;o++){var l=a.frames[o],h=(l.rect.width,l.rect.height),c=l.bpl,d=l.bpp,u=new Uint8Array(h*c+h);l.cimg=t.encode._filterZero(l.img,h,d,c,u)}return a},t.encode.compress=function(e,i,r,s,n,a){null==a&&(a=!1);for(var o=6,l=8,h=4,c=255,d=0;d<e.length;d++)for(var u=new Uint8Array(e[d]),f=u.length,p=0;p<f;p+=4)c&=u[p+3];var g=255!=c,m={},v=[];if(0!=e.length&&(m[0]=0,v.push(0),0!=s&&s--),0!=s){var _=t.quantize(e,s,n);for(e=_.bufs,p=0;p<_.plte.length;p++)null==m[b=_.plte[p].est.rgba]&&(m[b]=v.length,v.push(b))}else for(d=0;d<e.length;d++){var y=new Uint32Array(e[d]);for(f=y.length,p=0;p<f;p++){var b=y[p];if((p<i||b!=y[p-1]&&b!=y[p-i])&&null==m[b]&&(m[b]=v.length,v.push(b),v.length>=300))break}}var x=!!g&&n,w=v.length;w<=256&&0==a&&(l=w<=2?1:w<=4?2:w<=16?4:8,n&&(l=8),g=!0);var A=[];for(d=0;d<e.length;d++){var C=new Uint8Array(e[d]),S=new Uint32Array(C.buffer),M=0,z=0,T=i,E=r,L=0;if(0!=d&&!x){for(var F=n||1==d||2==A[A.length-2].dispose?1:2,I=0,O=1e9,D=0;D<F;D++){for(var k=new Uint8Array(e[d-1-D]),R=new Uint32Array(e[d-1-D]),P=i,U=r,B=-1,N=-1,G=0;G<r;G++)for(var V=0;V<i;V++)S[p=G*i+V]!=R[p]&&(V<P&&(P=V),V>B&&(B=V),G<U&&(U=G),G>N&&(N=G));var j=-1==B?1:(B-P+1)*(N-U+1);j<O&&(O=j,I=D,-1==B?(M=z=0,T=E=1):(M=P,z=U,T=B-P+1,E=N-U+1))}k=new Uint8Array(e[d-1-I]),1==I&&(A[A.length-1].dispose=2);var H=new Uint8Array(T*E*4);new Uint32Array(H.buffer),t._copyTile(k,i,r,H,T,E,-M,-z,0),t._copyTile(C,i,r,H,T,E,-M,-z,3)?(t._copyTile(C,i,r,H,T,E,-M,-z,2),L=1):(t._copyTile(C,i,r,H,T,E,-M,-z,0),L=0),C=H,S=new Uint32Array(C.buffer)}var W=4*T;if(w<=256&&0==a){for(W=Math.ceil(l*T/8),H=new Uint8Array(W*E),G=0;G<E;G++){p=G*W;var q=G*T;if(8==l)for(V=0;V<T;V++)H[p+V]=m[S[q+V]];else if(4==l)for(V=0;V<T;V++)H[p+(V>>1)]|=m[S[q+V]]<<4-4*(1&V);else if(2==l)for(V=0;V<T;V++)H[p+(V>>2)]|=m[S[q+V]]<<6-2*(3&V);else if(1==l)for(V=0;V<T;V++)H[p+(V>>3)]|=m[S[q+V]]<<7-1*(7&V)}C=H,o=3,h=1}else if(0==g&&1==e.length){H=new Uint8Array(T*E*3);var Y=T*E;for(p=0;p<Y;p++){var Z=3*p,X=4*p;H[Z]=C[X],H[Z+1]=C[X+1],H[Z+2]=C[X+2]}C=H,o=2,h=3,W=3*T}A.push({rect:{x:M,y:z,width:T,height:E},img:C,bpl:W,bpp:h,blend:L,dispose:x?1:0})}return{ctype:o,depth:l,plte:v,gotAlpha:g,frames:A}},t.encode._filterZero=function(i,r,s,n,a){for(var o=[],l=0;l<5;l++)if(!(r*n>5e5)||2!=l&&3!=l&&4!=l){for(var h=0;h<r;h++)t.encode._filterLine(a,i,h,n,s,l);if(o.push(e.deflate(a)),1==s)break}for(var c,d=1e9,u=0;u<o.length;u++)o[u].length<d&&(c=u,d=o[u].length);return o[c]},t.encode._filterLine=function(e,i,r,s,n,a){var o=r*s,l=o+r,h=t.decode._paeth;if(e[l]=a,l++,0==a)for(var c=0;c<s;c++)e[l+c]=i[o+c];else if(1==a){for(c=0;c<n;c++)e[l+c]=i[o+c];for(c=n;c<s;c++)e[l+c]=i[o+c]-i[o+c-n]+256&255}else if(0==r){for(c=0;c<n;c++)e[l+c]=i[o+c];if(2==a)for(c=n;c<s;c++)e[l+c]=i[o+c];if(3==a)for(c=n;c<s;c++)e[l+c]=i[o+c]-(i[o+c-n]>>1)+256&255;if(4==a)for(c=n;c<s;c++)e[l+c]=i[o+c]-h(i[o+c-n],0,0)+256&255}else{if(2==a)for(c=0;c<s;c++)e[l+c]=i[o+c]+256-i[o+c-s]&255;if(3==a){for(c=0;c<n;c++)e[l+c]=i[o+c]+256-(i[o+c-s]>>1)&255;for(c=n;c<s;c++)e[l+c]=i[o+c]+256-(i[o+c-s]+i[o+c-n]>>1)&255}if(4==a){for(c=0;c<n;c++)e[l+c]=i[o+c]+256-h(0,i[o+c-s],0)&255;for(c=n;c<s;c++)e[l+c]=i[o+c]+256-h(i[o+c-n],i[o+c-s],i[o+c-n-s])&255}}},t.crc={table:function(){for(var t=new Uint32Array(256),e=0;e<256;e++){for(var i=e,r=0;r<8;r++)1&i?i=3988292384^i>>>1:i>>>=1;t[e]=i}return t}(),update:function(e,i,r,s){for(var n=0;n<s;n++)e=t.crc.table[255&(e^i[r+n])]^e>>>8;return e},crc:function(e,i,r){return 4294967295^t.crc.update(4294967295,e,i,r)}},t.quantize=function(e,i,r){for(var s=[],n=0,a=0;a<e.length;a++)s.push(t.encode.alphaMul(new Uint8Array(e[a]),r)),n+=e[a].byteLength;var o=new Uint8Array(n),l=new Uint32Array(o.buffer),h=0;for(a=0;a<s.length;a++){for(var c=s[a],d=c.length,u=0;u<d;u++)o[h+u]=c[u];h+=d}var f={i0:0,i1:o.length,bst:null,est:null,tdst:0,left:null,right:null};f.bst=t.quantize.stats(o,f.i0,f.i1),f.est=t.quantize.estats(f.bst);for(var p=[f];p.length<i;){var g=0,m=0;for(a=0;a<p.length;a++)p[a].est.L>g&&(g=p[a].est.L,m=a);if(g<.001)break;var v=p[m],_=t.quantize.splitPixels(o,l,v.i0,v.i1,v.est.e,v.est.eMq255),y={i0:v.i0,i1:_,bst:null,est:null,tdst:0,left:null,right:null};y.bst=t.quantize.stats(o,y.i0,y.i1),y.est=t.quantize.estats(y.bst);var b={i0:_,i1:v.i1,bst:null,est:null,tdst:0,left:null,right:null};for(b.bst={R:[],m:[],N:v.bst.N-y.bst.N},a=0;a<16;a++)b.bst.R[a]=v.bst.R[a]-y.bst.R[a];for(a=0;a<4;a++)b.bst.m[a]=v.bst.m[a]-y.bst.m[a];b.est=t.quantize.estats(b.bst),v.left=y,v.right=b,p[m]=y,p.push(b)}p.sort((function(t,e){return e.bst.N-t.bst.N}));for(var x=0;x<s.length;x++){var w=t.quantize.planeDst,A=new Uint8Array(s[x].buffer),C=new Uint32Array(s[x].buffer),S=A.length;for(a=0;a<S;a+=4){for(var M=A[a]*(1/255),z=A[a+1]*(1/255),T=A[a+2]*(1/255),E=A[a+3]*(1/255),L=f;L.left;)L=w(L.est,M,z,T,E)<=0?L.left:L.right;C[a>>2]=L.est.rgba}s[x]=C.buffer}return{bufs:s,plte:p}},t.quantize.getNearest=function(e,i,r,s,n){if(null==e.left)return e.tdst=t.quantize.dist(e.est.q,i,r,s,n),e;var a=t.quantize.planeDst(e.est,i,r,s,n),o=e.left,l=e.right;a>0&&(o=e.right,l=e.left);var h=t.quantize.getNearest(o,i,r,s,n);if(h.tdst<=a*a)return h;var c=t.quantize.getNearest(l,i,r,s,n);return c.tdst<h.tdst?c:h},t.quantize.planeDst=function(t,e,i,r,s){var n=t.e;return n[0]*e+n[1]*i+n[2]*r+n[3]*s-t.eMq},t.quantize.dist=function(t,e,i,r,s){var n=e-t[0],a=i-t[1],o=r-t[2],l=s-t[3];return n*n+a*a+o*o+l*l},t.quantize.splitPixels=function(e,i,r,s,n,a){var o=t.quantize.vecDot;for(s-=4;r<s;){for(;o(e,r,n)<=a;)r+=4;for(;o(e,s,n)>a;)s-=4;if(r>=s)break;var l=i[r>>2];i[r>>2]=i[s>>2],i[s>>2]=l,r+=4,s-=4}for(;o(e,r,n)>a;)r-=4;return r+4},t.quantize.vecDot=function(t,e,i){return t[e]*i[0]+t[e+1]*i[1]+t[e+2]*i[2]+t[e+3]*i[3]},t.quantize.stats=function(t,e,i){for(var r=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],s=[0,0,0,0],n=i-e>>2,a=e;a<i;a+=4){var o=t[a]*(1/255),l=t[a+1]*(1/255),h=t[a+2]*(1/255),c=t[a+3]*(1/255);s[0]+=o,s[1]+=l,s[2]+=h,s[3]+=c,r[0]+=o*o,r[1]+=o*l,r[2]+=o*h,r[3]+=o*c,r[5]+=l*l,r[6]+=l*h,r[7]+=l*c,r[10]+=h*h,r[11]+=h*c,r[15]+=c*c}return r[4]=r[1],r[8]=r[2],r[12]=r[3],r[9]=r[6],r[13]=r[7],r[14]=r[11],{R:r,m:s,N:n}},t.quantize.estats=function(e){var i=e.R,r=e.m,s=e.N,n=r[0],a=r[1],o=r[2],l=r[3],h=0==s?0:1/s,c=[i[0]-n*n*h,i[1]-n*a*h,i[2]-n*o*h,i[3]-n*l*h,i[4]-a*n*h,i[5]-a*a*h,i[6]-a*o*h,i[7]-a*l*h,i[8]-o*n*h,i[9]-o*a*h,i[10]-o*o*h,i[11]-o*l*h,i[12]-l*n*h,i[13]-l*a*h,i[14]-l*o*h,i[15]-l*l*h],d=c,u=t.M4,f=[.5,.5,.5,.5],p=0,g=0;if(0!=s)for(var m=0;m<10&&(f=u.multVec(d,f),g=Math.sqrt(u.dot(f,f)),f=u.sml(1/g,f),!(Math.abs(g-p)<1e-9));m++)p=g;var v=[n*h,a*h,o*h,l*h],_=u.dot(u.sml(255,v),f),y=v[3]<.001?0:1/v[3];return{Cov:c,q:v,e:f,L:p,eMq255:_,eMq:u.dot(f,v),rgba:(Math.round(255*v[3])<<24|Math.round(255*v[2]*y)<<16|Math.round(255*v[1]*y)<<8|Math.round(255*v[0]*y))>>>0}},t.M4={multVec:function(t,e){return[t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3],t[4]*e[0]+t[5]*e[1]+t[6]*e[2]+t[7]*e[3],t[8]*e[0]+t[9]*e[1]+t[10]*e[2]+t[11]*e[3],t[12]*e[0]+t[13]*e[1]+t[14]*e[2]+t[15]*e[3]]},dot:function(t,e){return t[0]*e[0]+t[1]*e[1]+t[2]*e[2]+t[3]*e[3]},sml:function(t,e){return[t*e[0],t*e[1],t*e[2],t*e[3]]}},t.encode.alphaMul=function(t,e){for(var i=new 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t.insert=0,4===e?(y(t,!0),0===t.strm.avail_out?3:4):(t.strstart>t.block_start&&(y(t,!1),t.strm.avail_out),1)})),new M(4,4,8,4,C),new M(4,5,16,8,C),new M(4,6,32,32,C),new M(4,4,16,16,S),new M(8,16,32,32,S),new M(8,16,128,128,S),new M(8,32,128,256,S),new M(32,128,258,1024,S),new M(32,258,258,4096,S)],e.deflateInit=function(t,e){return L(t,e,8,15,8,0)},e.deflateInit2=L,e.deflateReset=E,e.deflateResetKeep=T,e.deflateSetHeader=function(t,e){return t&&t.state?2!==t.state.wrap?h:(t.state.gzhead=e,0):h},e.deflate=function(t,e){var i,s,a,l;if(!t||!t.state||e>5||e<0)return t?g(t,h):h;if(s=t.state,!t.output||!t.input&&0!==t.avail_in||s.status===p&&4!==e)return 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t;m--,_+=u[p++]<<y,y+=8}_>>>=L,y-=L,i.back+=L}if(_>>>=z,y-=z,i.back+=z,i.length=E,0===T){i.mode=26;break}if(32&T){i.back=-1,i.mode=h;break}if(64&T){t.msg="invalid literal/length code",i.mode=c;break}i.extra=15&T,i.mode=22;case 22:if(i.extra){for(R=i.extra;y<R;){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}i.length+=_&(1<<i.extra)-1,_>>>=i.extra,y-=i.extra,i.back+=i.extra}i.was=i.length,i.mode=23;case 23:for(;T=(P=i.distcode[_&(1<<i.distbits)-1])>>>16&255,E=65535&P,!((z=P>>>24)<=y);){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}if(!(240&T)){for(L=z,F=T,I=E;T=(P=i.distcode[I+((_&(1<<L+F)-1)>>L)])>>>16&255,E=65535&P,!(L+(z=P>>>24)<=y);){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}_>>>=L,y-=L,i.back+=L}if(_>>>=z,y-=z,i.back+=z,64&T){t.msg="invalid distance code",i.mode=c;break}i.offset=E,i.extra=15&T,i.mode=24;case 24:if(i.extra){for(R=i.extra;y<R;){if(0===m)break t;m--,_+=u[p++]<<y,y+=8}i.offset+=_&(1<<i.extra)-1,_>>>=i.extra,y-=i.extra,i.back+=i.extra}if(i.offset>i.dmax){t.msg="invalid distance too far 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t.next_out=g,t.avail_out=v,t.next_in=p,t.avail_in=m,i.hold=_,i.bits=y,(i.wsize||A!==t.avail_out&&i.mode<c&&(i.mode<27||4!==e))&&x(t,t.output,t.next_out,A-t.avail_out)?(i.mode=31,-4):(w-=t.avail_in,A-=t.avail_out,t.total_in+=w,t.total_out+=A,i.total+=A,i.wrap&&A&&(t.adler=i.check=i.flags?n(i.check,f,A,t.next_out-A):s(i.check,f,A,t.next_out-A)),t.data_type=i.bits+(i.last?64:0)+(i.mode===h?128:0)+(20===i.mode||15===i.mode?256:0),(0===w&&0===A||4===e)&&0===D&&(D=-5),D)},e.inflateEnd=function(t){if(!t||!t.state)return l;var e=t.state;return e.window&&(e.window=null),t.state=null,0},e.inflateGetHeader=function(t,e){var i;return t&&t.state&&2&(i=t.state).wrap?(i.head=e,e.done=!1,0):l},e.inflateSetDictionary=function(t,e){var i,r=e.length;return t&&t.state?0!==(i=t.state).wrap&&11!==i.mode?l:11===i.mode&&s(1,e,r,0)!==i.check?-3:x(t,e,r,r)?(i.mode=31,-4):(i.havedict=1,0):l},e.inflateInfo="pako inflate (from Nodeca project)"},358:(t,e,i)=>{"use strict";var r=i(981),s=15,n=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0],a=[16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78],o=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0],l=[16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64];t.exports=function(t,e,i,h,c,d,u,f){var p,g,m,v,_,y,b,x,w,A=f.bits,C=0,S=0,M=0,z=0,T=0,E=0,L=0,F=0,I=0,O=0,D=null,k=0,R=new r.Buf16(16),P=new r.Buf16(16),U=null,B=0;for(C=0;C<=s;C++)R[C]=0;for(S=0;S<h;S++)R[e[i+S]]++;for(T=A,z=s;z>=1&&0===R[z];z--);if(T>z&&(T=z),0===z)return c[d++]=20971520,c[d++]=20971520,f.bits=1,0;for(M=1;M<z&&0===R[M];M++);for(T<M&&(T=M),F=1,C=1;C<=s;C++)if(F<<=1,(F-=R[C])<0)return-1;if(F>0&&(0===t||1!==z))return-1;for(P[1]=0,C=1;C<s;C++)P[C+1]=P[C]+R[C];for(S=0;S<h;S++)0!==e[i+S]&&(u[P[e[i+S]]++]=S);if(0===t?(D=U=u,y=19):1===t?(D=n,k-=257,U=a,B-=257,y=256):(D=o,U=l,y=-1),O=0,S=0,C=M,_=d,E=T,L=0,m=-1,v=(I=1<<T)-1,1===t&&I>852||2===t&&I>592)return 1;for(;;){b=C-L,u[S]<y?(x=0,w=u[S]):u[S]>y?(x=U[B+u[S]],w=D[k+u[S]]):(x=96,w=0),p=1<<C-L,M=g=1<<E;do{c[_+(O>>L)+(g-=p)]=b<<24|x<<16|w}while(0!==g);for(p=1<<C-1;O&p;)p>>=1;if(0!==p?(O&=p-1,O+=p):O=0,S++,0==--R[C]){if(C===z)break;C=e[i+u[S]]}if(C>T&&(O&v)!==m){for(0===L&&(L=T),_+=M,F=1<<(E=C-L);E+L<z&&!((F-=R[E+L])<=0);)E++,F<<=1;if(I+=1<<E,1===t&&I>852||2===t&&I>592)return 1;c[m=O&v]=T<<24|E<<16|_-d}}return 0!==O&&(c[_+O]=C-L<<24|64<<16),f.bits=T,0}},834:t=>{"use strict";t.exports={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient 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e,i,r,s=t.w_size;e=t.hash_size,r=e;do{i=t.head[--r],t.head[r]=i>=s?i-s:0}while(--e);e=s,r=e;do{i=t.prev[--r],t.prev[r]=i>=s?i-s:0}while(--e)};let Ct=(t,e,i)=>(e<<t.hash_shift^i)&t.hash_mask;const St=t=>{const e=t.state;let i=e.pending;i>t.avail_out&&(i=t.avail_out),0!==i&&(t.output.set(e.pending_buf.subarray(e.pending_out,e.pending_out+i),t.next_out),t.next_out+=i,e.pending_out+=i,t.total_out+=i,t.avail_out-=i,e.pending-=i,0===e.pending&&(e.pending_out=0))},Mt=(t,e)=>{Z(t,t.block_start>=0?t.block_start:-1,t.strstart-t.block_start,e),t.block_start=t.strstart,St(t.strm)},zt=(t,e)=>{t.pending_buf[t.pending++]=e},Tt=(t,e)=>{t.pending_buf[t.pending++]=e>>>8&255,t.pending_buf[t.pending++]=255&e},Et=(t,e,i,r)=>{let s=t.avail_in;return s>r&&(s=r),0===s?0:(t.avail_in-=s,e.set(t.input.subarray(t.next_in,t.next_in+s),i),1===t.state.wrap?t.adler=G(t.adler,e,s,i):2===t.state.wrap&&(t.adler=j(t.adler,e,s,i)),t.next_in+=s,t.total_in+=s,s)},Lt=(t,e)=>{let i,r,s=t.max_chain_length,n=t.strstart,a=t.prev_length,o=t.nice_match;const l=t.strstart>t.w_size-mt?t.strstart-(t.w_size-mt):0,h=t.window,c=t.w_mask,d=t.prev,u=t.strstart+gt;let f=h[n+a-1],p=h[n+a];t.prev_length>=t.good_match&&(s>>=2),o>t.lookahead&&(o=t.lookahead);do{if(i=e,h[i+a]===p&&h[i+a-1]===f&&h[i]===h[n]&&h[++i]===h[n+1]){n+=2,i++;do{}while(h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&h[++n]===h[++i]&&n<u);if(r=gt-(u-n),n=u-gt,r>a){if(t.match_start=e,a=r,r>=o)break;f=h[n+a-1],p=h[n+a]}}}while((e=d[e&c])>l&&0!=--s);return a<=t.lookahead?a:t.lookahead},Ft=t=>{const e=t.w_size;let i,r,s;do{if(r=t.window_size-t.lookahead-t.strstart,t.strstart>=e+(e-mt)&&(t.window.set(t.window.subarray(e,e+e-r),0),t.match_start-=e,t.strstart-=e,t.block_start-=e,t.insert>t.strstart&&(t.insert=t.strstart),At(t),r+=e),0===t.strm.avail_in)break;if(i=Et(t.strm,t.window,t.strstart+t.lookahead,r),t.lookahead+=i,t.lookahead+t.insert>=3)for(s=t.strstart-t.insert,t.ins_h=t.window[s],t.ins_h=Ct(t,t.ins_h,t.window[s+1]);t.insert&&(t.ins_h=Ct(t,t.ins_h,t.window[s+3-1]),t.prev[s&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=s,s++,t.insert--,!(t.lookahead+t.insert<3)););}while(t.lookahead<mt&&0!==t.strm.avail_in)},It=(t,e)=>{let i,r,s,n=t.pending_buf_size-5>t.w_size?t.w_size:t.pending_buf_size-5,a=0,o=t.strm.avail_in;do{if(i=65535,s=t.bi_valid+42>>3,t.strm.avail_out<s)break;if(s=t.strm.avail_out-s,r=t.strstart-t.block_start,i>r+t.strm.avail_in&&(i=r+t.strm.avail_in),i>s&&(i=s),i<n&&(0===i&&e!==tt||e===Q||i!==r+t.strm.avail_in))break;a=e===tt&&i===r+t.strm.avail_in?1:0,Y(t,0,0,a),t.pending_buf[t.pending-4]=i,t.pending_buf[t.pending-3]=i>>8,t.pending_buf[t.pending-2]=~i,t.pending_buf[t.pending-1]=~i>>8,St(t.strm),r&&(r>i&&(r=i),t.strm.output.set(t.window.subarray(t.block_start,t.block_start+r),t.strm.next_out),t.strm.next_out+=r,t.strm.avail_out-=r,t.strm.total_out+=r,t.block_start+=r,i-=r),i&&(Et(t.strm,t.strm.output,t.strm.next_out,i),t.strm.next_out+=i,t.strm.avail_out-=i,t.strm.total_out+=i)}while(0===a);return o-=t.strm.avail_in,o&&(o>=t.w_size?(t.matches=2,t.window.set(t.strm.input.subarray(t.strm.next_in-t.w_size,t.strm.next_in),0),t.strstart=t.w_size,t.insert=t.strstart):(t.window_size-t.strstart<=o&&(t.strstart-=t.w_size,t.window.set(t.window.subarray(t.w_size,t.w_size+t.strstart),0),t.matches<2&&t.matches++,t.insert>t.strstart&&(t.insert=t.strstart)),t.window.set(t.strm.input.subarray(t.strm.next_in-o,t.strm.next_in),t.strstart),t.strstart+=o,t.insert+=o>t.w_size-t.insert?t.w_size-t.insert:o),t.block_start=t.strstart),t.high_water<t.strstart&&(t.high_water=t.strstart),a?4:e!==Q&&e!==tt&&0===t.strm.avail_in&&t.strstart===t.block_start?2:(s=t.window_size-t.strstart,t.strm.avail_in>s&&t.block_start>=t.w_size&&(t.block_start-=t.w_size,t.strstart-=t.w_size,t.window.set(t.window.subarray(t.w_size,t.w_size+t.strstart),0),t.matches<2&&t.matches++,s+=t.w_size,t.insert>t.strstart&&(t.insert=t.strstart)),s>t.strm.avail_in&&(s=t.strm.avail_in),s&&(Et(t.strm,t.window,t.strstart,s),t.strstart+=s,t.insert+=s>t.w_size-t.insert?t.w_size-t.insert:s),t.high_water<t.strstart&&(t.high_water=t.strstart),s=t.bi_valid+42>>3,s=t.pending_buf_size-s>65535?65535:t.pending_buf_size-s,n=s>t.w_size?t.w_size:s,r=t.strstart-t.block_start,(r>=n||(r||e===tt)&&e!==Q&&0===t.strm.avail_in&&r<=s)&&(i=r>s?s:r,a=e===tt&&0===t.strm.avail_in&&i===r?1:0,Y(t,t.block_start,i,a),t.block_start+=i,St(t.strm)),a?3:1)},Ot=(t,e)=>{let i,r;for(;;){if(t.lookahead<mt){if(Ft(t),t.lookahead<mt&&e===Q)return 1;if(0===t.lookahead)break}if(i=0,t.lookahead>=3&&(t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart),0!==i&&t.strstart-i<=t.w_size-mt&&(t.match_length=Lt(t,i)),t.match_length>=3)if(r=X(t,t.strstart-t.match_start,t.match_length-3),t.lookahead-=t.match_length,t.match_length<=t.max_lazy_match&&t.lookahead>=3){t.match_length--;do{t.strstart++,t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart}while(0!=--t.match_length);t.strstart++}else t.strstart+=t.match_length,t.match_length=0,t.ins_h=t.window[t.strstart],t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+1]);else r=X(t,0,t.window[t.strstart]),t.lookahead--,t.strstart++;if(r&&(Mt(t,!1),0===t.strm.avail_out))return 1}return t.insert=t.strstart<2?t.strstart:2,e===tt?(Mt(t,!0),0===t.strm.avail_out?3:4):t.sym_next&&(Mt(t,!1),0===t.strm.avail_out)?1:2},Dt=(t,e)=>{let i,r,s;for(;;){if(t.lookahead<mt){if(Ft(t),t.lookahead<mt&&e===Q)return 1;if(0===t.lookahead)break}if(i=0,t.lookahead>=3&&(t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart),t.prev_length=t.match_length,t.prev_match=t.match_start,t.match_length=2,0!==i&&t.prev_length<t.max_lazy_match&&t.strstart-i<=t.w_size-mt&&(t.match_length=Lt(t,i),t.match_length<=5&&(t.strategy===lt||3===t.match_length&&t.strstart-t.match_start>4096)&&(t.match_length=2)),t.prev_length>=3&&t.match_length<=t.prev_length){s=t.strstart+t.lookahead-3,r=X(t,t.strstart-1-t.prev_match,t.prev_length-3),t.lookahead-=t.prev_length-1,t.prev_length-=2;do{++t.strstart<=s&&(t.ins_h=Ct(t,t.ins_h,t.window[t.strstart+3-1]),i=t.prev[t.strstart&t.w_mask]=t.head[t.ins_h],t.head[t.ins_h]=t.strstart)}while(0!=--t.prev_length);if(t.match_available=0,t.match_length=2,t.strstart++,r&&(Mt(t,!1),0===t.strm.avail_out))return 1}else if(t.match_available){if(r=X(t,0,t.window[t.strstart-1]),r&&Mt(t,!1),t.strstart++,t.lookahead--,0===t.strm.avail_out)return 1}else t.match_available=1,t.strstart++,t.lookahead--}return t.match_available&&(r=X(t,0,t.window[t.strstart-1]),t.match_available=0),t.insert=t.strstart<2?t.strstart:2,e===tt?(Mt(t,!0),0===t.strm.avail_out?3:4):t.sym_next&&(Mt(t,!1),0===t.strm.avail_out)?1:2};function kt(t,e,i,r,s){this.good_length=t,this.max_lazy=e,this.nice_length=i,this.max_chain=r,this.func=s}const Rt=[new kt(0,0,0,0,It),new kt(4,4,8,4,Ot),new kt(4,5,16,8,Ot),new kt(4,6,32,32,Ot),new kt(4,4,16,16,Dt),new kt(8,16,32,32,Dt),new kt(8,16,128,128,Dt),new kt(8,32,128,256,Dt),new kt(32,128,258,1024,Dt),new kt(32,258,258,4096,Dt)];function Pt(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=pt,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(1146),this.dyn_dtree=new Uint16Array(122),this.bl_tree=new Uint16Array(78),wt(this.dyn_ltree),wt(this.dyn_dtree),wt(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(16),this.heap=new Uint16Array(573),wt(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(573),wt(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Ut=t=>{if(!t)return 1;const e=t.state;return!e||e.strm!==t||e.status!==vt&&57!==e.status&&69!==e.status&&73!==e.status&&91!==e.status&&103!==e.status&&e.status!==_t&&e.status!==yt?1:0},Bt=t=>{if(Ut(t))return bt(t,st);t.total_in=t.total_out=0,t.data_type=ft;const e=t.state;return e.pending=0,e.pending_out=0,e.wrap<0&&(e.wrap=-e.wrap),e.status=2===e.wrap?57:e.wrap?vt:_t,t.adler=2===e.wrap?0:1,e.last_flush=-2,q(e),it},Nt=t=>{const e=Bt(t);var i;return e===it&&((i=t.state).window_size=2*i.w_size,wt(i.head),i.max_lazy_match=Rt[i.level].max_lazy,i.good_match=Rt[i.level].good_length,i.nice_match=Rt[i.level].nice_length,i.max_chain_length=Rt[i.level].max_chain,i.strstart=0,i.block_start=0,i.lookahead=0,i.insert=0,i.match_length=i.prev_length=2,i.match_available=0,i.ins_h=0),e},Gt=(t,e,i,r,s,n)=>{if(!t)return st;let a=1;if(e===ot&&(e=6),r<0?(a=0,r=-r):r>15&&(a=2,r-=16),s<1||s>9||i!==pt||r<8||r>15||e<0||e>9||n<0||n>dt||8===r&&1!==a)return bt(t,st);8===r&&(r=9);const o=new Pt;return t.state=o,o.strm=t,o.status=vt,o.wrap=a,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=s+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+3-1)/3),o.window=new Uint8Array(2*o.w_size),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<s+6,o.pending_buf_size=4*o.lit_bufsize,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=3*(o.lit_bufsize-1),o.level=e,o.strategy=n,o.method=i,Nt(t)};var Vt={deflateInit:(t,e)=>Gt(t,e,pt,15,8,ut),deflateInit2:Gt,deflateReset:Nt,deflateResetKeep:Bt,deflateSetHeader:(t,e)=>Ut(t)||2!==t.state.wrap?st:(t.state.gzhead=e,it),deflate:(t,e)=>{if(Ut(t)||e>et||e<0)return t?bt(t,st):st;const i=t.state;if(!t.output||0!==t.avail_in&&!t.input||i.status===yt&&e!==tt)return bt(t,0===t.avail_out?at:st);const r=i.last_flush;if(i.last_flush=e,0!==i.pending){if(St(t),0===t.avail_out)return i.last_flush=-1,it}else if(0===t.avail_in&&xt(e)<=xt(r)&&e!==tt)return bt(t,at);if(i.status===yt&&0!==t.avail_in)return bt(t,at);if(i.status===vt&&0===i.wrap&&(i.status=_t),i.status===vt){let e=pt+(i.w_bits-8<<4)<<8,r=-1;if(r=i.strategy>=ht||i.level<2?0:i.level<6?1:6===i.level?2:3,e|=r<<6,0!==i.strstart&&(e|=32),e+=31-e%31,Tt(i,e),0!==i.strstart&&(Tt(i,t.adler>>>16),Tt(i,65535&t.adler)),t.adler=1,i.status=_t,St(t),0!==i.pending)return i.last_flush=-1,it}if(57===i.status)if(t.adler=0,zt(i,31),zt(i,139),zt(i,8),i.gzhead)zt(i,(i.gzhead.text?1:0)+(i.gzhead.hcrc?2:0)+(i.gzhead.extra?4:0)+(i.gzhead.name?8:0)+(i.gzhead.comment?16:0)),zt(i,255&i.gzhead.time),zt(i,i.gzhead.time>>8&255),zt(i,i.gzhead.time>>16&255),zt(i,i.gzhead.time>>24&255),zt(i,9===i.level?2:i.strategy>=ht||i.level<2?4:0),zt(i,255&i.gzhead.os),i.gzhead.extra&&i.gzhead.extra.length&&(zt(i,255&i.gzhead.extra.length),zt(i,i.gzhead.extra.length>>8&255)),i.gzhead.hcrc&&(t.adler=j(t.adler,i.pending_buf,i.pending,0)),i.gzindex=0,i.status=69;else if(zt(i,0),zt(i,0),zt(i,0),zt(i,0),zt(i,0),zt(i,9===i.level?2:i.strategy>=ht||i.level<2?4:0),zt(i,3),i.status=_t,St(t),0!==i.pending)return i.last_flush=-1,it;if(69===i.status){if(i.gzhead.extra){let e=i.pending,r=(65535&i.gzhead.extra.length)-i.gzindex;for(;i.pending+r>i.pending_buf_size;){let s=i.pending_buf_size-i.pending;if(i.pending_buf.set(i.gzhead.extra.subarray(i.gzindex,i.gzindex+s),i.pending),i.pending=i.pending_buf_size,i.gzhead.hcrc&&i.pending>e&&(t.adler=j(t.adler,i.pending_buf,i.pending-e,e)),i.gzindex+=s,St(t),0!==i.pending)return i.last_flush=-1,it;e=0,r-=s}let s=new Uint8Array(i.gzhead.extra);i.pending_buf.set(s.subarray(i.gzindex,i.gzindex+r),i.pending),i.pending+=r,i.gzhead.hcrc&&i.pending>e&&(t.adler=j(t.adler,i.pending_buf,i.pending-e,e)),i.gzindex=0}i.status=73}if(73===i.status){if(i.gzhead.name){let e,r=i.pending;do{if(i.pending===i.pending_buf_size){if(i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r)),St(t),0!==i.pending)return i.last_flush=-1,it;r=0}e=i.gzindex<i.gzhead.name.length?255&i.gzhead.name.charCodeAt(i.gzindex++):0,zt(i,e)}while(0!==e);i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r)),i.gzindex=0}i.status=91}if(91===i.status){if(i.gzhead.comment){let e,r=i.pending;do{if(i.pending===i.pending_buf_size){if(i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r)),St(t),0!==i.pending)return i.last_flush=-1,it;r=0}e=i.gzindex<i.gzhead.comment.length?255&i.gzhead.comment.charCodeAt(i.gzindex++):0,zt(i,e)}while(0!==e);i.gzhead.hcrc&&i.pending>r&&(t.adler=j(t.adler,i.pending_buf,i.pending-r,r))}i.status=103}if(103===i.status){if(i.gzhead.hcrc){if(i.pending+2>i.pending_buf_size&&(St(t),0!==i.pending))return i.last_flush=-1,it;zt(i,255&t.adler),zt(i,t.adler>>8&255),t.adler=0}if(i.status=_t,St(t),0!==i.pending)return i.last_flush=-1,it}if(0!==t.avail_in||0!==i.lookahead||e!==Q&&i.status!==yt){let r=0===i.level?It(i,e):i.strategy===ht?((t,e)=>{let i;for(;;){if(0===t.lookahead&&(Ft(t),0===t.lookahead)){if(e===Q)return 1;break}if(t.match_length=0,i=X(t,0,t.window[t.strstart]),t.lookahead--,t.strstart++,i&&(Mt(t,!1),0===t.strm.avail_out))return 1}return 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Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]);if(Ge(t)||!t.output||!t.input&&0!==t.avail_in)return Se;i=t.state,i.mode===Ie&&(i.mode=Oe),a=t.next_out,s=t.output,l=t.avail_out,n=t.next_in,r=t.input,o=t.avail_in,h=i.hold,c=i.bits,d=o,u=l,A=we;t:for(;;)switch(i.mode){case Le:if(0===i.wrap){i.mode=Oe;break}for(;c<16;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(2&i.wrap&&35615===h){0===i.wbits&&(i.wbits=15),i.check=0,S[0]=255&h,S[1]=h>>>8&255,i.check=j(i.check,S,2,0),h=0,c=0,i.mode=16181;break}if(i.head&&(i.head.done=!1),!(1&i.wrap)||(((255&h)<<8)+(h>>8))%31){t.msg="incorrect header check",i.mode=Ue;break}if((15&h)!==Ee){t.msg="unknown compression method",i.mode=Ue;break}if(h>>>=4,c-=4,w=8+(15&h),0===i.wbits&&(i.wbits=w),w>15||w>i.wbits){t.msg="invalid window size",i.mode=Ue;break}i.dmax=1<<i.wbits,i.flags=0,t.adler=i.check=1,i.mode=512&h?16189:Ie,h=0,c=0;break;case 16181:for(;c<16;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(i.flags=h,(255&i.flags)!==Ee){t.msg="unknown 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t;s.set(r.subarray(n,n+f),a),o-=f,n+=f,l-=f,a+=f,i.length-=f;break}i.mode=Ie;break;case 16196:for(;c<14;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(i.nlen=257+(31&h),h>>>=5,c-=5,i.ndist=1+(31&h),h>>>=5,c-=5,i.ncode=4+(15&h),h>>>=4,c-=4,i.nlen>286||i.ndist>30){t.msg="too many length or distance symbols",i.mode=Ue;break}i.have=0,i.mode=16197;case 16197:for(;i.have<i.ncode;){for(;c<3;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}i.lens[T[i.have++]]=7&h,h>>>=3,c-=3}for(;i.have<19;)i.lens[T[i.have++]]=0;if(i.lencode=i.lendyn,i.lenbits=7,M={bits:i.lenbits},A=_e(0,i.lens,0,19,i.lencode,0,i.work,M),i.lenbits=M.bits,A){t.msg="invalid code lengths set",i.mode=Ue;break}i.have=0,i.mode=16198;case 16198:for(;i.have<i.nlen+i.ndist;){for(;C=i.lencode[h&(1<<i.lenbits)-1],m=C>>>24,v=C>>>16&255,_=65535&C,!(m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(_<16)h>>>=m,c-=m,i.lens[i.have++]=_;else{if(16===_){for(z=m+2;c<z;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(h>>>=m,c-=m,0===i.have){t.msg="invalid bit 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Re:if(o>=6&&l>=258){t.next_out=a,t.avail_out=l,t.next_in=n,t.avail_in=o,i.hold=h,i.bits=c,ue(t,u),a=t.next_out,s=t.output,l=t.avail_out,n=t.next_in,r=t.input,o=t.avail_in,h=i.hold,c=i.bits,i.mode===Ie&&(i.back=-1);break}for(i.back=0;C=i.lencode[h&(1<<i.lenbits)-1],m=C>>>24,v=C>>>16&255,_=65535&C,!(m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(v&&!(240&v)){for(y=m,b=v,x=_;C=i.lencode[x+((h&(1<<y+b)-1)>>y)],m=C>>>24,v=C>>>16&255,_=65535&C,!(y+m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}h>>>=y,c-=y,i.back+=y}if(h>>>=m,c-=m,i.back+=m,i.length=_,0===v){i.mode=16205;break}if(32&v){i.back=-1,i.mode=Ie;break}if(64&v){t.msg="invalid literal/length code",i.mode=Ue;break}i.extra=15&v,i.mode=16201;case 16201:if(i.extra){for(z=i.extra;c<z;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}i.length+=h&(1<<i.extra)-1,h>>>=i.extra,c-=i.extra,i.back+=i.extra}i.was=i.length,i.mode=16202;case 16202:for(;C=i.distcode[h&(1<<i.distbits)-1],m=C>>>24,v=C>>>16&255,_=65535&C,!(m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(!(240&v)){for(y=m,b=v,x=_;C=i.distcode[x+((h&(1<<y+b)-1)>>y)],m=C>>>24,v=C>>>16&255,_=65535&C,!(y+m<=c);){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}h>>>=y,c-=y,i.back+=y}if(h>>>=m,c-=m,i.back+=m,64&v){t.msg="invalid distance code",i.mode=Ue;break}i.offset=_,i.extra=15&v,i.mode=16203;case 16203:if(i.extra){for(z=i.extra;c<z;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}i.offset+=h&(1<<i.extra)-1,h>>>=i.extra,c-=i.extra,i.back+=i.extra}if(i.offset>i.dmax){t.msg="invalid distance too far back",i.mode=Ue;break}i.mode=16204;case 16204:if(0===l)break t;if(f=u-l,i.offset>f){if(f=i.offset-f,f>i.whave&&i.sane){t.msg="invalid distance too far back",i.mode=Ue;break}f>i.wnext?(f-=i.wnext,p=i.wsize-f):p=i.wnext-f,f>i.length&&(f=i.length),g=i.window}else g=s,p=a-i.offset,f=i.length;f>l&&(f=l),l-=f,i.length-=f;do{s[a++]=g[p++]}while(--f);0===i.length&&(i.mode=Re);break;case 16205:if(0===l)break t;s[a++]=i.length,l--,i.mode=Re;break;case Pe:if(i.wrap){for(;c<32;){if(0===o)break t;o--,h|=r[n++]<<c,c+=8}if(u-=l,t.total_out+=u,i.total+=u,4&i.wrap&&u&&(t.adler=i.check=i.flags?j(i.check,s,u,a-u):G(i.check,s,u,a-u)),u=l,4&i.wrap&&(i.flags?h:Be(h))!==i.check){t.msg="incorrect data check",i.mode=Ue;break}h=0,c=0}i.mode=16207;case 16207:if(i.wrap&&i.flags){for(;c<32;){if(0===o)break t;o--,h+=r[n++]<<c,c+=8}if(4&i.wrap&&h!==(4294967295&i.total)){t.msg="incorrect length check",i.mode=Ue;break}h=0,c=0}i.mode=16208;case 16208:A=Ae;break t;case Ue:A=Me;break t;case 16210:return ze;default:return Se}return 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$e=function(){this.text=0,this.time=0,this.xflags=0,this.os=0,this.extra=null,this.extra_len=0,this.name="",this.comment="",this.hcrc=0,this.done=!1};const Je=Object.prototype.toString,{Z_NO_FLUSH:ti,Z_FINISH:ei,Z_OK:ii,Z_STREAM_END:ri,Z_NEED_DICT:si,Z_STREAM_ERROR:ni,Z_DATA_ERROR:ai,Z_MEM_ERROR:oi}=W;function li(t){this.options=Ht({chunkSize:65536,windowBits:15,to:""},t||{});const e=this.options;e.raw&&e.windowBits>=0&&e.windowBits<16&&(e.windowBits=-e.windowBits,0===e.windowBits&&(e.windowBits=-15)),!(e.windowBits>=0&&e.windowBits<16)||t&&t.windowBits||(e.windowBits+=32),e.windowBits>15&&e.windowBits<48&&(15&e.windowBits||(e.windowBits|=15)),this.err=0,this.msg="",this.ended=!1,this.chunks=[],this.strm=new Qt,this.strm.avail_out=0;let i=Qe.inflateInit2(this.strm,e.windowBits);if(i!==ii)throw new Error(H[i]);if(this.header=new $e,Qe.inflateGetHeader(this.strm,this.header),e.dictionary&&("string"==typeof e.dictionary?e.dictionary=Zt(e.dictionary):"[object 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t=Kt(i.output,i.next_out),e=i.next_out-t,s=Xt(i.output,t);i.next_out=e,i.avail_out=r-e,e&&i.output.set(i.output.subarray(t,t+e),0),this.onData(s)}else this.onData(i.output.length===i.next_out?i.output:i.output.subarray(0,i.next_out));if(n!==ii||0!==o){if(n===ri)return n=Qe.inflateEnd(this.strm),this.onEnd(n),this.ended=!0,!0;if(0===i.avail_in)break}}return!0},li.prototype.onData=function(t){this.chunks.push(t)},li.prototype.onEnd=function(t){t===ii&&("string"===this.options.to?this.result=this.chunks.join(""):this.result=Wt(this.chunks)),this.chunks=[],this.err=t,this.msg=this.strm.msg};var ci={Inflate:li,inflate:hi,inflateRaw:function(t,e){return(e=e||{}).raw=!0,hi(t,e)},ungzip:hi,constants:W};const{Deflate:di,deflate:ui,deflateRaw:fi,gzip:pi}=ce,{Inflate:gi,inflate:mi,inflateRaw:vi,ungzip:_i}=ci;var yi=di,bi=ui,xi=fi,wi=pi,Ai=gi,Ci=mi,Si=vi,Mi=_i,zi=W,Ti={Deflate:di,deflate:ui,deflateRaw:fi,gzip:pi,Inflate:gi,inflate:mi,inflateRaw:vi,ungzip:_i,constants:W}}},__webpack_module_cache__={};function __webpack_require__(t){var e=__webpack_module_cache__[t];if(void 0!==e)return e.exports;var i=__webpack_module_cache__[t]={exports:{}};return __webpack_modules__[t].call(i.exports,i,i.exports,__webpack_require__),i.exports}__webpack_require__.d=(t,e)=>{for(var i in e)__webpack_require__.o(e,i)&&!__webpack_require__.o(t,i)&&Object.defineProperty(t,i,{enumerable:!0,get:e[i]})},__webpack_require__.o=(t,e)=>Object.prototype.hasOwnProperty.call(t,e),__webpack_require__.r=t=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},__webpack_require__(421),__webpack_require__(185);var __webpack_exports__=__webpack_require__(307);return __webpack_exports__})()));
\ No newline at end of file
diff --git a/assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt b/assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt
new file mode 100644
index 0000000..1b2a169
--- /dev/null
+++ b/assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt
@@ -0,0 +1,90 @@
+3Dmol.js incorporates code from GLmol, Three.js, and jQuery and
+is licensed under a BSD-3-Clause license.
+
+* 3Dmol.js
+	Copyright (c) 2014, University of Pittsburgh and contributors
+	All rights reserved.
+
+	Redistribution and use in source and binary forms, with or without 
+	modification, are permitted provided that the following conditions are met:
+
+	1. Redistributions of source code must retain the above copyright 
+	notice, this list of conditions and the following disclaimer.
+
+	2. Redistributions in binary form must reproduce the above copyright 
+	notice, this list of conditions and the following disclaimer in the 
+	documentation and/or other materials provided with the distribution.
+
+	3. Neither the name of the copyright holder nor the names of its 
+	contributors may be used to endorse or promote products derived from 
+	this software without specific prior written permission.
+
+	THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
+	"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
+	LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A 
+	PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+	HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
+	SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 
+	TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 
+	PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 
+	LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 
+	NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 
+	SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+* GLmol
+ GLmol - Molecular Viewer on WebGL/Javascript (0.47)
+  (C) Copyright 2011-2012, biochem_fan
+      License: dual license of MIT or LGPL3
+
+  Contributors:
+    Robert Hanson for parseXYZ, deferred instantiation
+
+
+
+* Three.js 
+     https://github.com/mrdoob/three.js
+
+     Copyright (c) 2010-2012 three.js Authors. All rights reserved.
+
+     Permission is hereby granted, free of charge, to any person obtaining a copy
+     of this software and associated documentation files (the "Software"), to deal
+     in the Software without restriction, including without limitation the rights
+     to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+     copies of the Software, and to permit persons to whom the Software is
+     furnished to do so, subject to the following conditions:
+
+     The above copyright notice and this permission notice shall be included in
+     all copies or substantial portions of the Software.
+
+     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+     IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+     FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+     AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+     LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+     OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+     THE SOFTWARE.
+
+* jQuery
+     http://jquery.org/
+
+     Copyright (c) 2011 John Resig
+
+     Permission is hereby granted, free of charge, to any person obtaining
+     a copy of this software and associated documentation files (the
+     "Software"), to deal in the Software without restriction, including
+     without limitation the rights to use, copy, modify, merge, publish,
+     distribute, sublicense, and/or sell copies of the Software, and to
+     permit persons to whom the Software is furnished to do so, subject to
+     the following conditions:
+
+     The above copyright notice and this permission notice shall be
+     included in all copies or substantial portions of the Software.
+
+     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+     EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+     MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+     NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+     LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+     OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+     WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
diff --git a/assets/variant_effect_inspection_tool/viewer_template.html b/assets/variant_effect_inspection_tool/viewer_template.html
new file mode 100644
index 0000000..c1e4f5a
--- /dev/null
+++ b/assets/variant_effect_inspection_tool/viewer_template.html
@@ -0,0 +1,907 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <!--
+  Generated by nf-core/deepmutscan (MIT). This viewer bundles 3Dmol.js (https://3dmol.org) —
+  Copyright (c) 2014 University of Pittsburgh and contributors, licensed BSD-3-Clause. The full
+  license text is distributed with the pipeline as assets/variant_effect_inspection_tool/3Dmol-min.js.LICENSE.txt.
+-->
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <title>nf-core/deepmutscan · variant effect inspection tool · __SAMPLE__</title>
+    <style>
+      :root {
+        --bg: #ffffff;
+        --panel: #f7f8fa;
+        --line: #e6e8ec;
+        --ink: #1c2230;
+        --muted: #6b7280;
+        --accent: #2f6df0;
+        --na: #dddddd;
+        --shadow: 0 1px 2px rgba(16, 24, 40, 0.06), 0 1px 3px rgba(16, 24, 40, 0.1);
+        --mono: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
+        --sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
+      }
+      * {
+        box-sizing: border-box;
+      }
+      html,
+      body {
+        margin: 0;
+        height: 100%;
+      }
+      body {
+        font-family: var(--sans);
+        color: var(--ink);
+        background: var(--bg);
+        font-size: 14px;
+      }
+      header {
+        display: flex;
+        align-items: center;
+        gap: 14px;
+        padding: 14px 22px;
+        border-bottom: 1px solid var(--line);
+      }
+      header .logo {
+        height: 44px;
+        width: auto;
+        display: block;
+      }
+      header .title {
+        color: var(--muted);
+        font-size: 18px;
+        font-weight: 500;
+        border-left: 1px solid var(--line);
+        padding-left: 14px;
+      }
+      header .spacer {
+        flex: 1;
+      }
+      header .sub {
+        color: var(--muted);
+        font-size: 14px;
+      }
+      header .sub strong {
+        color: var(--ink);
+      }
+      .layout {
+        display: grid;
+        grid-template-columns: 288px 1fr 340px;
+        gap: 0;
+        height: calc(100vh - 74px - 92px);
+      }
+      .sidebar,
+      .detail {
+        padding: 16px;
+        overflow: auto;
+      }
+      .sidebar {
+        border-right: 1px solid var(--line);
+      }
+      .detail {
+        border-left: 1px solid var(--line);
+        background: var(--panel);
+      }
+      #stage {
+        position: relative;
+        background: radial-gradient(120% 120% at 50% 0%, #fbfcfe 0%, #f1f3f7 100%);
+      }
+      #viewer {
+        position: absolute;
+        inset: 0;
+      }
+      .group {
+        margin-bottom: 18px;
+      }
+      .group h3 {
+        font-size: 11px;
+        text-transform: uppercase;
+        letter-spacing: 0.08em;
+        color: var(--muted);
+        margin: 0 0 8px;
+      }
+      label.field {
+        display: block;
+        margin-bottom: 10px;
+      }
+      label.field span {
+        display: block;
+        font-size: 12px;
+        color: var(--muted);
+        margin-bottom: 4px;
+      }
+      select,
+      button {
+        font-family: inherit;
+        font-size: 13px;
+        color: var(--ink);
+      }
+      select {
+        width: 100%;
+        padding: 7px 9px;
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        background: #fff;
+      }
+      .btnrow {
+        display: flex;
+        gap: 8px;
+        flex-wrap: wrap;
+      }
+      button {
+        padding: 7px 11px;
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        background: #fff;
+        cursor: pointer;
+        box-shadow: var(--shadow);
+      }
+      button:hover {
+        border-color: #cdd2da;
+      }
+      button.primary {
+        background: var(--accent);
+        border-color: var(--accent);
+        color: #fff;
+      }
+      button.primary:hover {
+        filter: brightness(0.96);
+      }
+      button.on {
+        background: var(--accent);
+        border-color: var(--accent);
+        color: #fff;
+      }
+      .legend {
+        margin-top: 6px;
+      }
+      .legend .bar {
+        height: 12px;
+        border-radius: 6px;
+        border: 1px solid var(--line);
+      }
+      .legend .ticks {
+        display: flex;
+        justify-content: space-between;
+        font-variant-numeric: tabular-nums;
+        color: var(--muted);
+        font-size: 11px;
+        margin-top: 4px;
+      }
+      .legend .desc {
+        color: var(--muted);
+        font-size: 12px;
+        margin-top: 8px;
+        line-height: 1.45;
+      }
+      .legend .na {
+        display: inline-flex;
+        align-items: center;
+        gap: 6px;
+        margin-top: 8px;
+        font-size: 12px;
+        color: var(--muted);
+      }
+      .swatch {
+        width: 12px;
+        height: 12px;
+        border-radius: 3px;
+        border: 1px solid var(--line);
+        display: inline-block;
+      }
+      .detail h2 {
+        font-size: 14px;
+        margin: 0 0 2px;
+      }
+      .detail .pos-sub {
+        color: var(--muted);
+        font-size: 12px;
+        margin-bottom: 12px;
+      }
+      .metrics {
+        display: grid;
+        grid-template-columns: 1fr auto;
+        gap: 6px 10px;
+        margin-bottom: 14px;
+      }
+      .metrics .k {
+        color: var(--muted);
+        font-size: 12px;
+      }
+      .metrics .v {
+        font-variant-numeric: tabular-nums;
+        font-size: 12px;
+      }
+      .subhead {
+        font-size: 11px;
+        text-transform: uppercase;
+        letter-spacing: 0.08em;
+        color: var(--muted);
+        margin: 4px 0 8px;
+      }
+      .vbar {
+        display: grid;
+        grid-template-columns: 22px 1fr 52px;
+        align-items: center;
+        gap: 8px;
+        margin: 1px 0;
+        padding: 2px 3px;
+        border-radius: 6px;
+        cursor: pointer;
+      }
+      .vbar:hover {
+        background: #eef0f4;
+      }
+      .vbar.sel {
+        background: #e8efff;
+        box-shadow: inset 0 0 0 1px var(--accent);
+      }
+      .vbar .aa {
+        font-family: var(--mono);
+        font-weight: 600;
+        text-align: center;
+      }
+      .vbar .track {
+        height: 14px;
+        background: #eef0f4;
+        border-radius: 4px;
+        position: relative;
+        overflow: hidden;
+      }
+      .vbar.sel .track {
+        background: #dbe5ff;
+      }
+      .vbar .fill {
+        position: absolute;
+        top: 0;
+        bottom: 0;
+      }
+      .vbar .num {
+        font-variant-numeric: tabular-nums;
+        font-size: 11px;
+        color: var(--muted);
+        text-align: right;
+      }
+      .empty {
+        color: var(--muted);
+        font-size: 13px;
+        line-height: 1.5;
+      }
+      .swarm {
+        margin-top: 14px;
+        border-top: 1px solid var(--line);
+        padding-top: 12px;
+      }
+      .swarm .shead {
+        display: flex;
+        justify-content: space-between;
+        align-items: baseline;
+        margin-bottom: 8px;
+      }
+      .swarm .shead b {
+        font-size: 12px;
+      }
+      .swarm .plot {
+        margin-bottom: 12px;
+      }
+      .swarm .plot .pt {
+        font-size: 11px;
+        color: var(--ink);
+        margin-bottom: 2px;
+        display: flex;
+        justify-content: space-between;
+      }
+      .swarm .plot .pt .val {
+        font-variant-numeric: tabular-nums;
+        color: var(--muted);
+      }
+      .swarm .plot canvas {
+        display: block;
+        width: 100%;
+        height: 96px;
+        border-radius: 6px;
+        background: #fff;
+        border: 1px solid var(--line);
+      }
+      .swarm .plot .ax {
+        display: flex;
+        justify-content: space-between;
+        font-size: 10px;
+        color: var(--muted);
+        font-variant-numeric: tabular-nums;
+        margin-top: 2px;
+      }
+      .ibtn {
+        display: inline-flex;
+        align-items: center;
+        justify-content: center;
+        width: 14px;
+        height: 14px;
+        border-radius: 50%;
+        border: 1px solid var(--line);
+        background: transparent;
+        color: var(--muted);
+        font:
+          italic 700 10px Georgia,
+          serif;
+        cursor: pointer;
+        padding: 0;
+        line-height: 1;
+        vertical-align: middle;
+        margin-left: 4px;
+      }
+      .ibtn:hover {
+        color: var(--ink);
+        border-color: var(--muted);
+      }
+      .ipop {
+        position: fixed;
+        z-index: 40;
+        max-width: 250px;
+        background: var(--panel);
+        color: var(--ink);
+        border: 1px solid var(--line);
+        border-radius: 8px;
+        padding: 8px 10px;
+        font-size: 12px;
+        line-height: 1.45;
+        box-shadow: 0 6px 22px rgba(0, 0, 0, 0.2);
+      }
+      .hint {
+        color: var(--muted);
+        font-size: 11px;
+        margin-top: 6px;
+      }
+      .reps {
+        display: flex;
+        flex-direction: column;
+        gap: 5px;
+      }
+      .reps label {
+        display: flex;
+        align-items: center;
+        gap: 7px;
+        font-size: 12.5px;
+        color: var(--ink2);
+        cursor: pointer;
+      }
+      .reps label:hover {
+        color: var(--ink);
+      }
+      .reps input {
+        accent-color: var(--accent);
+        margin: 0;
+      }
+      .seqwrap {
+        border-top: 1px solid var(--line);
+        height: 92px;
+        padding: 8px 14px;
+        overflow-x: auto;
+        overflow-y: hidden;
+        white-space: nowrap;
+        background: #fff;
+      }
+      .seqwrap .ruler {
+        height: 14px;
+        position: relative;
+        font-size: 10px;
+        color: var(--muted);
+        font-family: var(--mono);
+      }
+      .seqwrap .ruler span {
+        position: absolute;
+        transform: translateX(-50%);
+      }
+      .seq {
+        font-family: var(--mono);
+        line-height: 1;
+        user-select: none;
+      }
+      .seq .res {
+        display: inline-block;
+        width: 13px;
+        height: 20px;
+        line-height: 20px;
+        text-align: center;
+        border-radius: 3px;
+        color: #11151f;
+        cursor: pointer;
+        font-size: 12px;
+      }
+      .seq .res.sel {
+        outline: 2px solid var(--ink);
+        outline-offset: -1px;
+      }
+      .seq .res.unmapped {
+        opacity: 0.45;
+      }
+    </style>
+  </head>
+  <body>
+    <header>
+      <a
+        href="https://nf-co.re/deepmutscan"
+        target="_blank"
+        rel="noopener noreferrer"
+        aria-label="nf-core/deepmutscan"
+        style="display: inline-block; line-height: 0"
+        ><img
+          class="logo"
+          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"
+          alt="nf-core/deepmutscan"
+      /></a>
+      <span class="title">variant effect inspection tool</span>
+      <span class="spacer"></span>
+      <span class="sub" id="sample-tag"></span>
+    </header>
+
+    <div class="layout">
+      <aside class="sidebar">
+        <div class="group">
+          <h3>Display</h3>
+          <label class="field"
+            ><span>Color residues by</span>
+            <select id="metric"></select
+          ></label>
+          <label class="field"
+            ><span>Representation</span>
+            <select id="rep">
+              <option value="cartoon">Cartoon</option>
+              <option value="surface">Surface</option>
+              <option value="stick">Sticks</option>
+              <option value="sphere">Spheres</option>
+              <option value="line">Lines</option>
+            </select></label
+          >
+          <div class="btnrow">
+            <button id="spin">Spin</button>
+            <button id="reset">Reset view</button>
+            <button id="clearsel">Clear selection</button>
+          </div>
+        </div>
+        <div class="group" id="repgroup" style="display: none">
+          <h3>
+            Replicates
+            <button
+              class="ibtn"
+              type="button"
+              data-desc="Include or exclude individual sequencing replicates to see how much each one drives the fitness estimate. Mean fitness and its SD are recomputed over the selected replicates only. This is exact, not an approximation: each replicate's fitness is derived from its own input/output counts and its own synonymous/stop anchors, so its value does not depend on which other replicates were in the run. The colour scale stays fixed so values remain comparable across selections. DiMSum and mutscan fit their own models across replicates and are not affected by this control."
+            >
+              i
+            </button>
+          </h3>
+          <div class="reps" id="reps"></div>
+          <div class="hint" id="rephint"></div>
+        </div>
+        <div class="group">
+          <h3>Legend</h3>
+          <div class="legend">
+            <div class="bar" id="legbar"></div>
+            <div class="ticks"><span id="legmin"></span><span id="legmid"></span><span id="legmax"></span></div>
+            <div class="desc" id="legdesc"></div>
+            <div class="na"><span class="swatch" style="background: var(--na)"></span> no data / unmapped residue</div>
+          </div>
+        </div>
+        <div class="group">
+          <h3>Export</h3>
+          <div class="btnrow">
+            <button class="primary" id="png">Download high-res PNG</button>
+            <button id="pymol">Download PyMOL (.pml)</button>
+          </div>
+          <div class="hint">
+            PNG renders the current 3D view at 2400&nbsp;px on a white background. The PyMOL script reproduces the
+            current coloring on the structure.
+          </div>
+        </div>
+      </aside>
+
+      <div id="stage"><div id="viewer"></div></div>
+
+      <aside class="detail" id="detail">
+        <div class="empty" id="detail-empty">
+          Click a residue — in the structure or the sequence below — to see every substitution's effect at that
+          position. Click several residues to compare; click a selected one again to deselect.
+        </div>
+        <div id="detail-body" style="display: none"></div>
+      </aside>
+    </div>
+
+    <div class="seqwrap">
+      <div class="ruler" id="ruler"></div>
+      <div class="seq" id="seq"></div>
+    </div>
+
+    <script>
+      /*__THREEDMOL_JS__*/
+    </script>
+    <script>
+      const DATA = /*__DATA_JSON__*/;
+      const PDB  = "__PDB_TEXT__";
+      const GREY = 0xdddddd;
+      const AA = "ACDEFGHIKLMNPQRSTVWY";
+      const METRIC_KEYS = Object.keys(DATA.metrics);
+
+      /* ---------- colour scales ---------- */
+      function lerp(a,b,t){return a+(b-a)*t}
+      function hex2rgb(h){return [(h>>16)&255,(h>>8)&255,h&255]}
+      function rgb2int(r,g,b){return (Math.round(r)<<16)|(Math.round(g)<<8)|Math.round(b)}
+      function rampColor(stops,t){
+        t=Math.max(0,Math.min(1,t));
+        const n=stops.length-1, x=t*n, i=Math.min(n-1,Math.floor(x)), f=x-i;
+        const a=hex2rgb(stops[i]), b=hex2rgb(stops[i+1]);
+        return rgb2int(lerp(a[0],b[0],f),lerp(a[1],b[1],f),lerp(a[2],b[2],f));
+      }
+      const VIRIDIS=[0x440154,0x414487,0x2a788e,0x22a884,0x7ad151,0xfde725];
+      const COOLWARM=[0x3a4cc0,0x6f8fe8,0xc3cfe2,0xf2f2f2,0xf2c0a4,0xe26a52,0xb40426];
+      function plddtColor(v){ if(v>=90)return 0x0053d6; if(v>=70)return 0x65cbf3; if(v>=50)return 0xffdb13; return 0xff7d45; }
+      function metricColor(metric,v){
+        if(v===null||v===undefined)return GREY;
+        const m=DATA.metrics[metric], lo=m.range[0], hi=m.range[1];
+        if(m.type==='plddt')return plddtColor(v);
+        let t=(v-lo)/((hi-lo)||1);
+        if(m.reverse) t=1-t;                       // e.g. fitness: low -> red, high -> blue
+        return m.type==='diverging'?rampColor(COOLWARM,t):rampColor(VIRIDIS,t);
+      }
+      function cssColor(intc){return '#'+intc.toString(16).padStart(6,'0')}
+      function textOn(intc){ const c=hex2rgb(intc); return (0.299*c[0]+0.587*c[1]+0.114*c[2])/255 < 0.55 ? '#fff' : '#11151f'; }
+
+      /* ---------- state ---------- */
+      let METRIC=METRIC_KEYS[0];
+      let REP='cartoon';
+      const selected=new Set();      // positions currently selected in 3D
+      let activePos=null;            // residue whose detail panel is shown
+      let activeVariant=null;        // {pos,mut} whose bee-swarm plots are shown
+      let resiColor={};              // structure resnum -> int colour
+      let initialView=null;
+      const byResnum={}, byPos={};
+      DATA.residues.forEach(r=>{byPos[r.pos]=r; if(r.resnum!=null)byResnum[r.resnum]=r;});
+
+      /* ---------- replicate selection ----------
+         The default fitness estimation never pools replicates: rescaled_fitness_rep<N> comes from
+         that replicate's own counts and its own synonymous/stop anchors, and `mean fitness`/`fitness sd`
+         are just the row-wise mean/SD across those columns. So recomputing them over a subset here is
+         exactly what a pipeline rerun without the excluded replicates would produce. */
+      const REPS=DATA.replicates||[];
+      const selReps=new Set(REPS.map((_,i)=>i));
+
+      // mirrors R's mean(x, na.rm=TRUE) / sd(x, na.rm=TRUE): NA dropped, sample SD, SD undefined for n<2
+      function meanSd(vals){
+        const v=vals.filter(x=>x!=null&&!isNaN(x));
+        if(!v.length) return {mean:null, sd:null};
+        const m=v.reduce((a,b)=>a+b,0)/v.length;
+        if(v.length<2) return {mean:m, sd:null};
+        const s=Math.sqrt(v.reduce((a,b)=>a+(b-m)*(b-m),0)/(v.length-1));
+        return {mean:m, sd:s};
+      }
+
+      // re-derive every fitness-dependent metric for the current replicate subset
+      function recomputeFitness(){
+        if(!REPS.length) return;
+        const sel=[...selReps];
+        DATA.residues.forEach(r=>{
+          const fits=[], sds=[];
+          r.variants.forEach(v=>{
+            if(!v.reps) return;
+            const ms=meanSd(sel.map(i=>v.reps[i]));
+            if('fitness' in v.metrics) v.metrics.fitness=ms.mean;
+            if('fitness_sd' in v.metrics) v.metrics.fitness_sd=ms.sd;
+            if(!v.syn && !v.stop && ms.mean!=null){ fits.push(ms.mean); if(ms.sd!=null) sds.push(ms.sd); }
+          });
+          const mean=a=>a.length?a.reduce((x,y)=>x+y,0)/a.length:null;
+          if('fitness' in r.metrics) r.metrics.fitness=mean(fits);
+          if('fitness_sd' in r.metrics) r.metrics.fitness_sd=mean(sds);
+          if('n_substitutions' in r.metrics) r.metrics.n_substitutions=fits.length||null;
+        });
+      }
+
+      function renderReps(){
+        if(REPS.length<2) return;                       // nothing to choose from
+        const host=document.getElementById('reps');
+        host.innerHTML=REPS.map((id,i)=>
+          `<label><input type="checkbox" data-i="${i}"${selReps.has(i)?' checked':''}>replicate ${id}</label>`
+        ).join('');
+        const n=selReps.size;
+        document.getElementById('rephint').textContent =
+          n===0 ? 'no replicates selected — fitness has no data, the structure is grey'
+          : n===REPS.length ? `all ${n} replicates — mean fitness as computed by the pipeline`
+          : n===1 ? 'single replicate — SD is undefined and shown as no data'
+          : `${n} of ${REPS.length} replicates — mean and SD recomputed`;
+      }
+
+      function buildColorMap(){
+        resiColor={};
+        DATA.residues.forEach(r=>{ if(r.resnum!=null) resiColor[r.resnum]=metricColor(METRIC,r.metrics[METRIC]); });
+      }
+
+      /* ---------- 3Dmol ---------- */
+      const viewer=$3Dmol.createViewer(document.getElementById('viewer'),{backgroundColor:'white'});
+      viewer.addModel(PDB,'pdb');
+      const colorfunc=atom=>(atom.resi in resiColor?resiColor[atom.resi]:GREY);
+
+      function applyStyle(){
+        buildColorMap();
+        viewer.removeAllSurfaces();
+        viewer.removeAllLabels();
+        viewer.setStyle({},{});
+        if(REP==='surface'){
+          // 3Dmol's click picker only tests sphere/stick/line geometry, never the surface mesh — so a lone
+          // surface is not clickable. Render small clickable spheres at the atom centres (hidden inside the
+          // opaque surface): a click passes through the non-pickable surface and hits the sphere behind it.
+          viewer.setStyle({},{sphere:{colorfunc:colorfunc,radius:1.2}});
+          viewer.addSurface($3Dmol.SurfaceType.VDW,{colorfunc:colorfunc,opacity:1.0});
+        }else{
+          const s={};
+          if(REP==='cartoon')s.cartoon={colorfunc:colorfunc};
+          else if(REP==='stick')s.stick={colorfunc:colorfunc,radius:0.22};
+          else if(REP==='sphere')s.sphere={colorfunc:colorfunc,scale:0.32};
+          else s.line={colorfunc:colorfunc};
+          viewer.setStyle({},s);
+        }
+        // highlight every selected residue: sticks + a label
+        selected.forEach(pos=>{
+          const r=byPos[pos]; if(!r||r.resnum==null)return;
+          viewer.addStyle({resi:r.resnum},{stick:{color:0x111827,radius:0.34}});
+          viewer.addResLabels({resi:r.resnum},{font:'sans-serif',fontSize:12,fontColor:'white',
+              backgroundColor:0x111827,backgroundOpacity:0.85,showBackground:true});
+        });
+        // Re-assert atom clickability after (re)building the representation. 3Dmol surface clicks route
+        // through the underlying atoms, so doing this here makes click-select work in every representation
+        // (cartoon, sticks, spheres, lines AND surface) — not only via the sequence track.
+        viewer.setClickable({}, true, onAtomClick);
+        viewer.render();
+      }
+      function onAtomClick(atom){ if(atom && atom.resi in byResnum) toggleResidue(byResnum[atom.resi].pos); }
+
+      /* ---------- legend ---------- */
+      function renderLegend(){
+        const m=DATA.metrics[METRIC], bar=document.getElementById('legbar');
+        let css;
+        if(m.type==='plddt'){
+          css='linear-gradient(90deg,#ff7d45 0%,#ff7d45 25%,#ffdb13 25%,#ffdb13 50%,#65cbf3 50%,#65cbf3 75%,#0053d6 75%)';
+          document.getElementById('legmin').textContent='0';
+          document.getElementById('legmid').textContent='50 · 70 · 90';
+          document.getElementById('legmax').textContent='100';
+        }else{
+          const stops=[]; for(let i=0;i<=10;i++)stops.push(cssColor(metricColor(METRIC,m.range[0]+(m.range[1]-m.range[0])*i/10)));
+          css='linear-gradient(90deg,'+stops.join(',')+')';
+          document.getElementById('legmin').textContent=nfmt(m.range[0]);
+          document.getElementById('legmid').textContent=nfmt((m.range[0]+m.range[1])/2);
+          document.getElementById('legmax').textContent=nfmt(m.range[1]);
+        }
+        bar.style.background=css;
+        document.getElementById('legdesc').textContent=m.desc;
+      }
+      function nfmt(x){return Math.abs(x)>=1000?x.toExponential(1):(Math.round(x*100)/100);}
+
+      /* ---------- sequence track ---------- */
+      function renderSequence(){
+        const seq=document.getElementById('seq'), ruler=document.getElementById('ruler');
+        seq.innerHTML=''; ruler.innerHTML='';
+        DATA.residues.forEach((r,i)=>{
+          const el=document.createElement('span');
+          el.className='res'+(r.resnum==null?' unmapped':'')+(selected.has(r.pos)?' sel':'');
+          el.textContent=r.wt; el.dataset.pos=r.pos;
+          const bg=metricColor(METRIC,r.metrics[METRIC]);
+          el.style.background=cssColor(bg);
+          el.style.color=textOn(bg);              // white letters on dark cells for legibility
+          el.title='pos '+r.pos+' ('+r.wt+')';
+          el.onclick=()=>toggleResidue(r.pos);
+          seq.appendChild(el);
+          if(r.pos%10===0){const t=document.createElement('span');t.textContent=r.pos;t.style.left=(i*13+6.5)+'px';ruler.appendChild(t);}
+        });
+      }
+      function markSequence(){
+        document.querySelectorAll('.seq .res').forEach(e=>e.classList.toggle('sel',selected.has(+e.dataset.pos)));
+      }
+
+      /* ---------- selection / camera ---------- */
+      function toggleResidue(pos){
+        const wasEmpty=selected.size===0;
+        if(selected.has(pos)){
+          // deselect — keep the current camera (do NOT reset the view here)
+          selected.delete(pos);
+          if(activePos===pos){ activePos = selected.size ? [...selected][selected.size-1] : null; activeVariant=null; }
+        }else{
+          selected.add(pos); activePos=pos; activeVariant=null;
+          const r=byPos[pos];
+          if(r&&r.resnum!=null){ viewer.center({resi:r.resnum},400); if(wasEmpty) viewer.zoom(1.15,400); }
+        }
+        markSequence();
+        applyStyle();
+        renderDetail();
+      }
+
+      /* ---------- residue detail ---------- */
+      function fmt(x,d=2){return x===null||x===undefined?'—':(Math.abs(x)>=1000?x.toExponential(1):(Math.round(x*Math.pow(10,d))/Math.pow(10,d)));}
+      function renderDetail(){
+        const empty=document.getElementById('detail-empty'), body=document.getElementById('detail-body');
+        if(activePos===null){ empty.style.display='block'; body.style.display='none'; return; }
+        empty.style.display='none'; body.style.display='block';
+        const r=byPos[activePos], fr=DATA.metrics.fitness.range;
+        let h='<h2>Position '+r.pos+' · '+r.wt+'</h2>';
+        h+='<div class="pos-sub">'+(r.resnum!=null?'structure residue '+r.resnum:'not mapped onto structure')
+           +(selected.size>1?' · '+selected.size+' residues selected':'')+'</div>';
+        h+='<div class="metrics">';
+        for(const k of METRIC_KEYS) h+='<div class="k">'+DATA.metrics[k].label+'</div><div class="v">'+fmt(r.metrics[k])+'</div>';
+        h+='</div>';
+        h+='<div class="subhead">Substitutions — click one for its distribution</div>';
+        if(!r.variants.length) h+='<div class="empty">No measured substitutions at this position.</div>';
+        r.variants.forEach(v=>{
+          const f=v.metrics?v.metrics.fitness:v.fitness, has=f!==null&&f!==undefined;
+          const t=has?Math.max(0,Math.min(1,(f-fr[0])/((fr[1]-fr[0])||1))):0;
+          const col=has?cssColor(metricColor('fitness',f)):'var(--na)';   // respects the fitness colour flip
+          const wpct=has?Math.max(4,t*100):0;
+          const isSel=activeVariant&&activeVariant.pos===r.pos&&activeVariant.mut===v.mut;
+          const lab=v.mut+(v.stop?' (stop)':v.syn?' (syn)':'');
+          h+='<div class="vbar'+(isSel?' sel':'')+'" data-mut="'+v.mut+'">'
+            +'<div class="aa">'+v.mut+'</div>'
+            +'<div class="track"><div class="fill" style="left:0;width:'+wpct+'%;background:'+col+'"></div></div>'
+            +'<div class="num" title="'+lab+'">'+fmt(f)+'</div></div>';
+        });
+        h+='<div id="swarm"></div>';
+        body.innerHTML=h;
+        body.querySelectorAll('.vbar').forEach(el=>{
+          el.onclick=()=>{ const mut=el.dataset.mut;
+            activeVariant=(activeVariant&&activeVariant.pos===r.pos&&activeVariant.mut===mut)?null:{pos:r.pos,mut:mut};
+            renderDetail();
+          };
+        });
+        if(activeVariant&&activeVariant.pos===r.pos) renderSwarm();
+      }
+
+      /* ---------- bee-swarm plots ---------- */
+      function jitter(pos,mutIdx){ const h=((pos*131+mutIdx*97)%211)/211; return h-0.5; }
+      function variantValue(metric,r,v){
+        // variant-scope metrics (e.g. fitness) have a per-substitution value; residue-scope metrics
+        // (coverage, pLDDT, correlation, ...) are per-position and shared by all substitutions there.
+        return DATA.metrics[metric].scope==='variant' ? (v.metrics? v.metrics[metric] : null) : r.metrics[metric];
+      }
+      function renderSwarm(){
+        const host=document.getElementById('swarm'); if(!host)return;
+        const av=activeVariant;
+        let h='<div class="swarm"><div class="shead"><b>'+av.mut+'&thinsp;@&thinsp;'+av.pos
+              +'</b><span class="hint" style="margin:0">one dot per variant</span></div>';
+        for(const k of METRIC_KEYS){
+          const info = DATA.metrics[k].desc
+            ? ' <button class="ibtn" data-desc="'+escAttr(DATA.metrics[k].desc)+'" aria-label="What is '+escAttr(DATA.metrics[k].label)+'?">i</button>'
+            : '';
+          h+='<div class="plot" data-metric="'+k+'"><div class="pt"><span>'+DATA.metrics[k].label+info
+            +'</span><span class="val" data-role="val"></span></div>'
+            +'<canvas></canvas><div class="ax"><span data-role="lo"></span><span data-role="hi"></span></div></div>';
+        }
+        h+='</div>';
+        host.innerHTML=h;
+        host.querySelectorAll('.plot').forEach(p=>drawSwarm(p, p.dataset.metric, av));
+      }
+      function drawSwarm(plotEl, metric, av){
+        const m=DATA.metrics[metric], lo=m.range[0], hi=m.range[1];
+        // population: one point per variant across all positions that has a value for this metric.
+        // Each point keeps its own value (x) and a stable per-substitution jitter (y), so the
+        // highlighted variant is shown at its TRUE location — not forced to the middle.
+        const pts=[]; let sel=null;
+        DATA.residues.forEach(r=>{
+          r.variants.forEach(v=>{
+            const val=variantValue(metric,r,v);
+            if(val===null||val===undefined)return;
+            const p={val, jit:jitter(r.pos, AA.indexOf(v.mut)), sel:(r.pos===av.pos&&v.mut===av.mut)};
+            pts.push(p); if(p.sel) sel=p;
+          });
+        });
+        const cv=plotEl.querySelector('canvas'), dpr=window.devicePixelRatio||1;
+        const cssW=Math.max(220, plotEl.clientWidth||280), cssH=96;
+        cv.width=Math.round(cssW*dpr); cv.height=Math.round(cssH*dpr);
+        const ctx=cv.getContext('2d'); ctx.scale(dpr,dpr);
+        const padX=12, padY=12, W=cssW, H=cssH, plotW=W-2*padX, midY=H/2, band=(H-2*padY)/2;
+        const xOf=val=>padX+plotW*Math.max(0,Math.min(1,(val-lo)/((hi-lo)||1)));
+        const yOf=jit=>midY+jit*2*band;
+        // population dots
+        for(const p of pts){ if(p.sel)continue;
+          ctx.beginPath(); ctx.arc(xOf(p.val), yOf(p.jit), 2.0, 0, 6.2832);
+          ctx.fillStyle=cssColor(metricColor(metric,p.val)); ctx.globalAlpha=0.4; ctx.fill();
+        }
+        ctx.globalAlpha=1;
+        // selected variant: highlight its actual data point (real x AND real jittered y)
+        if(sel){
+          const x=xOf(sel.val), y=yOf(sel.jit);
+          ctx.strokeStyle='rgba(17,24,39,.22)'; ctx.lineWidth=1;
+          ctx.beginPath(); ctx.moveTo(x,padY-4); ctx.lineTo(x,H-padY+4); ctx.stroke();
+          ctx.beginPath(); ctx.arc(x, y, 6.5, 0, 6.2832);
+          ctx.fillStyle=cssColor(metricColor(metric,sel.val)); ctx.fill();
+          ctx.lineWidth=3; ctx.strokeStyle='#fff'; ctx.stroke();
+          ctx.lineWidth=1.5; ctx.strokeStyle='#111827'; ctx.stroke();
+        }
+        plotEl.querySelector('[data-role=lo]').textContent=nfmt(lo);
+        plotEl.querySelector('[data-role=hi]').textContent=nfmt(hi);
+        plotEl.querySelector('[data-role=val]').textContent=sel?nfmt(sel.val):'no value';
+      }
+
+      /* ---------- controls ---------- */
+      function initMetricSelect(){
+        const sel=document.getElementById('metric');
+        for(const k of METRIC_KEYS){ const o=document.createElement('option');o.value=k;o.textContent=DATA.metrics[k].label;sel.appendChild(o); }
+        sel.value=METRIC;
+        sel.onchange=()=>{METRIC=sel.value;refresh();};
+      }
+      function refresh(){renderLegend();renderSequence();applyStyle();}
+      let spinning=false;
+      document.getElementById('rep').onchange=e=>{REP=e.target.value;applyStyle();};
+      document.getElementById('spin').onclick=e=>{spinning=!spinning;viewer.spin(spinning?'y':false);e.target.classList.toggle('on',spinning);};
+      document.getElementById('reset').onclick=()=>{ if(initialView){viewer.setView(initialView);viewer.render();} };
+      document.getElementById('clearsel').onclick=()=>{ selected.clear(); activePos=null; activeVariant=null;
+        markSequence(); applyStyle(); renderDetail(); };  // clears selection but keeps the camera
+      document.getElementById('png').onclick=downloadPNG;
+      document.getElementById('pymol').onclick=downloadPymol;
+
+      function downloadPNG(){
+        const el=document.getElementById('viewer');
+        const ow=el.style.width, oh=el.style.height, op=el.style.position, ol=el.style.left;
+        el.style.position='fixed'; el.style.left='-99999px'; el.style.width='2400px'; el.style.height='1800px';
+        viewer.resize(); viewer.render();
+        const uri=viewer.pngURI();
+        el.style.position=op; el.style.left=ol; el.style.width=ow; el.style.height=oh;
+        viewer.resize(); viewer.render();
+        const a=document.createElement('a'); a.href=uri;
+        a.download='deepmutscan_'+DATA.sample+'_'+METRIC+'.png'; document.body.appendChild(a); a.click(); a.remove();
+      }
+      function downloadPymol(){
+        buildColorMap();
+        const obj=(DATA.sample||'wildtype').replace(/[^A-Za-z0-9_]/g,'_');
+        let py='from pymol import cmd\n';
+        py+='cmd.read_pdbstr("""'+PDB.replace(/"""/g,'')+'""", "'+obj+'")\n';
+        py+='cmd.hide("everything")\ncmd.show("cartoon","'+obj+'")\ncmd.bg_color("white")\n';
+        DATA.residues.forEach(r=>{ if(r.resnum==null)return;
+          const c=hex2rgb((r.resnum in resiColor)?resiColor[r.resnum]:GREY);
+          const nm='dm_'+METRIC+'_'+r.resnum;
+          py+='cmd.set_color("'+nm+'",['+(c[0]/255).toFixed(3)+','+(c[1]/255).toFixed(3)+','+(c[2]/255).toFixed(3)+'])\n';
+          py+='cmd.color("'+nm+'","'+obj+' and resi '+r.resnum+'")\n';
+        });
+        const pml='# nf-core/deepmutscan structure viewer — coloring: '+DATA.metrics[METRIC].label
+          +'\n# Run in PyMOL:  File > Run Script...  (or:  @this_file.pml )\npython\n'+py+'python end\ncmd.orient()\n';
+        const blob=new Blob([pml],{type:'text/plain'});
+        const a=document.createElement('a'); a.href=URL.createObjectURL(blob);
+        a.download='deepmutscan_'+obj+'_'+METRIC+'.pml'; document.body.appendChild(a); a.click(); a.remove();
+        URL.revokeObjectURL(a.href);
+      }
+
+      /* ---------- replicate toggles ---------- */
+      if(REPS.length>1){
+        document.getElementById('repgroup').style.display='';
+        document.getElementById('reps').addEventListener('change',e=>{
+          const cb=e.target.closest('input[type=checkbox]'); if(!cb)return;
+          const i=+cb.dataset.i;
+          if(cb.checked) selReps.add(i); else selReps.delete(i);
+          // an empty selection is a legitimate state: no replicates -> no fitness -> everything grey (NA)
+          recomputeFitness();
+          renderReps();
+          refresh();                                   // legend + sequence + structure colouring
+          renderDetail();                              // detail panel numbers
+          if(activeVariant) renderSwarm();             // bee-swarms
+        });
+        renderReps();
+      }
+
+      /* ---------- boot ---------- */
+      document.getElementById('sample-tag').innerHTML='sample <strong>'+DATA.sample+'</strong> · '+DATA.residues.length+' residues';
+      initMetricSelect();
+      renderLegend(); renderSequence();
+      viewer.zoomTo(); applyStyle();
+      initialView=viewer.getView();
+      renderDetail();
+      window.addEventListener('resize',()=>{viewer.resize();viewer.render(); if(activeVariant)renderSwarm();});
+
+      /* ---------- bee-swarm info-button popovers ---------- */
+      function escAttr(s){return String(s).replace(/&/g,'&amp;').replace(/"/g,'&quot;').replace(/</g,'&lt;');}
+      document.addEventListener('click',e=>{
+        const prev=document.querySelector('.ipop'); if(prev)prev.remove();
+        const b=e.target.closest('.ibtn'); if(!b)return;
+        e.preventDefault(); e.stopPropagation();
+        const pop=document.createElement('div'); pop.className='ipop'; pop.textContent=b.getAttribute('data-desc');
+        document.body.appendChild(pop);
+        const r=b.getBoundingClientRect();
+        pop.style.left=Math.max(8,Math.min(r.left,innerWidth-pop.offsetWidth-8))+'px';
+        pop.style.top=(r.bottom+6+pop.offsetHeight>innerHeight ? r.top-pop.offsetHeight-6 : r.bottom+6)+'px';
+      });
+    </script>
+  </body>
+</html>
diff --git a/conf/base.config b/conf/base.config
index 3aae334..6dd4679 100644
--- a/conf/base.config
+++ b/conf/base.config
@@ -9,8 +9,6 @@
 */
 
 process {
-
-    // TODO nf-core: Check the defaults for all processes
     cpus   = { 1      * task.attempt }
     memory = { 6.GB   * task.attempt }
     time   = { 4.h    * task.attempt }
@@ -19,12 +17,17 @@ process {
     maxRetries    = 1
     maxErrors     = '-1'
 
+    // Disable 'noclobber' and pre-clean versions.yml so modules can overwrite it
+    beforeScript = '''
+      set +o noclobber
+      rm -f versions.yml || true
+    '''
+
     // Process-specific resource requirements
     // NOTE - Please try and reuse the labels below as much as possible.
     //        These labels are used and recognised by default in DSL2 files hosted on nf-core/modules.
     //        If possible, it would be nice to keep the same label naming convention when
     //        adding in your local modules too.
-    // TODO nf-core: Customise requirements for specific processes.
     // See https://www.nextflow.io/docs/latest/config.html#config-process-selectors
     withLabel:process_single {
         cpus   = { 1                   }
@@ -59,8 +62,4 @@ process {
         errorStrategy = 'retry'
         maxRetries    = 2
     }
-    withLabel: process_gpu {
-        ext.use_gpu = { workflow.profile.contains('gpu') }
-        accelerator = { workflow.profile.contains('gpu') ? 1 : null }
-    }
 }
diff --git a/conf/containers_conda_lock_files_amd64.config b/conf/containers_conda_lock_files_amd64.config
index d3ee1b4..41e234d 100644
--- a/conf/containers_conda_lock_files_amd64.config
+++ b/conf/containers_conda_lock_files_amd64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'modules/nf-core/fastqc/.conda-lock/linux_amd64-bd-5cb1a2fa2f18c7c2_1.txt' } }
-process { withName: 'MULTIQC' { container = 'modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-db7c73dae76bc9e6_1.txt' } }
+process { withName: 'MULTIQC' { container = 'modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt' } }
diff --git a/conf/containers_conda_lock_files_arm64.config b/conf/containers_conda_lock_files_arm64.config
index 2b90ac4..5b5b9aa 100644
--- a/conf/containers_conda_lock_files_arm64.config
+++ b/conf/containers_conda_lock_files_arm64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'modules/nf-core/fastqc/.conda-lock/linux_arm64-bd-e455e32f745abe68_1.txt' } }
-process { withName: 'MULTIQC' { container = 'modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-d167b8012595a136_1.txt' } }
+process { withName: 'MULTIQC' { container = 'modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt' } }
diff --git a/conf/containers_docker_amd64.config b/conf/containers_docker_amd64.config
index 65f1814..a66e3d1 100644
--- a/conf/containers_docker_amd64.config
+++ b/conf/containers_docker_amd64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'community.wave.seqera.io/library/fastqc:0.12.1--5cb1a2fa2f18c7c2' } }
-process { withName: 'MULTIQC' { container = 'community.wave.seqera.io/library/multiqc:1.34--db7c73dae76bc9e6' } }
+process { withName: 'MULTIQC' { container = 'community.wave.seqera.io/library/multiqc:1.35--c17fb751507e9dfc' } }
diff --git a/conf/containers_docker_arm64.config b/conf/containers_docker_arm64.config
index 6c845ba..215f686 100644
--- a/conf/containers_docker_arm64.config
+++ b/conf/containers_docker_arm64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'community.wave.seqera.io/library/fastqc:0.12.1--e455e32f745abe68' } }
-process { withName: 'MULTIQC' { container = 'community.wave.seqera.io/library/multiqc:1.34--d167b8012595a136' } }
+process { withName: 'MULTIQC' { container = 'community.wave.seqera.io/library/multiqc:1.35--5c84a5000a226ab5' } }
diff --git a/conf/containers_singularity_https_amd64.config b/conf/containers_singularity_https_amd64.config
index 838f248..2fb11a3 100644
--- a/conf/containers_singularity_https_amd64.config
+++ b/conf/containers_singularity_https_amd64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/f2/f20b021476d1d87658820f971ebecc1e8cdbde0f338eb0d9cea2b0a8fc54a54b/data' } }
-process { withName: 'MULTIQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1b/1bef8af6be88c5733461959c46ac8ef73d18f65277f62a1695d0e1633054f9c2/data' } }
+process { withName: 'MULTIQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/c8/c8e346f4f6080eadf1253505e6ff09ef004454fc18e8d672006fd7b222cc412e/data' } }
diff --git a/conf/containers_singularity_https_arm64.config b/conf/containers_singularity_https_arm64.config
index 090173b..5fa5b4f 100644
--- a/conf/containers_singularity_https_arm64.config
+++ b/conf/containers_singularity_https_arm64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/46/46daf2dad0169afd2ae047c3e50ed3776259f664bf07e5e06b045dc23449e994/data' } }
-process { withName: 'MULTIQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/9a/9a1fec9662a152683e6fcae440d0ce20920b3b89dc62d1e3a52e73f92eba0969/data' } }
+process { withName: 'MULTIQC' { container = 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e4/e48aa28aebc881254a499b24c3e1ce77b8df1b85a5432699ed6f72eb17ac7fb5/data' } }
diff --git a/conf/containers_singularity_oras_amd64.config b/conf/containers_singularity_oras_amd64.config
index 773f369..b334375 100644
--- a/conf/containers_singularity_oras_amd64.config
+++ b/conf/containers_singularity_oras_amd64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'oras://community.wave.seqera.io/library/fastqc:0.12.1--5c4bd442468d75dd' } }
-process { withName: 'MULTIQC' { container = 'oras://community.wave.seqera.io/library/multiqc:1.34--4fc8657c816047c0' } }
+process { withName: 'MULTIQC' { container = 'oras://community.wave.seqera.io/library/multiqc:1.35--c680f2aea25ccec2' } }
diff --git a/conf/containers_singularity_oras_arm64.config b/conf/containers_singularity_oras_arm64.config
index 798cc63..661c656 100644
--- a/conf/containers_singularity_oras_arm64.config
+++ b/conf/containers_singularity_oras_arm64.config
@@ -1,2 +1,2 @@
 process { withName: 'FASTQC' { container = 'oras://community.wave.seqera.io/library/fastqc:0.12.1--127a87fc06499035' } }
-process { withName: 'MULTIQC' { container = 'oras://community.wave.seqera.io/library/multiqc:1.34--7fbd82d945c06726' } }
+process { withName: 'MULTIQC' { container = 'oras://community.wave.seqera.io/library/multiqc:1.35--c0468833d65b2f81' } }
diff --git a/conf/modules.config b/conf/modules.config
index d203d2b..0dd3406 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -7,6 +7,9 @@
         ext.args2  = Second set of arguments appended to command in module (multi-tool modules).
         ext.args3  = Third set of arguments appended to command in module (multi-tool modules).
         ext.prefix = File name prefix for output files.
+
+    Output layout: Outputs are published directly into functional directories
+    under `${outdir}/...` without sample-specific or reference parent folders.
 ----------------------------------------------------------------------------------------
 */
 
@@ -20,6 +23,12 @@ process {
 
     withName: FASTQC {
         ext.args = '--quiet'
+        containerOptions = ''
+        publishDir = [
+            path: { "${params.outdir}/fastqc" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename.equals('versions.yml') ? null : filename }
+        ]
     }
 
     withName: 'MULTIQC' {
@@ -31,4 +40,233 @@ process {
         ]
     }
 
+    // --- Reference-derived, merged into intermediate_files ---
+
+    withName: 'BWA_INDEX' {
+        publishDir = [
+            path: "${params.outdir}/intermediate_files",
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'DMSANALYSIS_POSSIBLE_MUTATIONS' {
+        publishDir = [
+            path: "${params.outdir}/intermediate_files",
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'DMSANALYSIS_AASEQ' {
+        publishDir = [
+            path: "${params.outdir}/intermediate_files",
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // --- Alignment / filtering / merging ---
+
+    withName: 'BWA_MEM' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/bam_files/bwa/mem" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'BAMFILTER_DMS' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/bam_files/filtered" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'PREMERGE' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/bam_files/premerged" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'SAMTOOLS_SORT' {
+        ext.prefix = { "${meta.id}.sorted" }
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/bam_files/sorted" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'SAMTOOLS_INDEX' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/bam_files/sorted" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // --- Variant counting / processing / error correction (per-replicate meta.id subfolder) ---
+
+    withName: 'VARIANTCOUNTING' {
+      publishDir = [
+        path: { "${params.outdir}/intermediate_files/variant_counts" },
+        mode: 'copy',
+        overwrite: false,
+        saveAs: { filename ->
+          if (filename == 'versions.yml') return null
+          "${meta.id}/${filename}"
+        }
+      ]
+    }
+
+    withName: 'DMSANALYSIS_PROCESS_VARIANT_COUNTS' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/processed_variant_counts" },
+            mode: 'copy',
+            saveAs: { filename ->
+                if (filename == 'versions.yml') return null
+                "${meta.id}/${filename}"
+            }
+        ]
+    }
+
+    withName: 'DMSANALYSIS_ERROR_CORRECTION_.*' {
+        publishDir = [
+            path: { "${params.outdir}/intermediate_files/processed_variant_counts" },
+            mode: 'copy',
+            saveAs: { filename ->
+                if (filename == 'versions.yml') return null
+                "${meta.id}/${filename}"
+            }
+        ]
+    }
+
+    // Per-sample visualisations file under library_QC/<sample>/. The negative lookahead excludes the two
+    // run-level reports, which have no per-sample `meta.id` to file under - Nextflow ACCUMULATES
+    // publishDir across matching selectors rather than letting a later `withName` override it, so if this
+    // selector also matched them its `${meta.id}` would still be evaluated (throwing for the EC report,
+    // and mis-filing the summary report under library_QC/run/).
+    withName: /.*VISUALIZATION_(?!ERROR_CORRECTION_REPORT|SUMMARY_REPORT).*/ {
+        publishDir = [
+          path: { "${params.outdir}/library_QC" },
+          mode: 'copy',
+          overwrite: false,
+          saveAs: { fn ->
+            if (fn == 'versions.yml') return null
+            "${meta.id}/${fn}"
+          }
+        ]
+      }
+
+    // Run-level, so it has no meta.id to file itself under - published flat under library_QC.
+    withName: 'VISUALIZATION_ERROR_CORRECTION_REPORT' {
+        publishDir = [
+            path: { "${params.outdir}/library_QC" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // The all-in-one report is the flagship output - published at the results root, not buried.
+    withName: 'VISUALIZATION_SUMMARY_REPORT' {
+        publishDir = [
+            path: { "${params.outdir}" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    // --- Fitness and Experimental Design ---
+
+    withName: 'COUNTS_TO_FITNESS' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/DiMSum_results/single_rep_counts" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'MERGE_COUNTS' {
+        publishDir = [
+            path: { "${params.outdir}/fitness" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'EXPDESIGN_FITNESS' {
+        publishDir = [
+            path: "${params.outdir}/fitness",
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FIND_SYNONYMOUS_MUTATION' {
+        publishDir = [
+            path: { "${params.outdir}/fitness" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FITNESS_CALCULATION' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/default_results" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FITNESS_QC' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/default_results" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'FITNESS_HEATMAP' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/default_results" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'RUN_DIMSUM' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/DiMSum_results" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'RUN_MUTSCAN' {
+        publishDir = [
+            path: { "${params.outdir}/fitness/mutscan_results" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'VISUALIZATION_SUMMARY_REPORT' {
+        publishDir = [
+            path: { "${params.outdir}" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
+
+    withName: 'VARIANT_EFFECT_INSPECTION_TOOL' {
+        publishDir = [
+            path: { "${params.outdir}/variant_effect_inspection_tool" },
+            mode: 'copy',
+            saveAs: { filename -> filename == 'versions.yml' ? null : filename }
+        ]
+    }
 }
diff --git a/conf/test.config b/conf/test.config
index ae518f2..79a730b 100644
--- a/conf/test.config
+++ b/conf/test.config
@@ -13,7 +13,7 @@
 process {
     resourceLimits = [
         cpus: 4,
-        memory: '15.GB',
+        memory: '8.GB',
         time: '1.h'
     ]
 }
@@ -23,8 +23,18 @@ params {
     config_profile_description = 'Minimal test dataset to check pipeline function'
 
     // Input data
-    // TODO nf-core: Specify the paths to your test data on nf-core/test-datasets
-    // TODO nf-core: Give any required params for the test so that command line flags are not needed
-    input  = params.pipelines_testdata_base_path + 'viralrecon/samplesheet/samplesheet_test_illumina_amplicon.csv'// Genome references
-    genome = 'R64-1-1'
+    input = params.pipelines_testdata_base_path + 'deepmutscan/samplesheet/GID1A_test.csv'
+    fasta = params.pipelines_testdata_base_path + 'deepmutscan/testdata/GID1A.fasta'
+    reading_frame = '352-1383'
+    min_counts = 2
+    mutagenesis_type = 'nnk_nns'
+    run_seqdepth = true
+    fitness = true
+    mutscan = true
+    dimsum  = true
+
+    // Precomputed wildtype structure (AlphaFold DB model, GID1A / UniProt Q9MAA7, CC-BY-4.0) so the
+    // interactive variant effect inspection tool runs end-to-end without structure prediction.
+    // TODO: move this fixture to nf-core/test-datasets and reference it via pipelines_testdata_base_path.
+    pdb = "${projectDir}/assets/test_structure/GID1A_AFDB.pdb"
 }
diff --git a/conf/test_full.config b/conf/test_full.config
index 27ac392..edb899d 100644
--- a/conf/test_full.config
+++ b/conf/test_full.config
@@ -17,7 +17,7 @@ params {
     // Input data for full size test
     // TODO nf-core: Specify the paths to your full test data ( on nf-core/test-datasets or directly in repositories, e.g. SRA)
     // TODO nf-core: Give any required params for the test so that command line flags are not needed
-    input = params.pipelines_testdata_base_path + 'viralrecon/samplesheet/samplesheet_full_illumina_amplicon.csv'
+    input = params.pipelines_testdata_base_path + 'samplesheet_qc_only.csv'
 
     // Genome references
     genome = 'R64-1-1'
diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
index 3852535..639617e 100644
--- a/docs/CONTRIBUTING.md
+++ b/docs/CONTRIBUTING.md
@@ -179,7 +179,3 @@ nf-core pipelines bump-version --nextflow . <min_nf_version>
 #### Images and figures guidelines
 
 If you update images or graphics, follow the nf-core [style guidelines](https://nf-co.re/docs/community/brand/workflow-schematics).
-
-## Pipeline specific contribution guidelines
-
-<!-- TODO nf-core: Add any pipeline specific contribution guidelines here, such as coding styles, procedures, checklists etc. -->
diff --git a/docs/images/fastqc.png b/docs/images/fastqc.png
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new file mode 100644
index 0000000..81e30d8
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diff --git a/docs/images/library_QC_SeqDepth.png b/docs/images/library_QC_SeqDepth.png
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diff --git a/docs/images/library_QC_counts_per_cov_heatmap.png b/docs/images/library_QC_counts_per_cov_heatmap.png
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diff --git a/docs/images/library_QC_logdiff_plot.png b/docs/images/library_QC_logdiff_plot.png
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diff --git a/docs/images/library_QC_rolling_coverage.png b/docs/images/library_QC_rolling_coverage.png
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diff --git a/docs/images/multiqc2.png b/docs/images/multiqc2.png
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diff --git a/docs/images/multiqc3.png b/docs/images/multiqc3.png
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diff --git a/docs/images/nf-core-deepmutscan_logo_light.png b/docs/images/nf-core-deepmutscan_logo_light.png
index f8bbe08..ed93fd9 100644
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diff --git a/docs/images/pipeline.png b/docs/images/pipeline.png
new file mode 100644
index 0000000..fa66090
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diff --git a/docs/output.md b/docs/output.md
index 743e91f..e316c7d 100644
--- a/docs/output.md
+++ b/docs/output.md
@@ -2,60 +2,139 @@
 
 ## Introduction
 
-This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline.
+The directories listed below will be created in the results directory after `nf-core/deepmutscan` has finished. All paths are relative to the top-level results directory:
+
+Every sample-specific output is grouped under a per-sample parent folder (`<sample>/`, the `sample` column of the samplesheet). Files derived from the reference (shared across all samples) live in `reference/`, and genuinely global reports at the top level:
+
+```tree title="nf-core/deepmutscan results"
+results/
+├── <sample>/                    # everything belonging to one biological sample
+│   ├── fastqc/                  # raw sequencing QC reports for this sample's fastq files
+│   ├── intermediate_files/      # alignments, raw / pre-filtered / error-corrected variant count tables
+│   ├── library_QC/              # per-replicate QC visualisations + the error-correction report
+│   ├── fitness/                 # merged counts, fitness + error estimates, heatmaps, DiMSum / mutscan
+│   └── variant_effect_inspection_tool/   # interactive HTML tool (only with --pdb)
+├── reference/                   # reference-derived, shared: BWA index, aa_seq.txt, possible_mutations.csv
+├── experimental_design/         # run-level DiMSum experimental design (from the whole samplesheet)
+├── multiqc/                     # shared raw sequencing QC report for all fastq files
+├── pipeline_info/               # Nextflow helper files, timeline and summary report
+├── timeline.html
+└── report.html
+```
 
-The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory.
+### FastQC
+
+[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the [FastQC help pages](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/).
+
+<details markdown="1">
+<summary>Output files</summary>
 
-<!-- TODO nf-core: Write this documentation describing your workflow's output -->
+- `fastqc/`
+  - `*_fastqc.zip`: Zip archive containing the FastQC report, tab-delimited data file and plot images
+  - `*_fastqc.html`: FastQC report containing quality metrics
 
-## Pipeline overview
+![FASTQC report](images/fastqc.png)
 
-The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps:
+</details>
 
-- [FastQC](#fastqc) - Raw read QC
-- [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline
-- [Pipeline information](#pipeline-information) - Report metrics generated during the workflow execution
+### MultiQC
 
-### FastQC
+[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory. Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>.### Pipeline information
 
 <details markdown="1">
 <summary>Output files</summary>
 
-- `fastqc/`
-  - `*_fastqc.html`: FastQC report containing quality metrics.
-  - `*_fastqc.zip`: Zip archive containing the FastQC report, tab-delimited data file and plot images.
+- `multiqc/`
+  - `multiqc_report.html`: a standalone `.html` file that can be viewed in your web browser
+  - `multiqc_data/`: directory containing parsed statistics from the different tools used in the pipeline
+  - `multiqc_plots/`: directory containing static images from the report in various formats
+
+![MULTIQC overview](images/multiqc1.png)
+![MULTIQC base-quality summary](images/multiqc2.png)
+![MULTIQC GC-content summary](images/multiqc3.png)
 
 </details>
 
-[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the [FastQC help pages](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/).
+### Intermediate files (Core Pipeline Stages 1-4)
 
-### MultiQC
+This directory is created during the first series of steps of the pipeline, featuring raw read alignments, filtering and variant counting.
 
 <details markdown="1">
 <summary>Output files</summary>
 
-- `multiqc/`
-  - `multiqc_report.html`: a standalone HTML file that can be viewed in your web browser.
-  - `multiqc_data/`: directory containing parsed statistics from the different tools used in the pipeline.
-  - `multiqc_plots/`: directory containing static images from the report in various formats.
+- `intermediate_files/`
+  - `aa_seq.txt`: a string of the reconstructed wildtype amino acid sequence of the specified open reading frame
+  - `possible_mutations.csv`: using the `--mutagenesis` argument, this file lists all the programmed mutations per position; these are used for variant count filtering and visualisation
+  - `bam_files/bwa/`: sets of BWA referenes indices from the original alignment(s), with BAM files for each sample in the `mem/` subfolder
+  - `bam_files/filtered/`: filtered BAM files for each sample, without indel-matching reads (wildtype reads are retained for downstream sequencing-error correction)
+  - `bam_files/premerged/`: filtered, read-merged and re-aligned BAM files for each sample, representing highest-quality alignments for subsequent variant counting
+  - `bam_files/sorted/`: coordinate-sorted and indexed premerged BAM files (required by the variant counter)
+  - `variant_counts/`: subfolders with the resulting variant count table (`*.variant_counts.tsv`) plus a `variant_counts_columns.tsv` column-description sheet, stratified by sample
+  - `processed_variant_counts/`: subfolders with prefiltered variant count tables, stratified by sample
 
 </details>
 
-[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory.
+### Library QC (Core Pipeline Stage 5)
+
+This directory is created during the second series of steps of the pipeline, featuring various QC visualisations for each sample.
+
+<details markdown="1">
+<summary>Output files</summary>
+
+- `library_QC/`
+  - `counts_heatmap.pdf`: a complete heatmap of absolute mutant counts, stratified by mutant amino acid (Y-axis) per position (X-axis)
+    ![Count heatmap](images/library_QC_counts_heatmap.png)
+
+  - `counts_per_cov_heatmap.pdf`: as above, but as a fraction of the total sequencing coverage
+  - `logdiff_plot.pdf`: sorted, log-scale coverage distribution of all mutants
+    ![Logarithmic count differences](images/library_QC_logdiff_plot.png)
+
+  - `logdiff_varying_bases.pdf`: as above, but stratified by hamming distance to the wildtype nucleotide sequence (colour shading)
+  - `rolling_coverage.pdf`: sliding-window rolling coverage
+    ![Rolling coverage](images/library_QC_rolling_coverage.png)
+
+  - `rolling_counts.pdf`: sliding-window rolling coverage, stratified by hamming distance to the wildtype nucleotide sequence (colour shading)
+    ![Rolling counts](images/library_QC_rolling_counts.png)
 
-Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see <http://multiqc.info>.
+  - `rolling_counts_per_cov.pdf`: as above, but as a fraction of the total sequencing coverage
+  - `SeqDepth.pdf` (optional via the `--run_seqdepth` argument): rarefaction curve of the sequencing coverage and how it relates to the percentage of programmed variants detected
+    ![Sequencing coverage rarefaction](images/library_QC_SeqDepth.png)
 
-### Pipeline information
+  - `<sample>/<sample>_error_correction_report.html` (unless `--error_correction none`): a self-contained interactive report showing the effect of the sequencing-error correction — positional error bias along the ORF, the distribution of per-variant correction magnitude, a raw-vs-corrected scatter, and a searchable per-variant table.
+
+</details>
+
+### Fitness (Core Pipeline Stages 6-8)
+
+This directory is created during the final series of steps of the pipeline, featuring fitness and fitness error estimates (when DMS input/output sample groups are specified).
 
 <details markdown="1">
 <summary>Output files</summary>
 
+- `fitness/`
+  - `counts_merged.tsv`: summarised gene variant counts across all input and output samples
+  - `default_results/fitness_estimation_count_correlation.pdf`: pair-wise replicate variant count scatterplots and correlations between all specified samples
+    ![Variant count correlation(s)](images/fitness_estimation_count_correlation.png)
+
+  - `default_results/fitness_estimation_fitness_correlation.pdf`: pair-wise fitness replicate scatterplots and correlations between all specified output samples
+    ![Fitness correlation(s)](images/fitness_estimation_fitness_correlation.png)
+
+  - `default_results/fitness_heatmap.pdf`: a complete heatmap of absolute mutant counts, stratified by mutant amino acid (Y-axis) per position (X-axis)
+    ![Default fitness heatmap](images/fitness_heatmap.png)
+
+  - `default_results/fitness_estimation.tsv`: table file with all fitness and fitness error estimates calculated
+  - `DiMSum_results/dimsum_results/` (optional): subfolder with the full set of [DiMSum](https://github.com/lehner-lab/DiMSum) outputs, including the associated `.HTML` report, `.Rdata` and `.tsv` files with fitness and fitness error estimates
+
+- `<sample>/variant_effect_inspection_tool.html` (only with `--pdb`): a single, portable, interactive HTML tool that projects per-residue metrics (fitness, coverage, counts, counts/coverage, and — when correction is on — positional error bias) onto the supplied wildtype 3D structure. Select a residue to inspect each substitution's effect; download high-resolution images/PyMOL sessions. Works offline and can be shared as a single file.
+
+</details>
+
+### Pipeline Info (Nextflow Reports)
+
 - `pipeline_info/`
   - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.dot`/`pipeline_dag.svg`.
   - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameter's are used when running the pipeline.
   - Reformatted samplesheet files used as input to the pipeline: `samplesheet.valid.csv`.
   - Parameters used by the pipeline run: `params.json`.
 
-</details>
-
 [Nextflow](https://www.nextflow.io/docs/latest/tracing.html) provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.
diff --git a/docs/pipeline_steps.md b/docs/pipeline_steps.md
new file mode 100644
index 0000000..5e6e2ed
--- /dev/null
+++ b/docs/pipeline_steps.md
@@ -0,0 +1,114 @@
+# nf-core/deepmutscan: Detailed Pipeline Steps
+
+This page provides in-depth descriptions of the data processing modules implemented in **nf-core/deepmutscan**. It is mainly intended for advanced users and developers who want to understand the rationale behind design choices, explore implementation details, and consider potential future extensions.
+
+---
+
+## Overview
+
+The pipeline processes deep mutational scanning (DMS) sequencing data in several stages:
+
+1. Alignment of reads to the reference open reading frame (ORF)
+2. Filtering of wildtype and erroneous reads
+3. Read merging for base error reduction
+4. Mutation counting
+5. DMS library quality control
+6. Data summarisation across samples
+7. Single nucleotide variant error correction
+8. Fitness estimation _(in development)_
+
+![pipeline](/docs/pipeline.png)
+
+Each step is explained below. Links are provided to the primary tools and libraries used, where applicable.
+
+---
+
+## 1. Alignment
+
+All paired-end raw reads are first aligned to the provided reference ORF using [**bwa-mem**](http://bio-bwa.sourceforge.net/). This is a highly efficient mapping algorithm for reads ≥100 bp, with its multi-threading support automatically handled by nf-core.
+
+In future versions of nf-core/deepmutscan, we consider the use of [**bwa-mem2**](https://github.com/bwa-mem2/bwa-mem2), which provides similar alignment rates with a moderate speed increase ([Vasimuddin et al., _IPDPS_ 2019](https://ieeexplore.ieee.org/document/8820962)). With the increasing diversity of sequencing platforms for DMS, new throughput, read length, and error profiles may require further alignment options to be implemented.
+
+---
+
+## 2. Filtering
+
+For long ORF site-saturation mutagenesis libraries, most aligned shotgun sequencing reads contain exact matches against the reference. Erroneous reads with unexpected indels are removed, while exact-match (wildtype) reads are retained — they are ignored by the variant counter and kept available for downstream sequencing-error correction.
+
+To this end, we use [**samtools view**](https://www.htslib.org/doc/samtools.html).
+
+---
+
+## 3. Read Merging
+
+Even the highest-accuracy next-generation sequencing platforms do not have perfect base accuracy. To minimise the effect of base errors (which would otherwise be counted as "false mutations"), nf-core/deepmutscan uses the overlap of each aligned read pair. With base errors on the forward and reverse read being independent, the pipeline applies the [**vsearch fastq_mergepairs**](https://github.com/torognes/vsearch) function to convert each read pair into a single consensus molecule with adjusted base error scores.
+
+> [!TIP]
+> Optimal merging performance is usually obtained if the average DNA fragment size matches the read size. For example, libraries sequenced with 150 bp paired-end reads should ideally also be sheared/tagmented to a mean size of 150 bp.
+
+Future versions may offer additional options depending on sequencing type and error profiles.
+
+---
+
+## 4. Variant Counting
+
+Aligned consensus reads are screened for exact, base-level mismatches. nf-core/deepmutscan uses a lightweight, dependency-free Python counter (built on [**pysam**](https://pysam.readthedocs.io) and [**polars**](https://pola.rs)) to count occurrences of all single, double, triple, and higher-order nucleotide changes between each read and the reference ORF, from a coordinate-sorted, indexed BAM. The counter replaces GATK `AnalyzeSaturationMutagenesis` (keeping the container light) while producing a column-compatible count table.
+
+Two options tune the counter: `--base_qual` sets the minimum base quality for a mutation to be counted (default Q30; not available in GATK), and `--min_flank` sets the minimum distance (nt) a mutation or covered base must lie from a read edge (default 2, matching GATK's `--min-flanking-length`; 0 disables). The `--min_flank` window is applied to both variant calls and coverage.
+
+---
+
+## 5. DMS Library Quality Control
+
+By integrating the reference ORF coordinates and the chosen DMS library type (default: NNK/NNs degenerate codon-based nicking), nf-core/deepmutscan calculates a number of mutation count summary statistics.
+
+Custom visualisations allow for inspection of (1) mutation efficiency along the ORF, (2) position-specific recovery of amino acid diversity, and (3) overall sequencing coverage evenness and saturation.
+
+---
+
+## 6. Data Summarisation for Fitness Estimation
+
+Steps 1-5 are iteratively run across all samples defined in the `.csv` spreadsheet. Once read alignment, merging, mutation counting, and library QC have been completed for the full list of samples, users can opt to proceed with fitness estimation. To this end, the pipeline generates all the necessary input files by merging mutation counts across samples.
+
+---
+
+## 7. Single Nucleotide Variant Error Correction
+
+With very deep sequencing, single-codon counts show position-dependent bias from sequencing errors. `nf-core/deepmutscan` distinguishes true single-nucleotide variants from artefacts via the `--error_correction` parameter, with three options:
+
+- `false_doubles` (**default**): because the library only contains single-codon changes, any observed multi-codon variant ("false double") must arise from sequencing error. Their counts, with a distance-dependent coverage model, estimate the per-nucleotide error rate that is subtracted from the single-codon counts. Needs no extra data. `--false_doubles_method` picks the estimator: `mle` (default, per-variant maximum likelihood) or `eb` (empirical Bayes, pooled and shrunk per substitution class — steadier for sparse variants). `--false_doubles_codon_window` (default 40) sets the codon matching window.
+- `none`: no correction.
+- `wildtype`: empirical error-rate modelling from **additional deep wildtype-only sequencing**. Add the wildtype sample(s) to the samplesheet with `type: wildtype`; their position-specific error profile is subtracted from each library sample of the same `sample`. This replaces the false-doubles correction.
+
+The corrected counts feed the fitness estimation and the mutation-count heatmaps; the corrected count tables are written next to the uncorrected ones under `intermediate_files/processed_variant_counts/`.
+
+---
+
+## 8. Fitness Estimation _(in development)_
+
+The final step of the pipeline will perform fitness estimation based on mutation counts. By default, we calculate fitness scores as the logarithm of variants' output to input ratio, normalised to that of the provided wildtype sequence. Future expansions may include:
+
+- Integration of other popular fitness inference tools, including [DiMSum](https://github.com/lehner-lab/DiMSum), [Enrich2](https://github.com/FowlerLab/Enrich2), [rosace](https://github.com/pimentellab/rosace/) and [mutscan](https://github.com/fmicompbio/mutscan)
+- Standardised output formats for downstream analyses and comparison
+
+> [!IMPORTANT]
+> We note that exact wildtype sequence reads are filtered out in stage 2. Including synonymous wildtype codons in the original mutagenesis design is therefore essential when it comes to calibrating the fitness calculations.
+
+---
+
+## Notes for Developers
+
+- Custom scripts used in filtering and mutation counting are available in the `bin/` directory of the repository.
+- Modules are implemented in Nextflow DSL2 and follow the nf-core community guidelines.
+- Contributions, optimisations, and additional analysis modules are welcome - please open a pull request or GitHub issue to discuss ideas.
+
+_This document is meant as a living reference. As the pipeline evolves, the descriptions of steps 7 and 8 will be expanded with concrete implementation details._
+
+---
+
+## Contact
+
+For detailled scientific or technical questions, feedback and experimental discussions, feel free to contact us directly:
+
+- Benjamin Wehnert — wehnertbenjamin@gmail.com
+- Maximilian Stammnitz — maximilian.stammnitz@crg.eu
diff --git a/docs/usage.md b/docs/usage.md
index 8edeeee..97fd15f 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -6,69 +6,33 @@
 
 ## Introduction
 
-<!-- TODO nf-core: Add documentation about anything specific to running your pipeline. For general topics, please point to (and add to) the main nf-core website. -->
+**nf-core/deepmutscan** is a workflow designed for the analysis of deep mutational scanning (DMS) data. DMS enables researchers to experimentally measure the fitness effects of thousands of genes or gene variants simultaneously, helping to classify disease causing mutants in human and animal populations, to learn the fundamental rules of virus evolution, protein architecture, splicing, small-molecule interactions and many other phenotypes.
 
-## Samplesheet input
-
-You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.
-
-```bash
---input '[path to samplesheet file]'
-```
-
-### Multiple runs of the same sample
-
-The `sample` identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:
-
-```csv title="samplesheet.csv"
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
-CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz
-```
-
-### Full samplesheet
-
-The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.
-
-A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where `TREATMENT_REP3` has been sequenced twice.
-
-```csv title="samplesheet.csv"
-sample,fastq_1,fastq_2
-CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
-CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
-CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz
-TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,
-TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,
-TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,
-TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,
-```
-
-| Column    | Description                                                                                                                                                                            |
-| --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `sample`  | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (`_`). |
-| `fastq_1` | Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".                                                             |
-| `fastq_2` | Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".                                                             |
-
-An [example samplesheet](../assets/samplesheet.csv) has been provided with the pipeline.
+This page provides in-depth descriptions of the data processing modules implemented in `nf-core/deepmutscan`. It is similarly intended for new deep mutational scanning aficionados, for advanced users and developers who want to understand the rationale behind certain design choices, to explore implementation details and consider potential future extensions.
 
 ## Running the pipeline
 
-The typical command for running the pipeline is as follows:
+The typical command for running the pipeline (on an example protein-coding gene with 100 amino acids) is as follows:
 
-```bash
-nextflow run nf-core/deepmutscan --input ./samplesheet.csv --outdir ./results --genome GRCh37 -profile docker
+```bash title="example_run.sh"
+nextflow run nf-core/deepmutscan \
+   -profile <docker/singularity/.../institute> \
+   --input ./samplesheet.csv \
+   --fasta ./ref.fa \
+   --reading_frame 1-300 \
+   --outdir ./results
 ```
 
-This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.
+The `-profile <PROFILE>` specification is mandatory and should reflect either your own institutional profile or any pipeline profile specified in the [profile section](##-profile).
+
+This will launch the pipeline by performing sequencing read alignments, various raw data QC analyses, optional mutant count error corrections, fitness and fitness error estimations.
 
 Note that the pipeline will create the following files in your working directory:
 
-```bash
-work                # Directory containing the nextflow working files
-<OUTDIR>            # Finished results in specified location (defined with --outdir)
-.nextflow_log       # Log file from Nextflow
-# Other nextflow hidden files, eg. history of pipeline runs and old logs.
+```console title="working directory"
+work               # Directory containing the nextflow working files
+results            # Finished results in specified location (defined with --outdir); needs full writing access
+.nextflow_log      # Log file from Nextflow
 ```
 
 If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.
@@ -80,7 +44,7 @@ Pipeline settings can be provided in a `yaml` or `json` file via `-params-file <
 
 The above pipeline run specified with a params file in yaml format:
 
-```bash
+```bash title="example_run.sh"
 nextflow run nf-core/deepmutscan -profile docker -params-file params.yaml
 ```
 
@@ -89,32 +53,153 @@ with:
 ```yaml title="params.yaml"
 input: './samplesheet.csv'
 outdir: './results/'
-genome: 'GRCh37'
+gene reference: 'ref.fa'
 <...>
 ```
 
 You can also generate such `YAML`/`JSON` files via [nf-core/launch](https://nf-co.re/launch).
 
-### Updating the pipeline
+## Inputs
+
+Users need to first prepare a samplesheet with your input/output data in which each row represents a pair of matched fastq files (paired end). This should look as follows:
+
+```csv title="samplesheet.csv"
+sample,type,replicate,file1,file2
+ORF1,input,1,/reads/forward1.fastq.gz,/reads/reverse1.fastq.gz
+ORF1,input,2,/reads/forward2.fastq.gz,/reads/reverse2.fastq.gz
+ORF1,output,1,/reads/forward3.fastq.gz,/reads/reverse3.fastq.gz
+ORF1,output,2,/reads/forward4.fastq.gz,/reads/reverse4.fastq.gz
+```
+
+Secondly, users need to specify the gene or gene region of interest using a reference FASTA file via `--fasta`. Provide the exact codon coordinates using `--reading_frame`.
+
+## Optional parameters
+
+Several optional parameters are available for `nf-core/deepmutscan`, some of which are currently _(in development)_.
+
+| Parameter            | Default         | Description                                                                    |
+| -------------------- | --------------- | ------------------------------------------------------------------------------ |
+| `--run_seqdepth`     | `true`          | Estimate sequencing-depth saturation by closed-form hypergeometric rarefaction |
+| `--fitness`          | `false`         | Default fitness inference module                                               |
+| `--dimsum`           | `false`         | Optional fitness inference module _(AMD/x86_64 systems only)_                  |
+| `--mutagenesis`      | `nnk`           | Deep mutational scanning strategy used                                         |
+| `--error-estimation` | `wt_sequencing` | Error model used to correct 1nt counts _(in development)_                      |
+| `--read-align`       | `bwa-mem`       | Customised read aligner _(in development)_                                     |
+
+## Pipeline output
+
+After execution, the pipeline creates the following directory structure:
+
+```tree title="nf-core/deepmutscan results"
+results/
+├── fastqc/              # Individual HTML reports for specified fastq files, raw sequencing QC
+├── fitness/             # Merged variant count tables, fitness and error estimates, replicate correlations and heatmaps
+├── intermediate_files/  # Raw alignments, raw and pre-filtered variant count tables, QC reports
+├── library_QC/          # Sample-specific PDF visualizations: position-wise sequencing coverage, count heatmaps, etc.
+├── multiqc/             # Shared HTML reports for all fastq files, raw sequencing QC
+├── pipelineinfo/        # Nextflow helper files for timeline and summary report generation
+├── timeline.html        # Nextflow timeline for all tasks
+└── report.html          # Nextflow summary report incl. detailed CPU and memory usage per for all tasks
+```
+
+## Detailed steps
+
+### 1. Alignment
+
+All paired-end raw reads are first aligned to the provided reference ORF using [**bwa-mem**](http://bio-bwa.sourceforge.net/). This is a highly efficient mapping algorithm for reads ≥100 bp, with its multi-threading support automatically handled by nf-core.
+
+In future versions of `nf-core/deepmutscan`, we consider the use of [**bwa-mem2**](https://github.com/bwa-mem2/bwa-mem2), which provides similar alignment quality at moderate speed increase ([Vasimuddin et al., _IPDPS_ 2019](https://ieeexplore.ieee.org/document/8820962)). With the increasing diversity of sequencing platforms for DMS new read length, throughput and error profiles may require further alignment options to be implemented.
+
+### 2. Filtering
+
+For long ORF site-saturation mutagenesis libraries, most aligned shotgun sequencing reads contain exact matches against the reference. Reads with likely artefactual indel-containing alignments are removed, while exact-match (wildtype) reads are retained — they are ignored by the variant counter and kept available for downstream sequencing-error correction.
+
+To this end, we use [**samtools view**](https://www.htslib.org/doc/samtools.html).
+
+### 3. Read Merging
+
+Even the highest-quality next-generation sequencing platforms do not feature perfect base accuracy. To minimise the effect of base errors (which would otherwise be counted as "false mutations"), `nf-core/deepmutscan` uses the overlap of each aligned read pair. With base errors on the forward and reverse read being independent, the pipeline applies the [**vsearch fastq_mergepairs**](https://github.com/torognes/vsearch) function to convert each read pair into a single consensus molecule with adjusted base error scores.
+
+> [!TIP]
+> Optimal merging benefit is usually obtained if the average DNA fragment size matches the read size. For example, libraries sequenced with 150 bp paired-end reads should ideally also be sheared/tagmented to a mean size of 150 bp.
+
+Future versions may offer additional options depending on sequencing type and error profiles.
+
+### 4. Variant Counting
+
+Aligned consensus reads are screened for exact, base-level mismatches. `nf-core/deepmutscan` uses a lightweight, dependency-free Python counter (built on [**pysam**](https://pysam.readthedocs.io) and [**polars**](https://pola.rs)) to count occurrences of all single, double, triple, and higher-order nucleotide changes between each read and the reference ORF, from a coordinate-sorted, indexed BAM. It produces a variant count table that is column-compatible with the previously used GATK `AnalyzeSaturationMutagenesis` output.
+
+Two options tune the counter: `--base_qual` sets the minimum base quality for a mutation to be counted (default `30`; not available in GATK), and `--min_flank` sets the minimum distance (in nt) a mutation or covered base must be from a read edge (default `2`, matching GATK's `--min-flanking-length`; set to `0` to disable). The `--min_flank` window is applied consistently to both variant calls and the coverage calculation.
+
+### 5. DMS Library Quality Control
+
+By integrating the reference ORF coordinates and the chosen DMS library type (default: NNK degenerate codons), `nf-core/deepmutscan` calculates a number of mutation count summary statistics.
+
+Custom visualisations allow for inspection of (1) mutation efficiency along the ORF, (2) position-specific recovery of mutant amino acid diversity, and (3) overall sequencing coverage evenness and library saturation.
+
+### 6. Data Summarisation for Fitness Estimation
+
+Steps 1-5 are run in parallel across all individual samples defined in the `.csv` spreadsheet. Once read alignment, filtering, merging, variant counting, and DMS library QC have been completed for the full list of samples – if input/output sample pairs are available – users can opt to proceed towards fitness estimation. To this end, the pipeline generates all the necessary preparatory files by generating a merged mutation count table across samples.
+
+### 7. Single Nucleotide Variant Error Correction
+
+Very deep sequencing introduces position-dependent false counts. Select a strategy with `--error_correction`:
+
+- `false_doubles` (**default**): the library only contains single-codon changes, so observed multi-codon variants ("false doubles") are sequencing errors. Their counts, together with a distance-dependent coverage model, estimate the per-nucleotide error rate, which is subtracted from the single-codon counts. No extra data required. Two estimators are available via `--false_doubles_method`:
+  - `mle` (**default**): a per-variant maximum-likelihood error rate.
+  - `eb`: an empirical-Bayes estimate that pools across variants and shrinks per substitution class, which is steadier for sparsely observed variants.
+
+  The matching window (in codons) is set by `--false_doubles_codon_window` (default 40).
+
+- `none`: disables error correction (counts pass through unchanged).
+- `wildtype`: subtracts a position-specific error profile measured from an **additional deep wildtype-only sequencing** sample. Provide it in the samplesheet with `type: wildtype`, sharing the same `sample` name as the input/output samples it should correct. This replaces the false-doubles correction.
+
+The corrected counts are used for every count-dependent step (fitness, count heatmaps, positional-bias and coverage QC). Whenever correction is applied, a self-contained `*_error_correction_report.html` is written per sample so its effect can be inspected interactively.
+
+### Interactive variant effect inspection tool (`--pdb`)
+
+When `--fitness` is set and a wildtype 3D structure is supplied via `--pdb <structure.pdb>` (a crystal structure, an AlphaFold DB model, or any PDB matching the wildtype ORF), the pipeline additionally builds a single, portable, interactive HTML tool per sample. It projects per-residue fitness, coverage, counts, counts/coverage and — when error correction is on — the positional error bias onto the structure; clicking a residue shows each substitution's effect. Structure prediction (nf-core/proteinfold) is not wired in this release, so `--pdb` is required to enable the tool.
+
+### 8. Fitness Estimation
+
+The final step of the pipeline will perform fitness estimation based on mutation counts. By default, we calculate fitness scores as the logarithm of variants' output to input ratio, normalised to that of the provided wildtype nucleotide sequence.
+
+Future expansions may include:
+
+- Integration of other popular fitness inference tools, including [DiMSum](https://github.com/lehner-lab/DiMSum), [Enrich2](https://github.com/FowlerLab/Enrich2), [rosace](https://github.com/pimentellab/rosace/) and [mutscan](https://github.com/fmicompbio/mutscan)
+- Standardised output formats for downstream analyses and comparison
+
+> [!IMPORTANT]
+> We note that exact wildtype sequence reads are filtered out in stage 2. Including synonymous wildtype codons in the original mutagenesis design is therefore essential when it comes to calibrating the fitness calculations.
+
+## Notes for Developers
+
+- Custom R and Python scripts used in variant counting, filtering, error correction and QC visualisation live in each module's own `templates/` directory (e.g. `modules/local/dmsanalysis/*/templates/`).
+- Modules are implemented in Nextflow DSL2 and follow the nf-core community guidelines.
+- Contributions, optimisations, and additional analysis modules are welcome: please open a Github [issue](https://github.com/nf-core/deepmutscan/issues/new) or [pull request](https://github.com/nf-core/deepmutscan/compare) to discuss or suggest ideas.
+
+_This document is meant as a living reference. As the pipeline evolves, the descriptions of steps 7 and 8 will be further expanded with concrete implementation details._
+
+## Updating the pipeline
 
-When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:
+When you run the original command above, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use this cached version if available - even if the pipeline has been updated since. To make sure that you are running the latest version of the pipeline, make sure that you regularly update the cached version:
 
 ```bash
 nextflow pull nf-core/deepmutscan
 ```
 
-### Reproducibility
+## Reproducibility
 
 It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.
 
-First, go to the [nf-core/deepmutscan releases page](https://github.com/nf-core/deepmutscan/releases) and find the latest pipeline version - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. Of course, you can switch to another version by changing the number after the `-r` flag.
+First, go to the [nf-core/deepmutscan releases page](https://github.com/nf-core/deepmutscan/releases) and find the latest pipeline version - numeric only (eg. `1.0.0`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.0.0`.
 
 This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.
 
 To further assist in reproducibility, you can use share and reuse [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
 
 > [!TIP]
-> If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
+> If you wish to share such a profile (e.g. providing it as supplementary material for academic publications), make sure to _not_ include your cluster specific file paths or institutional specific profiles.
 
 ## Core Nextflow arguments
 
@@ -135,7 +220,7 @@ The pipeline also dynamically loads configurations from [https://github.com/nf-c
 Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important!
 They are loaded in sequence, so later profiles can overwrite earlier profiles.
 
-If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer environment.
+If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it may lead to varying or even irreproducible results across users' different computer environments.
 
 - `test`
   - A profile with a complete configuration for automated testing
@@ -149,7 +234,7 @@ If `-profile` is not specified, the pipeline will run locally and expect all sof
 - `shifter`
   - A generic configuration profile to be used with [Shifter](https://nersc.gitlab.io/development/shifter/how-to-use/)
 - `charliecloud`
-  - A generic configuration profile to be used with [Charliecloud](https://charliecloud.io/)
+  - A generic configuration profile to be used with [Charliecloud](https://hpc.github.io/charliecloud/)
 - `apptainer`
   - A generic configuration profile to be used with [Apptainer](https://apptainer.org/)
 - `wave`
@@ -159,7 +244,7 @@ If `-profile` is not specified, the pipeline will run locally and expect all sof
 
 ### `-resume`
 
-Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html).
+Specify this when restarting a pipeline. Nextflow will use cached results (from within the `/work` directory) from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html).
 
 You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names.
 
diff --git a/log.txt b/log.txt
new file mode 100644
index 0000000..dceb2c1
--- /dev/null
+++ b/log.txt
@@ -0,0 +1,613 @@
+May-07 23:01:49.322 [main] DEBUG nextflow.cli.Launcher - $> nextflow run . -profile test,docker --fasta /Users/benjaminwehnert/GID1A_SUNi_ref_small.fasta --reading_frame 352-1383 --min_counts 2 --mutagenesis_type max_diff_to_wt --outdir ./results
+May-07 23:01:49.500 [main] DEBUG nextflow.cli.CmdRun - N E X T F L O W  ~  version 24.04.4
+May-07 23:01:49.524 [main] DEBUG nextflow.plugin.PluginsFacade - Setting up plugin manager > mode=prod; embedded=false; plugins-dir=/Users/benjaminwehnert/.nextflow/plugins; core-plugins: nf-amazon@2.5.3,nf-azure@1.6.1,nf-cloudcache@0.4.1,nf-codecommit@0.2.1,nf-console@1.1.3,nf-ga4gh@1.3.0,nf-google@1.13.2-patch1,nf-tower@1.9.1,nf-wave@1.4.2-patch1
+May-07 23:01:49.533 [main] INFO  o.pf4j.DefaultPluginStatusProvider - Enabled plugins: []
+May-07 23:01:49.534 [main] INFO  o.pf4j.DefaultPluginStatusProvider - Disabled plugins: []
+May-07 23:01:49.536 [main] INFO  org.pf4j.DefaultPluginManager - PF4J version 3.12.0 in 'deployment' mode
+May-07 23:01:49.549 [main] INFO  org.pf4j.AbstractPluginManager - No plugins
+May-07 23:01:50.385 [main] WARN  nextflow.config.Manifest - Invalid config manifest attribute `contributors`
+May-07 23:01:50.406 [main] DEBUG nextflow.config.ConfigBuilder - Found config local: /Users/benjaminwehnert/dmscore/nextflow.config
+May-07 23:01:50.408 [main] DEBUG nextflow.config.ConfigBuilder - Parsing config file: /Users/benjaminwehnert/dmscore/nextflow.config
+May-07 23:01:50.426 [main] DEBUG n.secret.LocalSecretsProvider - Secrets store: /Users/benjaminwehnert/.nextflow/secrets/store.json
+May-07 23:01:50.428 [main] DEBUG nextflow.secret.SecretsLoader - Discovered secrets providers: [nextflow.secret.LocalSecretsProvider@169268a7] - activable => nextflow.secret.LocalSecretsProvider@169268a7
+May-07 23:01:50.438 [main] DEBUG nextflow.config.ConfigBuilder - Applying config profile: `test,docker`
+May-07 23:01:51.909 [main] DEBUG nextflow.config.ConfigBuilder - Available config profiles: [bih, cfc_dev, uzl_omics, ifb_core, embl_hd, denbi_qbic, alice, mjolnir_globe, uppmax, giga, incliva, ilifu, ki_luria, uge, icr_alma, rosalind_uge, lugh, mccleary, unibe_ibu, vai, czbiohub_aws, jax, roslin, ccga_med, tes, scw, unc_longleaf, tigem, tubingen_apg, google, apollo, ipop_up, vsc_calcua, pdc_kth, googlels, ceci_nic5, humantechnopole, stjude, daisybio, eddie, medair, biowulf, apptainer, bi, bigpurple, adcra, cedars, pawsey_setonix, vsc_kul_uhasselt, pawsey_nimbus, ucl_myriad, utd_ganymede, charliecloud, seattlechildrens, icr_davros, ceres, arm, munin, rosalind, hasta, cfc, uzh, shu_bmrc, ebi_codon_slurm, ebc, ccga_dx, crick, ku_sund_danhead, marvin, shifter, biohpc_gen, mana, mamba, york_viking, unc_lccc, wehi, awsbatch, wustl_htcf, arcc, ceci_dragon2, imperial, maestro, software_license, cannon, genotoul, nci_gadi, abims, janelia, nu_genomics, googlebatch, oist, sahmri, kaust, alliance_canada, mpcdf, leicester, vsc_ugent, create, sage, cambridge, jex, podman, ebi_codon, cheaha, xanadu, nyu_hpc, test, marjorie, computerome, ucd_sonic, seg_globe, mssm, sanger, dkfz, bluebear, pasteur, einstein, ethz_euler, m3c, test_full, imb, ucl_cscluster, tuos_stanage, azurebatch, hki, seadragon, crukmi, csiro_petrichor, qmul_apocrita, wave, docker, engaging, gis, hypatia, psmn, eva, unity, cropdiversityhpc, nygc, fgcz, conda, crg, singularity, mpcdf_viper, pe2, self_hosted_runner, tufts, uw_hyak_pedslabs, binac2, debug, genouest, cbe, unsw_katana, gitpod, phoenix, seawulf, uod_hpc, fub_curta, uct_hpc, aws_tower, binac, fsu_draco]
+May-07 23:01:51.958 [main] DEBUG nextflow.cli.CmdRun - Applied DSL=2 by global default
+May-07 23:01:51.972 [main] DEBUG nextflow.cli.CmdRun - Launching `./main.nf` [modest_coulomb] DSL2 - revision: 84101fc51c
+May-07 23:01:51.974 [main] DEBUG nextflow.plugin.PluginsFacade - Plugins declared=[nf-schema@2.3.0]
+May-07 23:01:51.974 [main] DEBUG nextflow.plugin.PluginsFacade - Plugins default=[]
+May-07 23:01:51.974 [main] DEBUG nextflow.plugin.PluginsFacade - Plugins resolved requirement=[nf-schema@2.3.0]
+May-07 23:01:51.975 [main] DEBUG nextflow.plugin.PluginUpdater - Installing plugin nf-schema version: 2.3.0
+May-07 23:01:51.983 [main] INFO  org.pf4j.AbstractPluginManager - Plugin 'nf-schema@2.3.0' resolved
+May-07 23:01:51.983 [main] INFO  org.pf4j.AbstractPluginManager - Start plugin 'nf-schema@2.3.0'
+May-07 23:01:51.990 [main] DEBUG nextflow.plugin.BasePlugin - Plugin started nf-schema@2.3.0
+May-07 23:01:52.045 [main] DEBUG nextflow.Session - Session UUID: 2397b75e-3882-46d7-ba2c-8549f8a2b4a6
+May-07 23:01:52.045 [main] DEBUG nextflow.Session - Run name: modest_coulomb
+May-07 23:01:52.046 [main] DEBUG nextflow.Session - Executor pool size: 8
+May-07 23:01:52.053 [main] DEBUG nextflow.file.FilePorter - File porter settings maxRetries=3; maxTransfers=50; pollTimeout=null
+May-07 23:01:52.057 [main] DEBUG nextflow.util.ThreadPoolBuilder - Creating thread pool 'FileTransfer' minSize=10; maxSize=24; workQueue=LinkedBlockingQueue[10000]; allowCoreThreadTimeout=false
+May-07 23:01:52.077 [main] DEBUG nextflow.cli.CmdRun -
+  Version: 24.04.4 build 5917
+  Created: 01-08-2024 07:05 UTC (09:05 CEST)
+  System: Mac OS X 15.0
+  Runtime: Groovy 4.0.21 on OpenJDK 64-Bit Server VM 17.0.13+0
+  Encoding: UTF-8 (UTF-8)
+  Process: 31358@MacBook-Air-von-Benjamin.local [127.0.0.1]
+  CPUs: 8 - Mem: 8 GB (94.6 MB) - Swap: 8 GB (737.5 MB)
+May-07 23:01:52.088 [main] DEBUG nextflow.Session - Work-dir: /Users/benjaminwehnert/dmscore/work [Mac OS X]
+May-07 23:01:52.088 [main] DEBUG nextflow.Session - Script base path does not exist or is not a directory: /Users/benjaminwehnert/dmscore/bin
+May-07 23:01:52.104 [main] DEBUG nextflow.executor.ExecutorFactory - Extension executors providers=[]
+May-07 23:01:52.123 [main] DEBUG nextflow.Session - Observer factory: DefaultObserverFactory
+May-07 23:01:52.143 [main] DEBUG nextflow.Session - Observer factory: ValidationObserverFactory
+May-07 23:01:52.174 [main] WARN  nextflow.config.Manifest - Invalid config manifest attribute `contributors`
+May-07 23:01:52.195 [main] DEBUG nextflow.cache.CacheFactory - Using Nextflow cache factory: nextflow.cache.DefaultCacheFactory
+May-07 23:01:52.205 [main] DEBUG nextflow.util.CustomThreadPool - Creating default thread pool > poolSize: 9; maxThreads: 1000
+May-07 23:01:52.264 [main] DEBUG nextflow.Session - Session start
+May-07 23:01:52.266 [main] DEBUG nextflow.trace.TraceFileObserver - Workflow started -- trace file: /Users/benjaminwehnert/dmscore/results/pipeline_info/execution_trace_2025-05-07_23-01-50.txt
+May-07 23:01:52.411 [main] DEBUG nextflow.script.ScriptRunner - > Launching execution
+May-07 23:01:53.516 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [paramsSummaryMap:paramsSummaryMap]; plugin Id: nf-schema
+May-07 23:01:53.917 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [paramsSummaryLog:paramsSummaryLog]; plugin Id: nf-schema
+May-07 23:01:53.918 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [validateParameters:validateParameters]; plugin Id: nf-schema
+May-07 23:01:53.920 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [paramsSummaryMap:paramsSummaryMap]; plugin Id: nf-schema
+May-07 23:01:53.921 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [samplesheetToList:samplesheetToList]; plugin Id: nf-schema
+May-07 23:01:54.131 [main] WARN  nextflow.script.ScriptBinding - Access to undefined parameter `custom_codon_library` -- Initialise it to a default value eg. `params.custom_codon_library = some_value`
+May-07 23:01:54.356 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [paramsSummaryLog:paramsSummaryLog]; plugin Id: nf-schema
+May-07 23:01:54.357 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [validateParameters:validateParameters]; plugin Id: nf-schema
+May-07 23:01:54.359 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [paramsSummaryMap:paramsSummaryMap]; plugin Id: nf-schema
+May-07 23:01:54.360 [main] DEBUG nextflow.script.IncludeDef - Loading included plugin extensions with names: [samplesheetToList:samplesheetToList]; plugin Id: nf-schema
+May-07 23:01:54.574 [main] INFO  nextflow.Nextflow -
+------------------------------------------------------
+                                        ,--./,-.
+        ___     __   __   __   ___     /,-._.--~'
+  |\ | |__  __ /  ` /  \ |__) |__         }  {
+  | \| |       \__, \__/ |  \ |___     \`-._,-`-,
+                                        `._,._,'
+  nf-core/dmscore 1.0.0dev
+------------------------------------------------------
+Input/output options
+  input                     : https://raw.githubusercontent.com/BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/samplesheet_qc_only.csv
+  outdir                    : ./results
+  min_counts                : 2
+  mutagenesis_type          : max_diff_to_wt
+
+Reference genome options
+  genome                    : R64-1-1
+  fasta                     : /Users/benjaminwehnert/GID1A_SUNi_ref_small.fasta
+
+Institutional config options
+  config_profile_name       : Test profile
+  config_profile_description: Minimal test dataset to check pipeline function
+
+Generic options
+  trace_report_suffix       : 2025-05-07_23-01-50
+
+Core Nextflow options
+  runName                   : modest_coulomb
+  containerEngine           : docker
+  launchDir                 : /Users/benjaminwehnert/dmscore
+  workDir                   : /Users/benjaminwehnert/dmscore/work
+  projectDir                : /Users/benjaminwehnert/dmscore
+  userName                  : benjaminwehnert
+  profile                   : test,docker
+  configFiles               : /Users/benjaminwehnert/dmscore/nextflow.config
+
+!! Only displaying parameters that differ from the pipeline defaults !!
+------------------------------------------------------
+* The nf-core framework
+    https://doi.org/10.1038/s41587-020-0439-x
+
+* Software dependencies
+    https://github.com/nf-core/dmscore/blob/master/CITATIONS.md
+
+May-07 23:01:54.576 [main] DEBUG n.validation.ValidationExtension - Starting parameters validation
+May-07 23:01:54.920 [main] DEBUG nextflow.validation.SchemaEvaluator - Started validating /BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/samplesheet_qc_only.csv
+May-07 23:01:55.945 [main] DEBUG nextflow.validation.SchemaEvaluator - Validation of file 'https://raw.githubusercontent.com/BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/samplesheet_qc_only.csv' passed!
+May-07 23:01:56.095 [main] DEBUG n.v.FormatDirectoryPathEvaluator - Cloud blob storage paths are not supported by 'FormatDirectoryPathEvaluator': 's3://ngi-igenomes/igenomes/'
+May-07 23:01:56.099 [main] DEBUG n.validation.ValidationExtension - Finishing parameters validation
+May-07 23:01:56.178 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_medium` matches labels `process_medium` for process with name NFCORE_DMSCORE:DMSCORE:FASTQC
+May-07 23:01:56.182 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:FASTQC` matches process NFCORE_DMSCORE:DMSCORE:FASTQC
+May-07 23:01:56.196 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.196 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.202 [main] DEBUG nextflow.executor.Executor - [warm up] executor > local
+May-07 23:01:56.206 [main] DEBUG n.processor.LocalPollingMonitor - Creating local task monitor for executor 'local' > cpus=8; memory=8 GB; capacity=8; pollInterval=100ms; dumpInterval=5m
+May-07 23:01:56.209 [main] DEBUG n.processor.TaskPollingMonitor - >>> barrier register (monitor: local)
+May-07 23:01:56.359 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_single` matches labels `process_single` for process with name NFCORE_DMSCORE:DMSCORE:MULTIQC
+May-07 23:01:56.360 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:MULTIQC` matches process NFCORE_DMSCORE:DMSCORE:MULTIQC
+May-07 23:01:56.362 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.363 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.372 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_single` matches labels `process_single` for process with name NFCORE_DMSCORE:DMSCORE:BWA_INDEX
+May-07 23:01:56.373 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:BWA_INDEX` matches process NFCORE_DMSCORE:DMSCORE:BWA_INDEX
+May-07 23:01:56.375 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.375 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.391 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_high` matches labels `process_high` for process with name NFCORE_DMSCORE:DMSCORE:BWA_MEM
+May-07 23:01:56.391 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:BWA_MEM` matches process NFCORE_DMSCORE:DMSCORE:BWA_MEM
+May-07 23:01:56.394 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.395 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.404 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_single` matches labels `process_single` for process with name NFCORE_DMSCORE:DMSCORE:BAMFILTER_DMS
+May-07 23:01:56.406 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:BAMFILTER_DMS` matches process NFCORE_DMSCORE:DMSCORE:BAMFILTER_DMS
+May-07 23:01:56.409 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.409 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.418 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_medium` matches labels `process_medium` for process with name NFCORE_DMSCORE:DMSCORE:PREMERGE
+May-07 23:01:56.418 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:PREMERGE` matches process NFCORE_DMSCORE:DMSCORE:PREMERGE
+May-07 23:01:56.420 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.420 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.429 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_high` matches labels `process_high` for process with name NFCORE_DMSCORE:DMSCORE:GATK_SATURATIONMUTAGENESIS
+May-07 23:01:56.430 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:GATK_SATURATIONMUTAGENESIS` matches process NFCORE_DMSCORE:DMSCORE:GATK_SATURATIONMUTAGENESIS
+May-07 23:01:56.433 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.433 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.443 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_single` matches labels `process_single` for process with name NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ
+May-07 23:01:56.443 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:DMSANALYSIS_AASEQ` matches process NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ
+May-07 23:01:56.446 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.446 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.458 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_single` matches labels `process_single` for process with name NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS
+May-07 23:01:56.459 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:DMSANALYSIS_POSSIBLE_MUTATIONS` matches process NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS
+May-07 23:01:56.461 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.461 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.480 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withLabel:process_single` matches labels `process_single` for process with name NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_PROCESS_GATK
+May-07 23:01:56.481 [main] DEBUG nextflow.script.ProcessConfig - Config settings `withName:DMSANALYSIS_PROCESS_GATK` matches process NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_PROCESS_GATK
+May-07 23:01:56.484 [main] DEBUG nextflow.executor.ExecutorFactory - << taskConfig executor: null
+May-07 23:01:56.484 [main] DEBUG nextflow.executor.ExecutorFactory - >> processorType: 'local'
+May-07 23:01:56.499 [main] DEBUG nextflow.Session - Config process names validation disabled as requested
+May-07 23:01:56.500 [main] DEBUG nextflow.Session - Igniting dataflow network (43)
+May-07 23:01:56.508 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:FASTQC
+May-07 23:01:56.508 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:MULTIQC
+May-07 23:01:56.509 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:BWA_INDEX
+May-07 23:01:56.509 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:BWA_MEM
+May-07 23:01:56.511 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:BAMFILTER_DMS
+May-07 23:01:56.512 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:PREMERGE
+May-07 23:01:56.512 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:GATK_SATURATIONMUTAGENESIS
+May-07 23:01:56.512 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ
+May-07 23:01:56.512 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS
+May-07 23:01:56.512 [main] DEBUG nextflow.processor.TaskProcessor - Starting process > NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_PROCESS_GATK
+May-07 23:01:56.514 [main] DEBUG nextflow.script.ScriptRunner - Parsed script files:
+  Script_e5f965e09aa3641b: /Users/benjaminwehnert/dmscore/./workflows/../modules/local/bamprocessing/premerge.nf
+  Script_113f045ba19c2c46: /Users/benjaminwehnert/dmscore/./workflows/../modules/nf-core/fastqc/main.nf
+  Script_4f612cfd8c52e8cd: /Users/benjaminwehnert/dmscore/./subworkflows/local/utils_nfcore_dmscore_pipeline/../../nf-core/utils_nextflow_pipeline/main.nf
+  Script_215a636da7ab24a2: /Users/benjaminwehnert/dmscore/./workflows/../subworkflows/local/utils_nfcore_dmscore_pipeline/../../nf-core/utils_nfschema_plugin/main.nf
+  Script_68606fccdecc54d9: /Users/benjaminwehnert/dmscore/./subworkflows/local/utils_nfcore_dmscore_pipeline/main.nf
+  Script_0f3077e98f6a93f6: /Users/benjaminwehnert/dmscore/./workflows/../modules/nf-core/bwa/mem/main.nf
+  Script_5c4e8d4051efa81e: /Users/benjaminwehnert/dmscore/./subworkflows/local/utils_nfcore_dmscore_pipeline/../../nf-core/utils_nfcore_pipeline/main.nf
+  Script_3bf8120d0b5bcde1: /Users/benjaminwehnert/dmscore/./workflows/../modules/local/dmsanalysis/possiblemutations.nf
+  Script_d65a0ba0a319d7db: /Users/benjaminwehnert/dmscore/./workflows/../modules/local/dmsanalysis/aaseq.nf
+  Script_4ff7366a79e0ef06: /Users/benjaminwehnert/dmscore/./workflows/../modules/local/bamprocessing/bamfilteringdms.nf
+  Script_c568aae5b239c1c5: /Users/benjaminwehnert/dmscore/main.nf
+  Script_09ccfa79b2802f41: /Users/benjaminwehnert/dmscore/./workflows/dmscore.nf
+  Script_4955288afd8ca61e: /Users/benjaminwehnert/dmscore/./workflows/../modules/nf-core/multiqc/main.nf
+  Script_a1878766b1a6b241: /Users/benjaminwehnert/dmscore/./workflows/../modules/local/dmsanalysis/processgatk.nf
+  Script_37dcd664b2773148: /Users/benjaminwehnert/dmscore/./workflows/../modules/local/gatk/saturationmutagenesis.nf
+  Script_25aa31c18d513c61: /Users/benjaminwehnert/dmscore/./workflows/../modules/nf-core/bwa/index/main.nf
+May-07 23:01:56.514 [main] DEBUG nextflow.script.ScriptRunner - > Awaiting termination
+May-07 23:01:56.514 [main] DEBUG nextflow.Session - Session await
+May-07 23:01:56.605 [Actor Thread 1] DEBUG nextflow.sort.BigSort - Sort completed -- entries: 1; slices: 1; internal sort time: 0.001 s; external sort time: 0.011 s; total time: 0.012 s
+May-07 23:01:56.605 [Actor Thread 2] DEBUG nextflow.sort.BigSort - Sort completed -- entries: 1; slices: 1; internal sort time: 0.001 s; external sort time: 0.011 s; total time: 0.012 s
+May-07 23:01:56.611 [Actor Thread 2] DEBUG nextflow.file.FileCollector - Saved collect-files list to: /Users/benjaminwehnert/dmscore/work/collect-file/4640128d9cfd4a45636425a4f43db374
+May-07 23:01:56.611 [Actor Thread 1] DEBUG nextflow.file.FileCollector - Saved collect-files list to: /Users/benjaminwehnert/dmscore/work/collect-file/04a58cc29ef1c76e5af4e2fb20a13ad7
+May-07 23:01:56.619 [Actor Thread 1] DEBUG nextflow.file.FileCollector - Deleting file collector temp dir: /var/folders/r0/ldrzd4wn1s3516hy0vsn8xzm0000gn/T/nxf-1651345332501275985
+May-07 23:01:56.619 [Actor Thread 2] DEBUG nextflow.file.FileCollector - Deleting file collector temp dir: /var/folders/r0/ldrzd4wn1s3516hy0vsn8xzm0000gn/T/nxf-12321695686053957190
+May-07 23:01:56.623 [Actor Thread 3] DEBUG nextflow.util.HashBuilder - [WARN] Unknown hashing type: class Script_09ccfa79b2802f41$_runScript_closure1$_closure26
+May-07 23:01:56.624 [Actor Thread 14] DEBUG nextflow.util.HashBuilder - Unable to get file attributes file: /NULL -- Cause: java.nio.file.NoSuchFileException: /NULL
+May-07 23:01:56.697 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:01:56.698 [Task submitter] INFO  nextflow.Session - [d4/e141d7] Submitted process > NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ (amino_acid_sequence)
+May-07 23:01:56.730 [FileTransfer-2] DEBUG nextflow.file.FilePorter - Copying foreign file https://raw.githubusercontent.com/BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/pMS190_GID1A_SUNi_S2_1_R2_50k.fastq to work dir: /Users/benjaminwehnert/dmscore/work/stage-2397b75e-3882-46d7-ba2c-8549f8a2b4a6/6a/8815724cee5e2e4e17200e98e8c0ad/pMS190_GID1A_SUNi_S2_1_R2_50k.fastq
+May-07 23:01:56.730 [FileTransfer-1] DEBUG nextflow.file.FilePorter - Copying foreign file https://raw.githubusercontent.com/BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/pMS190_GID1A_SUNi_S2_1_R1_50k.fastq to work dir: /Users/benjaminwehnert/dmscore/work/stage-2397b75e-3882-46d7-ba2c-8549f8a2b4a6/40/dfe72a8fa5ce1b038fdf1aa1fb4749/pMS190_GID1A_SUNi_S2_1_R1_50k.fastq
+May-07 23:01:58.744 [Actor Thread 15] INFO  nextflow.file.FilePorter - Staging foreign file: https://raw.githubusercontent.com/BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/pMS190_GID1A_SUNi_S2_1_R1_50k.fastq
+May-07 23:02:00.581 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 3; name: NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ (amino_acid_sequence); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/d4/e141d76a7c3b4130080bcdf8a831a3]
+May-07 23:02:00.585 [Task monitor] DEBUG nextflow.util.ThreadPoolBuilder - Creating thread pool 'TaskFinalizer' minSize=10; maxSize=24; workQueue=LinkedBlockingQueue[10000]; allowCoreThreadTimeout=false
+May-07 23:02:00.612 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:00.613 [Task submitter] INFO  nextflow.Session - [49/349fd9] Submitted process > NFCORE_DMSCORE:DMSCORE:BWA_INDEX (GID1A_SUNi_ref_small.fasta)
+May-07 23:02:00.663 [TaskFinalizer-1] DEBUG nextflow.util.ThreadPoolBuilder - Creating thread pool 'PublishDir' minSize=10; maxSize=24; workQueue=LinkedBlockingQueue[10000]; allowCoreThreadTimeout=false
+May-07 23:02:00.756 [Actor Thread 15] INFO  nextflow.file.FilePorter - Staging foreign file: https://raw.githubusercontent.com/BenjaminWehnert1008/test-datasets/dmsqc/dmsqc/pMS190_GID1A_SUNi_S2_1_R2_50k.fastq
+May-07 23:02:00.757 [Actor Thread 4] WARN  nextflow.processor.TaskContext - Cannot serialize context map. Cause: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue -- Resume will not work on this process
+May-07 23:02:00.763 [Actor Thread 4] DEBUG nextflow.processor.TaskContext - Failed to serialize delegate map items: [
+  'meta':[Script_09ccfa79b2802f41$_runScript_closure1$_closure26] =
+  'pos_range':[java.lang.String] = 352-1383
+  '$':[java.lang.Boolean] = true
+  'wt_seq':[nextflow.processor.TaskPath] = GID1A_SUNi_ref_small.fasta
+  'script':[nextflow.processor.TaskPath] = aa_seq.R
+  'task':[nextflow.processor.TaskConfig] = [container:community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-biocmanager_r-dplyr_pruned:0fd2e39a5bf2ecaa, withName:PREMERGE:[publishDir:[path:./results/intermediate_files/bam_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure9$_closure21@26dc7ffc]], memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure15@13f9d575, withName:FASTQC:[ext:[args:--quiet], containerOptions:], withLabel:error_retry:[errorStrategy:retry, maxRetries:2], when:nextflow.script.TaskClosure@264d7648, withLabel:process_high:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure9$_closure23@1b36f3f3, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure9$_closure24@5e504cf6, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure9$_closure25@35f6834], withLabel:error_ignore:[errorStrategy:ignore], resourceLimits:[cpus:4, memory:8.GB, time:1.h], withName:DMSANALYSIS_AASEQ:[publishDir:[path:./results/intermediate_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure13$_closure25@4eeda121]], withLabel:process_high_memory:[memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure11$_closure27@62e86a64], withName:BAMFILTER_DMS:[publishDir:[path:./results/intermediate_files/bam_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure8$_closure20@3e03bd33]], publishDir:[[path:./results/intermediate_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure13$_closure25@4eeda121]], withName:BWA_MEM:[publishDir:[path:./results/intermediate_files/bam_files/bwa/mem, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure7$_closure19@46e56c0f]], executor:local, stub:nextflow.script.TaskClosure@6522395b, conda:null/environment.yml, withName:GATK_SATURATIONMUTAGENESIS:[publishDir:[path:./results/intermediate_files/gatk, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure10$_closure22@4438d4d1]], cacheable:true, withLabel:process_low:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure7$_closure17@67f11340, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure7$_closure18@a998ea5, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure7$_closure19@7e85964c], withLabel:process_medium:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure8$_closure20@7c995b11, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure8$_closure21@131d3cd1, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure8$_closure22@55b774b1], tag:amino_acid_sequence, withName:MULTIQC:[ext:[args:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure5$_closure15@7d2bfbd], publishDir:[path:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure5$_closure16@31a52d85, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure5$_closure17@4ba464d4]], workDir:/Users/benjaminwehnert/dmscore/work/d4/e141d76a7c3b4130080bcdf8a831a3, exitStatus:0, ext:[:], withLabel:process_single:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure14@255893ed, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure15@13f9d575, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure16@37e5efac], withName:BWA_INDEX:[publishDir:[path:./results/intermediate_files/bam_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure6$_closure18@2f3435d0]], process:NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ, debug:false, cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure14@255893ed, index:1, label:[process_single], withLabel:process_long:[time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure10$_closure26@475e6626], maxRetries:1, maxErrors:-1, shell:bash
+
+set -e # Exit if a tool returns a non-zero status/exit code
+set -u # Treat unset variables and parameters as an error
+set -o pipefail # Returns the status of the last command to exit with a non-zero status or zero if all successfully execute
+set -C # No clobber - prevent output redirection from overwriting files.
+, withName:DMSANALYSIS_POSSIBLE_MUTATIONS:[publishDir:[path:./results/intermediate_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure11$_closure23@267852db]], name:NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_AASEQ (amino_acid_sequence), containerOptions:-u $(id -u):$(id -g), errorStrategy:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure5@3e785137, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure16@37e5efac, withName:DMSANALYSIS_PROCESS_GATK:[publishDir:[path:./results/intermediate_files/processed_gatk_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure14$_closure26@30ec799d]], hash:d4e141d76a7c3b4130080bcdf8a831a3]
+]
+com.esotericsoftware.kryo.KryoException: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue
+Serialization trace:
+queue (org.codehaus.groovy.util.ReferenceManager$CallBackedManager)
+manager (org.codehaus.groovy.util.ReferenceManager$1)
+manager (org.codehaus.groovy.util.ReferenceBundle)
+bundle (org.codehaus.groovy.reflection.CachedClass$4)
+cachedSuperClass (org.codehaus.groovy.reflection.stdclasses.ObjectCachedClass)
+cachedClass (org.codehaus.groovy.reflection.CachedMethod)
+allMethods (groovy.lang.MetaClassImpl)
+delegate (groovy.runtime.metaclass.NextflowDelegatingMetaClass)
+metaClass (groovyx.gpars.dataflow.DataflowVariable)
+first (groovyx.gpars.dataflow.stream.DataflowStream)
+asyncHead (groovyx.gpars.dataflow.stream.DataflowStreamReadAdapter)
+source (nextflow.extension.MapOp)
+owner (nextflow.extension.MapOp$_apply_closure1)
+code (groovyx.gpars.dataflow.operator.DataflowOperatorActor)
+actor (groovyx.gpars.dataflow.operator.DataflowOperator)
+allOperators (nextflow.Session)
+session (nextflow.validation.ValidationExtension)
+target (nextflow.script.FunctionDef)
+definitions (nextflow.script.ScriptMeta)
+meta (nextflow.script.ScriptBinding)
+binding (Script_09ccfa79b2802f41)
+delegate (Script_09ccfa79b2802f41$_runScript_closure1)
+delegate (Script_09ccfa79b2802f41$_runScript_closure1$_closure26)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:82)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:82)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:22)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:82)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:22)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:95)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:21)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:95)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:21)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at org.codehaus.groovy.vmplugin.v8.IndyInterface.fromCache(IndyInterface.java:321)
+	at nextflow.util.KryoHelper.serialize(SerializationHelper.groovy:166)
+	at org.codehaus.groovy.vmplugin.v8.IndyInterface.fromCache(IndyInterface.java:321)
+	at nextflow.processor.TaskContext.serialize(TaskContext.groovy:198)
+	at nextflow.cache.CacheDB.writeTaskEntry0(CacheDB.groovy:148)
+	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
+	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
+	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
+	at java.base/java.lang.reflect.Method.invoke(Method.java:569)
+	at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:343)
+	at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:328)
+	at groovy.lang.MetaClassImpl.doInvokeMethod(MetaClassImpl.java:1333)
+	at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1088)
+	at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1007)
+	at org.codehaus.groovy.runtime.InvokerHelper.invokePogoMethod(InvokerHelper.java:645)
+	at org.codehaus.groovy.runtime.InvokerHelper.invokeMethod(InvokerHelper.java:628)
+	at org.codehaus.groovy.runtime.InvokerHelper.invokeMethodSafe(InvokerHelper.java:82)
+	at nextflow.cache.CacheDB$_putTaskAsync_closure1.doCall(CacheDB.groovy:157)
+	at nextflow.cache.CacheDB$_putTaskAsync_closure1.call(CacheDB.groovy)
+	at groovyx.gpars.agent.AgentBase.onMessage(AgentBase.java:102)
+	at groovyx.gpars.agent.Agent.handleMessage(Agent.java:84)
+	at groovyx.gpars.agent.AgentCore$1.handleMessage(AgentCore.java:48)
+	at groovyx.gpars.util.AsyncMessagingCore.run(AsyncMessagingCore.java:132)
+	at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
+	at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
+	at java.base/java.lang.Thread.run(Thread.java:840)
+Caused by: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue
+	at com.esotericsoftware.kryo.factories.ReflectionSerializerFactory.makeSerializer(ReflectionSerializerFactory.java:48)
+	at com.esotericsoftware.kryo.factories.ReflectionSerializerFactory.makeSerializer(ReflectionSerializerFactory.java:26)
+	at com.esotericsoftware.kryo.Kryo.newDefaultSerializer(Kryo.java:351)
+	at com.esotericsoftware.kryo.Kryo.getDefaultSerializer(Kryo.java:344)
+	at com.esotericsoftware.kryo.util.DefaultClassResolver.registerImplicit(DefaultClassResolver.java:56)
+	at com.esotericsoftware.kryo.Kryo.getRegistration(Kryo.java:461)
+	at com.esotericsoftware.kryo.util.DefaultClassResolver.writeClass(DefaultClassResolver.java:79)
+	at com.esotericsoftware.kryo.Kryo.writeClass(Kryo.java:488)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:57)
+	... 106 common frames omitted
+Caused by: java.lang.reflect.InvocationTargetException: null
+	at jdk.internal.reflect.GeneratedConstructorAccessor39.newInstance(Unknown Source)
+	at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
+	at java.base/java.lang.reflect.Constructor.newInstanceWithCaller(Constructor.java:500)
+	at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:481)
+	at com.esotericsoftware.kryo.factories.ReflectionSerializerFactory.makeSerializer(ReflectionSerializerFactory.java:35)
+	... 114 common frames omitted
+Caused by: java.lang.reflect.InaccessibleObjectException: Unable to make field private final java.lang.ref.ReferenceQueue$Lock java.lang.ref.ReferenceQueue.lock accessible: module java.base does not "opens java.lang.ref" to unnamed module @7b02881e
+	at java.base/java.lang.reflect.AccessibleObject.checkCanSetAccessible(AccessibleObject.java:354)
+	at java.base/java.lang.reflect.AccessibleObject.checkCanSetAccessible(AccessibleObject.java:297)
+	at java.base/java.lang.reflect.Field.checkCanSetAccessible(Field.java:178)
+	at java.base/java.lang.reflect.Field.setAccessible(Field.java:172)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.buildValidFields(FieldSerializer.java:282)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.rebuildCachedFields(FieldSerializer.java:217)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.rebuildCachedFields(FieldSerializer.java:156)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.<init>(FieldSerializer.java:133)
+	... 119 common frames omitted
+May-07 23:02:02.378 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 1; name: NFCORE_DMSCORE:DMSCORE:BWA_INDEX (GID1A_SUNi_ref_small.fasta); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/49/349fd93ff7dd98e92e426a30457fb9]
+May-07 23:02:02.387 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:02.388 [Task submitter] INFO  nextflow.Session - [36/b138e8] Submitted process > NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS (table /w all possible variants)
+May-07 23:02:02.422 [Actor Thread 8] WARN  nextflow.processor.TaskContext - Cannot serialize context map. Cause: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue -- Resume will not work on this process
+May-07 23:02:13.067 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 2; name: NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS (table /w all possible variants); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/36/b138e8e27f7423eea98b17aaf74451]
+May-07 23:02:13.085 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:13.085 [Task submitter] INFO  nextflow.Session - [67/c2a06b] Submitted process > NFCORE_DMSCORE:DMSCORE:BWA_MEM (gid1a_1_quality_1_pe)
+May-07 23:02:13.133 [Actor Thread 6] WARN  nextflow.processor.TaskContext - Cannot serialize context map. Cause: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue -- Resume will not work on this process
+May-07 23:02:13.136 [Actor Thread 6] DEBUG nextflow.processor.TaskContext - Failed to serialize delegate map items: [
+  'meta':[Script_09ccfa79b2802f41$_runScript_closure1$_closure26] =
+  'pos_range':[java.lang.String] = 352-1383
+  'mutagenesis_type':[java.lang.String] = max_diff_to_wt
+  '$':[java.lang.Boolean] = true
+  'wt_seq':[nextflow.processor.TaskPath] = GID1A_SUNi_ref_small.fasta
+  'custom_codon_library':[nextflow.processor.TaskPath] = NULL
+  'script':[nextflow.processor.TaskPath] = possible_mutations.R
+  'task':[nextflow.processor.TaskConfig] = [container:community.wave.seqera.io/library/bioconductor-biostrings_r-base_r-biocmanager_r-dplyr_pruned:0fd2e39a5bf2ecaa, withName:PREMERGE:[publishDir:[path:./results/intermediate_files/bam_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure9$_closure21@26dc7ffc]], memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure15@13f9d575, withName:FASTQC:[ext:[args:--quiet], containerOptions:], withLabel:error_retry:[errorStrategy:retry, maxRetries:2], when:nextflow.script.TaskClosure@5f4899a5, withLabel:process_high:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure9$_closure23@1b36f3f3, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure9$_closure24@5e504cf6, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure9$_closure25@35f6834], withLabel:error_ignore:[errorStrategy:ignore], resourceLimits:[cpus:4, memory:8.GB, time:1.h], withName:DMSANALYSIS_AASEQ:[publishDir:[path:./results/intermediate_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure13$_closure25@4eeda121]], withLabel:process_high_memory:[memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure11$_closure27@62e86a64], withName:BAMFILTER_DMS:[publishDir:[path:./results/intermediate_files/bam_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure8$_closure20@3e03bd33]], publishDir:[[path:./results/intermediate_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure11$_closure23@267852db]], withName:BWA_MEM:[publishDir:[path:./results/intermediate_files/bam_files/bwa/mem, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure7$_closure19@46e56c0f]], executor:local, stub:nextflow.script.TaskClosure@7069093e, conda:null/environment.yml, withName:GATK_SATURATIONMUTAGENESIS:[publishDir:[path:./results/intermediate_files/gatk, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure10$_closure22@4438d4d1]], cacheable:true, withLabel:process_low:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure7$_closure17@67f11340, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure7$_closure18@a998ea5, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure7$_closure19@7e85964c], withLabel:process_medium:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure8$_closure20@7c995b11, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure8$_closure21@131d3cd1, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure8$_closure22@55b774b1], tag:table /w all possible variants, withName:MULTIQC:[ext:[args:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure5$_closure15@7d2bfbd], publishDir:[path:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure5$_closure16@31a52d85, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure5$_closure17@4ba464d4]], workDir:/Users/benjaminwehnert/dmscore/work/36/b138e8e27f7423eea98b17aaf74451, exitStatus:0, ext:[:], withLabel:process_single:[cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure14@255893ed, memory:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure15@13f9d575, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure16@37e5efac], withName:BWA_INDEX:[publishDir:[path:./results/intermediate_files/bam_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure6$_closure18@2f3435d0]], process:NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS, debug:false, cpus:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure14@255893ed, index:1, label:[process_single], withLabel:process_long:[time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure10$_closure26@475e6626], maxRetries:1, maxErrors:-1, shell:bash
+
+set -e # Exit if a tool returns a non-zero status/exit code
+set -u # Treat unset variables and parameters as an error
+set -o pipefail # Returns the status of the last command to exit with a non-zero status or zero if all successfully execute
+set -C # No clobber - prevent output redirection from overwriting files.
+, withName:DMSANALYSIS_POSSIBLE_MUTATIONS:[publishDir:[path:./results/intermediate_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure11$_closure23@267852db]], name:NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_POSSIBLE_MUTATIONS (table /w all possible variants), containerOptions:-u $(id -u):$(id -g), errorStrategy:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure5@3e785137, time:Script126E8BAA3BC9692B58F2A1C965A1D472$_run_closure1$_closure6$_closure16@37e5efac, withName:DMSANALYSIS_PROCESS_GATK:[publishDir:[path:./results/intermediate_files/processed_gatk_files, mode:copy, saveAs:ScriptE8646A4B8FFA7429020F836DC9CB8146$_run_closure1$_closure14$_closure26@30ec799d]], hash:36b138e8e27f7423eea98b17aaf74451]
+]
+com.esotericsoftware.kryo.KryoException: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue
+Serialization trace:
+queue (org.codehaus.groovy.util.ReferenceManager$CallBackedManager)
+manager (org.codehaus.groovy.util.ReferenceManager$1)
+manager (org.codehaus.groovy.util.ReferenceBundle)
+bundle (org.codehaus.groovy.reflection.CachedClass$4)
+cachedSuperClass (org.codehaus.groovy.reflection.stdclasses.ObjectCachedClass)
+cachedClass (org.codehaus.groovy.reflection.CachedMethod)
+allMethods (groovy.lang.MetaClassImpl)
+delegate (groovy.runtime.metaclass.NextflowDelegatingMetaClass)
+metaClass (groovyx.gpars.dataflow.DataflowVariable)
+first (groovyx.gpars.dataflow.stream.DataflowStream)
+asyncHead (groovyx.gpars.dataflow.stream.DataflowStreamReadAdapter)
+source (nextflow.extension.MapOp)
+owner (nextflow.extension.MapOp$_apply_closure1)
+code (groovyx.gpars.dataflow.operator.DataflowOperatorActor)
+actor (groovyx.gpars.dataflow.operator.DataflowOperator)
+allOperators (nextflow.Session)
+session (nextflow.validation.ValidationExtension)
+target (nextflow.script.FunctionDef)
+definitions (nextflow.script.ScriptMeta)
+meta (nextflow.script.ScriptBinding)
+binding (Script_09ccfa79b2802f41)
+delegate (Script_09ccfa79b2802f41$_runScript_closure1)
+delegate (Script_09ccfa79b2802f41$_runScript_closure1$_closure26)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:82)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:82)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:22)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:82)
+	at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:22)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:95)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:21)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:523)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:61)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.write(FieldSerializer.java:495)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:95)
+	at com.esotericsoftware.kryo.serializers.MapSerializer.write(MapSerializer.java:21)
+	at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:599)
+	at org.codehaus.groovy.vmplugin.v8.IndyInterface.fromCache(IndyInterface.java:321)
+	at nextflow.util.KryoHelper.serialize(SerializationHelper.groovy:166)
+	at org.codehaus.groovy.vmplugin.v8.IndyInterface.fromCache(IndyInterface.java:321)
+	at nextflow.processor.TaskContext.serialize(TaskContext.groovy:198)
+	at nextflow.cache.CacheDB.writeTaskEntry0(CacheDB.groovy:148)
+	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
+	at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
+	at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
+	at java.base/java.lang.reflect.Method.invoke(Method.java:569)
+	at org.codehaus.groovy.reflection.CachedMethod.invoke(CachedMethod.java:343)
+	at groovy.lang.MetaMethod.doMethodInvoke(MetaMethod.java:328)
+	at groovy.lang.MetaClassImpl.doInvokeMethod(MetaClassImpl.java:1333)
+	at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1088)
+	at groovy.lang.MetaClassImpl.invokeMethod(MetaClassImpl.java:1007)
+	at org.codehaus.groovy.runtime.InvokerHelper.invokePogoMethod(InvokerHelper.java:645)
+	at org.codehaus.groovy.runtime.InvokerHelper.invokeMethod(InvokerHelper.java:628)
+	at org.codehaus.groovy.runtime.InvokerHelper.invokeMethodSafe(InvokerHelper.java:82)
+	at nextflow.cache.CacheDB$_putTaskAsync_closure1.doCall(CacheDB.groovy:157)
+	at nextflow.cache.CacheDB$_putTaskAsync_closure1.call(CacheDB.groovy)
+	at groovyx.gpars.agent.AgentBase.onMessage(AgentBase.java:102)
+	at groovyx.gpars.agent.Agent.handleMessage(Agent.java:84)
+	at groovyx.gpars.agent.AgentCore$1.handleMessage(AgentCore.java:48)
+	at groovyx.gpars.util.AsyncMessagingCore.run(AsyncMessagingCore.java:132)
+	at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)
+	at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
+	at java.base/java.lang.Thread.run(Thread.java:840)
+Caused by: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue
+	at com.esotericsoftware.kryo.factories.ReflectionSerializerFactory.makeSerializer(ReflectionSerializerFactory.java:48)
+	at com.esotericsoftware.kryo.factories.ReflectionSerializerFactory.makeSerializer(ReflectionSerializerFactory.java:26)
+	at com.esotericsoftware.kryo.Kryo.newDefaultSerializer(Kryo.java:351)
+	at com.esotericsoftware.kryo.Kryo.getDefaultSerializer(Kryo.java:344)
+	at com.esotericsoftware.kryo.util.DefaultClassResolver.registerImplicit(DefaultClassResolver.java:56)
+	at com.esotericsoftware.kryo.Kryo.getRegistration(Kryo.java:461)
+	at com.esotericsoftware.kryo.util.DefaultClassResolver.writeClass(DefaultClassResolver.java:79)
+	at com.esotericsoftware.kryo.Kryo.writeClass(Kryo.java:488)
+	at com.esotericsoftware.kryo.serializers.ObjectField.write(ObjectField.java:57)
+	... 106 common frames omitted
+Caused by: java.lang.reflect.InvocationTargetException: null
+	at jdk.internal.reflect.GeneratedConstructorAccessor39.newInstance(Unknown Source)
+	at java.base/jdk.internal.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
+	at java.base/java.lang.reflect.Constructor.newInstanceWithCaller(Constructor.java:500)
+	at java.base/java.lang.reflect.Constructor.newInstance(Constructor.java:481)
+	at com.esotericsoftware.kryo.factories.ReflectionSerializerFactory.makeSerializer(ReflectionSerializerFactory.java:35)
+	... 114 common frames omitted
+Caused by: java.lang.reflect.InaccessibleObjectException: Unable to make field private final java.lang.ref.ReferenceQueue$Lock java.lang.ref.ReferenceQueue.lock accessible: module java.base does not "opens java.lang.ref" to unnamed module @7b02881e
+	at java.base/java.lang.reflect.AccessibleObject.checkCanSetAccessible(AccessibleObject.java:354)
+	at java.base/java.lang.reflect.AccessibleObject.checkCanSetAccessible(AccessibleObject.java:297)
+	at java.base/java.lang.reflect.Field.checkCanSetAccessible(Field.java:178)
+	at java.base/java.lang.reflect.Field.setAccessible(Field.java:172)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.buildValidFields(FieldSerializer.java:282)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.rebuildCachedFields(FieldSerializer.java:217)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.rebuildCachedFields(FieldSerializer.java:156)
+	at com.esotericsoftware.kryo.serializers.FieldSerializer.<init>(FieldSerializer.java:133)
+	... 119 common frames omitted
+May-07 23:02:17.570 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 5; name: NFCORE_DMSCORE:DMSCORE:BWA_MEM (gid1a_1_quality_1_pe); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/67/c2a06b1c7b4781da339f3e7aacd574]
+May-07 23:02:17.585 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:17.586 [Task submitter] INFO  nextflow.Session - [48/c69654] Submitted process > NFCORE_DMSCORE:DMSCORE:FASTQC (gid1a_1_quality_1_pe)
+May-07 23:02:17.748 [TaskFinalizer-4] DEBUG nextflow.processor.TaskProcessor - Process NFCORE_DMSCORE:DMSCORE:BWA_MEM > Skipping output binding because one or more optional files are missing: fileoutparam<1:1>
+May-07 23:02:17.749 [TaskFinalizer-4] DEBUG nextflow.processor.TaskProcessor - Process NFCORE_DMSCORE:DMSCORE:BWA_MEM > Skipping output binding because one or more optional files are missing: fileoutparam<2:1>
+May-07 23:02:17.749 [TaskFinalizer-4] DEBUG nextflow.processor.TaskProcessor - Process NFCORE_DMSCORE:DMSCORE:BWA_MEM > Skipping output binding because one or more optional files are missing: fileoutparam<3:1>
+May-07 23:02:17.828 [Actor Thread 14] WARN  nextflow.processor.TaskContext - Cannot serialize context map. Cause: java.lang.IllegalArgumentException: Unable to create serializer "com.esotericsoftware.kryo.serializers.FieldSerializer" for class: java.lang.ref.ReferenceQueue -- Resume will not work on this process
+May-07 23:02:28.533 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 4; name: NFCORE_DMSCORE:DMSCORE:FASTQC (gid1a_1_quality_1_pe); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/48/c69654e9b29a194ea81b7c28f63819]
+May-07 23:02:28.582 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:28.583 [Task submitter] INFO  nextflow.Session - [4e/7ee9bd] Submitted process > NFCORE_DMSCORE:DMSCORE:BAMFILTER_DMS (gid1a_1_quality_1_pe)
+May-07 23:02:28.752 [Actor Thread 12] DEBUG nextflow.sort.BigSort - Sort completed -- entries: 2; slices: 1; internal sort time: 0.013 s; external sort time: 0.008 s; total time: 0.021 s
+May-07 23:02:28.776 [Actor Thread 12] DEBUG nextflow.file.FileCollector - Saved collect-files list to: /Users/benjaminwehnert/dmscore/work/collect-file/c1149f4a215bbe0651400d7f32cdafdc
+May-07 23:02:28.784 [Actor Thread 12] DEBUG nextflow.file.FileCollector - Deleting file collector temp dir: /var/folders/r0/ldrzd4wn1s3516hy0vsn8xzm0000gn/T/nxf-15345989576056151242
+May-07 23:02:32.866 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 6; name: NFCORE_DMSCORE:DMSCORE:BAMFILTER_DMS (gid1a_1_quality_1_pe); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/4e/7ee9bd66291d03ed6f3c2bbdfefe0d]
+May-07 23:02:32.872 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:32.873 [Task submitter] INFO  nextflow.Session - [55/5dd043] Submitted process > NFCORE_DMSCORE:DMSCORE:MULTIQC
+May-07 23:02:43.262 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 7; name: NFCORE_DMSCORE:DMSCORE:MULTIQC; status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/55/5dd0430083da5c332e7950544ac6e0]
+May-07 23:02:43.292 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:43.294 [Task submitter] INFO  nextflow.Session - [aa/6e83ae] Submitted process > NFCORE_DMSCORE:DMSCORE:PREMERGE (gid1a_1_quality_1_pe)
+May-07 23:02:43.298 [TaskFinalizer-7] DEBUG nextflow.processor.TaskProcessor - Process NFCORE_DMSCORE:DMSCORE:MULTIQC > Skipping output binding because one or more optional files are missing: fileoutparam<2>
+May-07 23:02:46.384 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 8; name: NFCORE_DMSCORE:DMSCORE:PREMERGE (gid1a_1_quality_1_pe); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/aa/6e83aebc1b031afabb4d8df09e97f3]
+May-07 23:02:46.488 [Task submitter] DEBUG n.executor.local.LocalTaskHandler - Launch cmd line: /bin/bash -ue .command.run
+May-07 23:02:46.489 [Task submitter] INFO  nextflow.Session - [f2/f878d3] Submitted process > NFCORE_DMSCORE:DMSCORE:GATK_SATURATIONMUTAGENESIS (gid1a_1_quality_1_pe)
+May-07 23:03:05.753 [Task monitor] DEBUG n.processor.TaskPollingMonitor - Task completed > TaskHandler[id: 9; name: NFCORE_DMSCORE:DMSCORE:GATK_SATURATIONMUTAGENESIS (gid1a_1_quality_1_pe); status: COMPLETED; exit: 0; error: -; workDir: /Users/benjaminwehnert/dmscore/work/f2/f878d301d101b20d245c27be19bbf3]
+May-07 23:03:05.837 [Actor Thread 2] DEBUG nextflow.processor.TaskProcessor - Handling unexpected condition for
+  task: name=NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_PROCESS_GATK (1); work-dir=null
+  error [nextflow.exception.ProcessUnrecoverableException]: Path value cannot be null
+May-07 23:03:05.858 [Actor Thread 2] ERROR nextflow.processor.TaskProcessor - Error executing process > 'NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_PROCESS_GATK (1)'
+
+Caused by:
+  Path value cannot be null
+
+
+
+Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run`
+May-07 23:03:05.860 [Actor Thread 2] INFO  nextflow.Session - Execution cancelled -- Finishing pending tasks before exit
+May-07 23:03:05.865 [Actor Thread 2] ERROR nextflow.Nextflow - Pipeline failed. Please refer to troubleshooting docs: https://nf-co.re/docs/usage/troubleshooting
+May-07 23:03:05.866 [main] DEBUG nextflow.Session - Session await > all processes finished
+May-07 23:03:05.867 [Task monitor] DEBUG n.processor.TaskPollingMonitor - <<< barrier arrives (monitor: local) - terminating tasks monitor poll loop
+May-07 23:03:05.867 [main] DEBUG nextflow.Session - Session await > all barriers passed
+May-07 23:03:05.868 [Actor Thread 12] DEBUG nextflow.processor.TaskProcessor - Handling unexpected condition for
+  task: name=NFCORE_DMSCORE:DMSCORE:DMSANALYSIS_PROCESS_GATK; work-dir=null
+  error [java.lang.InterruptedException]: java.lang.InterruptedException
+May-07 23:03:05.872 [main] DEBUG nextflow.util.ThreadPoolManager - Thread pool 'TaskFinalizer' shutdown completed (hard=false)
+May-07 23:03:05.872 [main] DEBUG nextflow.util.ThreadPoolManager - Thread pool 'PublishDir' shutdown completed (hard=false)
+May-07 23:03:05.880 [main] INFO  nextflow.Nextflow - -[nf-core/dmscore] Pipeline completed with errors-
+May-07 23:03:05.897 [main] DEBUG n.trace.WorkflowStatsObserver - Workflow completed > WorkflowStats[succeededCount=9; failedCount=0; ignoredCount=0; cachedCount=0; pendingCount=0; submittedCount=0; runningCount=0; retriesCount=0; abortedCount=0; succeedDuration=2m 24s; failedDuration=0ms; cachedDuration=0ms;loadCpus=0; loadMemory=0; peakRunning=2; peakCpus=8; peakMemory=16 GB; ]
+May-07 23:03:05.898 [main] DEBUG nextflow.trace.TraceFileObserver - Workflow completed -- saving trace file
+May-07 23:03:05.900 [main] DEBUG nextflow.trace.ReportObserver - Workflow completed -- rendering execution report
+May-07 23:03:07.314 [main] DEBUG nextflow.trace.TimelineObserver - Workflow completed -- rendering execution timeline
+May-07 23:03:07.511 [main] DEBUG nextflow.cache.CacheDB - Closing CacheDB done
+May-07 23:03:07.545 [main] INFO  org.pf4j.AbstractPluginManager - Stop plugin 'nf-schema@2.3.0'
+May-07 23:03:07.545 [main] DEBUG nextflow.plugin.BasePlugin - Plugin stopped nf-schema
+May-07 23:03:07.546 [main] DEBUG nextflow.util.ThreadPoolManager - Thread pool 'FileTransfer' shutdown completed (hard=false)
+May-07 23:03:07.547 [main] DEBUG nextflow.script.ScriptRunner - > Execution complete -- Goodbye
diff --git a/main.nf b/main.nf
index 7c028f7..41a451c 100644
--- a/main.nf
+++ b/main.nf
@@ -26,9 +26,6 @@ include { getGenomeAttribute      } from './subworkflows/local/utils_nfcore_deep
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 */
 
-// TODO nf-core: Remove this line if you don't need a FASTA file
-//   This is an example of how to use getGenomeAttribute() to fetch parameters
-//   from igenomes.config using `--genome`
 params.fasta = getGenomeAttribute('fasta')
 
 /*
@@ -60,6 +57,7 @@ workflow NFCORE_DEEPMUTSCAN {
     emit:
     multiqc_report = DEEPMUTSCAN.out.multiqc_report // channel: /path/to/multiqc_report.html
 }
+
 /*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
     RUN MAIN WORKFLOW
@@ -78,10 +76,7 @@ workflow {
         params.monochrome_logs,
         args,
         params.outdir,
-        params.input,
-        params.help,
-        params.help_full,
-        params.show_hidden
+        params.input
     )
 
     //
diff --git a/modules.json b/modules.json
index adda4fc..d9f1096 100644
--- a/modules.json
+++ b/modules.json
@@ -5,6 +5,16 @@
         "https://github.com/nf-core/modules.git": {
             "modules": {
                 "nf-core": {
+                    "bwa/index": {
+                        "branch": "master",
+                        "git_sha": "6d46786420b4d7bc88eba026eb389c0c5535d120",
+                        "installed_by": ["modules"]
+                    },
+                    "bwa/mem": {
+                        "branch": "master",
+                        "git_sha": "2fb127c8fd13de0adaa676df7169131e45c0b114",
+                        "installed_by": ["modules"]
+                    },
                     "fastqc": {
                         "branch": "master",
                         "git_sha": "6d46786420b4d7bc88eba026eb389c0c5535d120",
@@ -12,7 +22,17 @@
                     },
                     "multiqc": {
                         "branch": "master",
-                        "git_sha": "008f9d3e61209bf995edac3ba531f54e269e1215",
+                        "git_sha": "98403d15b0e50edae1f3fec5eae5e24982f1fade",
+                        "installed_by": ["modules"]
+                    },
+                    "samtools/index": {
+                        "branch": "master",
+                        "git_sha": "6d46786420b4d7bc88eba026eb389c0c5535d120",
+                        "installed_by": ["modules"]
+                    },
+                    "samtools/sort": {
+                        "branch": "master",
+                        "git_sha": "6d46786420b4d7bc88eba026eb389c0c5535d120",
                         "installed_by": ["modules"]
                     }
                 }
@@ -21,7 +41,7 @@
                 "nf-core": {
                     "utils_nextflow_pipeline": {
                         "branch": "master",
-                        "git_sha": "05954dab2ff481bcb999f24455da29a5828af08d",
+                        "git_sha": "1a545fcbd762911c21a64ced3dbef99b2b51ac75",
                         "installed_by": ["subworkflows"]
                     },
                     "utils_nfcore_pipeline": {
@@ -31,7 +51,7 @@
                     },
                     "utils_nfschema_plugin": {
                         "branch": "master",
-                        "git_sha": "fdc08b8b1ae74f56686ce21f7ea11ad11990ce57",
+                        "git_sha": "a7b27fd25bfa8dcc07d299e88bd790585901a436",
                         "installed_by": ["subworkflows"]
                     }
                 }
diff --git a/modules/local/bamprocessing/bam_filter/environment.yml b/modules/local/bamprocessing/bam_filter/environment.yml
new file mode 100644
index 0000000..a5338f6
--- /dev/null
+++ b/modules/local/bamprocessing/bam_filter/environment.yml
@@ -0,0 +1,5 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::samtools=1.21
diff --git a/modules/local/bamprocessing/bam_filter/main.nf b/modules/local/bamprocessing/bam_filter/main.nf
new file mode 100644
index 0000000..bed65dd
--- /dev/null
+++ b/modules/local/bamprocessing/bam_filter/main.nf
@@ -0,0 +1,51 @@
+
+process BAMFILTER_DMS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
+        'https://depot.galaxyproject.org/singularity/samtools:1.21--h96c455f_1':
+        'biocontainers/samtools:1.21--h96c455f_1' }"
+
+    input:
+    tuple val(meta), path(bam)
+
+    output:
+    tuple val(meta), path("*.bam"), emit: bam
+    path "versions.yml"           , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args = task.ext.args ?: ''
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    """
+    # Keep primary, mapped, MAPQ>=30 reads and drop indel/splice alignments.
+    # Wildtype (NM:i:0) reads are intentionally retained (needed for downstream
+    # sequencing-error correction); the variant counter ignores them for calling.
+    samtools view -h -F 4 -F 256 -q 30 $bam | \
+    samtools view -h | \
+    awk '{if(\$6 !~ /I/ && \$6 !~ /D/ && \$6 !~ /N/) print \$0}' | \
+    samtools view -bS > ${meta.id}_filtered.bam
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        samtools: \$(samtools --version |& sed '1!d ; s/samtools //')
+END_VERSIONS
+    """
+
+    stub:
+    """
+    # Must match the name the script block writes: the input bam is staged under
+    # \${meta.id}.bam, so touching that name here would just re-touch the input and
+    # leave the process with no output of its own.
+    touch ${meta.id}_filtered.bam
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        samtools: \$(samtools --version |& sed '1!d ; s/samtools //')
+END_VERSIONS
+    """
+}
diff --git a/modules/local/bamprocessing/bam_filter/meta.yml b/modules/local/bamprocessing/bam_filter/meta.yml
new file mode 100644
index 0000000..78c92ea
--- /dev/null
+++ b/modules/local/bamprocessing/bam_filter/meta.yml
@@ -0,0 +1,48 @@
+name: "bamfilter_dms"
+description: Filters BAM files specifically for Deep Mutational Scanning (DMS) analysis by removing unmapped reads, low-quality alignments, secondary alignments, indels (CIGAR I/D/N), and perfectly matching wild-type reads.
+keywords:
+  - dms
+  - bam
+  - filtering
+  - samtools
+  - indels
+tools:
+  - "samtools":
+      description: "Tools for manipulating next-generation sequencing data"
+      homepage: "http://www.htslib.org/"
+      documentation: "http://www.htslib.org/doc/samtools.html"
+      tool_dev_url: "https://github.com/samtools/samtools"
+      doi: "10.1093/bioinformatics/btp352"
+      licence: ["MIT/Expat"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - bam:
+        type: file
+        description: Input BAM file to be filtered
+        pattern: "*.{bam}"
+
+output:
+  - bam:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - "*.bam":
+          type: file
+          description: Filtered BAM file containing only high-quality mutated reads without indels
+          pattern: "*_filtered.bam"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/bamprocessing/premerge/environment.yml b/modules/local/bamprocessing/premerge/environment.yml
new file mode 100644
index 0000000..0e199ab
--- /dev/null
+++ b/modules/local/bamprocessing/premerge/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bwa=0.7.19
+  - bioconda::samtools=1.21
+  - bioconda::vsearch=2.30.0
diff --git a/modules/local/bamprocessing/premerge/main.nf b/modules/local/bamprocessing/premerge/main.nf
new file mode 100644
index 0000000..fbf433f
--- /dev/null
+++ b/modules/local/bamprocessing/premerge/main.nf
@@ -0,0 +1,51 @@
+
+process PREMERGE {
+    tag "$meta.id"
+    label 'process_medium'
+
+    conda "${moduleDir}/environment.yml"
+    container "community.wave.seqera.io/library/bwa_samtools_vsearch:28e8640725d3d8e9"
+
+    input:
+    tuple val(meta), path(bam)
+    path wt_seq
+
+    output:
+    tuple val(meta), path("*.bam"), emit: bam
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    """
+    # Convert BAM to paired FASTQ files
+    samtools fastq -1 forward_reads.fastq -2 reverse_reads.fastq -0 /dev/null -s /dev/null -n $bam
+
+    # Merge paired reads
+    vsearch --fastq_mergepairs forward_reads.fastq --reverse reverse_reads.fastq --fastqout merged_reads.fastq --fastq_minovlen 10 --fastq_allowmergestagger
+
+    # Re-align merged reads
+    bwa index $wt_seq
+    bwa mem $wt_seq merged_reads.fastq | samtools view -Sb - > ${prefix}_merged.bam
+
+    # Save version information
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        premerge: \$(samtools --version |& sed '1!d ; s/samtools //')
+END_VERSIONS
+    """
+
+    stub:
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    """
+    touch merged_reads.fastq
+    touch merged_reads.bam
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        premerge: dummy_version
+END_VERSIONS
+    """
+}
diff --git a/modules/local/bamprocessing/premerge/meta.yml b/modules/local/bamprocessing/premerge/meta.yml
new file mode 100644
index 0000000..76186fe
--- /dev/null
+++ b/modules/local/bamprocessing/premerge/meta.yml
@@ -0,0 +1,65 @@
+name: "premerge"
+description: Processes paired-end BAM files by converting them to FASTQ, merging overlapping paired reads using VSEARCH, and re-aligning the merged single-end reads to the wild-type reference using BWA.
+keywords:
+  - paired-end merging
+  - alignment
+  - bwa
+  - vsearch
+  - samtools
+  - dms
+tools:
+  - "samtools":
+      description: "Tools for manipulating next-generation sequencing data"
+      homepage: "http://www.htslib.org/"
+      documentation: "http://www.htslib.org/doc/samtools.html"
+      tool_dev_url: "https://github.com/samtools/samtools"
+      doi: "10.1093/bioinformatics/btp352"
+      licence: ["MIT/Expat"]
+  - "vsearch":
+      description: "Versatile open-source tool for metagenomics"
+      homepage: "https://github.com/torognes/vsearch"
+      documentation: "https://github.com/torognes/vsearch/blob/master/README.md"
+      doi: "10.7717/peerj.2584"
+      licence: ["GPL-3.0-or-later", "BSD-2-Clause"]
+  - "bwa":
+      description: "Burrow-Wheeler Aligner for short-read alignment"
+      homepage: "http://bio-bwa.sourceforge.net/"
+      documentation: "http://bio-bwa.sourceforge.net/bwa.shtml"
+      doi: "10.1093/bioinformatics/btp324"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - bam:
+        type: file
+        description: Input paired-end BAM file
+        pattern: "*.{bam}"
+  - - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type reference sequence
+        pattern: "*.{fasta,fa}"
+
+output:
+  - bam:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - "*.bam":
+          type: file
+          description: BAM file containing the merged and re-aligned reads
+          pattern: "*_merged.bam"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/dmsanalysis/aa_seq/environment.yml b/modules/local/dmsanalysis/aa_seq/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/aa_seq/main.nf b/modules/local/dmsanalysis/aa_seq/main.nf
new file mode 100644
index 0000000..01ac974
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/main.nf
@@ -0,0 +1,31 @@
+process DMSANALYSIS_AASEQ {
+    tag "amino_acid_sequence"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(wt_seq)
+    val pos_range
+
+    output:
+    tuple val(meta), path("aa_seq.txt"), emit: aa_seq
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'aa_seq.R'
+
+    stub:
+    """
+    touch aa_seq.txt
+    echo "DMSANALYSIS_AASEQ:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/aa_seq/meta.yml b/modules/local/dmsanalysis/aa_seq/meta.yml
new file mode 100644
index 0000000..46a761d
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/meta.yml
@@ -0,0 +1,54 @@
+name: "dmsanalysis_aaseq"
+description: Translates a wild-type DNA sequence into an amino acid sequence based on a provided coding region (ORF) range.
+keywords:
+  - translation
+  - amino acid
+  - dna
+  - dms
+  - biostrings
+tools:
+  - "bioconductor-biostrings":
+      description: "Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/Biostrings/man/Biostrings.pdf"
+      licence: ["Artistic-2.0"]
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/mutscan"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'ref' ]`
+    - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type DNA reference sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions in the format 'start-stop', e.g., '352-1383'
+
+output:
+  - aa_seq:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - aa_seq.txt:
+          type: file
+          description: Text file containing the translated amino acid sequence
+          pattern: "aa_seq.txt"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/dmsanalysis/aa_seq/templates/aa_seq.R b/modules/local/dmsanalysis/aa_seq/templates/aa_seq.R
new file mode 100644
index 0000000..7c2e8e5
--- /dev/null
+++ b/modules/local/dmsanalysis/aa_seq/templates/aa_seq.R
@@ -0,0 +1,65 @@
+#!/usr/bin/env Rscript
+
+# input: wildtype-seq, start&stopp pos.
+# output: amino acid sequence within the start-stop frame (.txt)
+
+# Load necessary libraries
+suppressMessages(library(Biostrings))
+
+# Define the function
+aa_seq <- function(wt_seq_input, pos_range, output_file) {
+  # Parse the start and stop positions from the input format "start-stop"
+  positions <- unlist(strsplit(pos_range, "-"))
+  start_pos <- as.numeric(positions[1])
+  stop_pos <- as.numeric(positions[2])
+
+  # Check if the input is a file or a string
+  if (file.exists(wt_seq_input)) {
+    # If it's a file, read the sequence from the fasta file
+    seq_data <- readDNAStringSet(filepath = wt_seq_input)
+    wt_seq <- seq_data[[1]]  # Extract the sequence
+  } else {
+    # Otherwise, treat the input as a sequence string
+    wt_seq <- DNAString(wt_seq_input)
+  }
+
+  # Extract the sequence between start and stop codons
+  coding_seq <- subseq(wt_seq, start = start_pos, end = stop_pos)
+
+  # Translate the coding sequence into an amino acid sequence
+  aa_seq <- translate(coding_seq)
+
+  # Write the amino acid sequence to a .txt file
+  write(as.character(aa_seq), file = output_file)
+}
+
+
+#####
+# run function
+#####
+aa_seq(
+  wt_seq_input = "$wt_seq",
+  "$pos_range",
+  "aa_seq.txt"
+  )
+
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/environment.yml b/modules/local/dmsanalysis/error_correction_false_doubles/environment.yml
new file mode 100644
index 0000000..07bd598
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-stringr=1.6.0
+  - bioconda::bioconductor-biostrings=2.78.0
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/main.nf b/modules/local/dmsanalysis/error_correction_false_doubles/main.nf
new file mode 100644
index 0000000..c43bd23
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/main.nf
@@ -0,0 +1,43 @@
+process DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(raw_counts), path(filtered), path(completed)
+    path wt_fasta
+    path aa_seq
+    val min_counts
+    val method
+    val codon_window
+
+    output:
+    tuple val(meta),
+        path("variantCounts_filtered_by_library_error_corrected.csv"),
+        path("variantCounts_for_heatmaps_error_corrected.csv"),
+        path("library_completed_variantCounts_error_corrected.csv"),
+        emit: corrected
+    tuple val(meta), path("seq_error_rate.csv"), emit: seq_error_rate
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'error_correction_false_doubles.R'
+
+    stub:
+    """
+    touch variantCounts_filtered_by_library_error_corrected.csv
+    touch variantCounts_for_heatmaps_error_corrected.csv
+    touch library_completed_variantCounts_error_corrected.csv
+    touch seq_error_rate.csv
+    echo "DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/meta.yml b/modules/local/dmsanalysis/error_correction_false_doubles/meta.yml
new file mode 100644
index 0000000..5840a90
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/meta.yml
@@ -0,0 +1,66 @@
+name: "dmsanalysis_error_correction_false_doubles"
+description: "Sequencing-error correction of single-codon variant counts using the false-double-mutant model: because the library only contains single-codon changes, observed multi-codon variants are treated as errors and used to estimate and subtract the per-nucleotide error rate."
+keywords:
+  - deep mutational scanning
+  - error correction
+  - false doubles
+  - variant counting
+tools:
+  - "r-base":
+      description: "The R programming language (base only)"
+      homepage: "https://www.r-project.org/"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1', type:'input', replicate:1 ]`
+    - raw_counts:
+        type: file
+        description: Raw (pre-library-filter) variant count table from VARIANTCOUNTING.
+        pattern: "*.variant_counts.tsv"
+    - filtered:
+        type: file
+        description: Library-filtered single-codon variant counts (annotated CSV).
+        pattern: "*.csv"
+    - completed:
+        type: file
+        description: Completed library table (all possible variants) to also correct.
+        pattern: "*.csv"
+  - - aa_seq:
+        type: file
+        description: Reference amino-acid sequence (one line).
+  - - min_counts:
+        type: integer
+        description: Minimum count threshold for the amino-acid-level heatmap table.
+
+output:
+  - corrected:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - variantCounts_filtered_by_library_error_corrected.csv:
+          type: file
+          description: Library-filtered counts with added `counts_corrected` / `counts_per_cov_corrected` columns.
+          pattern: "variantCounts_filtered_by_library_error_corrected.csv"
+      - variantCounts_for_heatmaps_error_corrected.csv:
+          type: file
+          description: Amino-acid-level heatmap table built from corrected counts (canonical `total_counts` columns).
+          pattern: "variantCounts_for_heatmaps_error_corrected.csv"
+      - library_completed_variantCounts_error_corrected.csv:
+          type: file
+          description: Completed library table with corrected counts propagated (for logdiff etc.).
+          pattern: "library_completed_variantCounts_error_corrected.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+maintainers:
+  - "@BenjaminWehnert1008"
diff --git a/modules/local/dmsanalysis/error_correction_false_doubles/templates/error_correction_false_doubles.R b/modules/local/dmsanalysis/error_correction_false_doubles/templates/error_correction_false_doubles.R
new file mode 100644
index 0000000..9282ca3
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_false_doubles/templates/error_correction_false_doubles.R
@@ -0,0 +1,741 @@
+#!/usr/bin/env Rscript
+
+## false-double mutant codon sequencing-error correction in nf-core/deepmutscan (nucleotide level).
+##
+## Two estimators, ported VERBATIM from Maxi's scripts (24.06.2026):
+##   * MLE (default): per-SNV maximum-likelihood error rate e = F / m, applied per variant.
+##   * EB:            pool F and m across SNVs, then a Gamma-Poisson empirical-Bayes fit with
+##                    six-category shrinkage -> posterior-mean rate e_EB.
+## `$method` (mle|eb) selects which one runs. Everything except I/O plumbing and the dispatch is
+## kept exactly as written.
+##
+## Nextflow template: an R `\$` is a literal dollar; backslashes meant for R are doubled.
+
+suppressMessages({
+  library(Biostrings)
+  library(stringr)
+})
+
+## -------------------------------------------------------------------------------------------------
+## MLE estimator (verbatim)
+## -------------------------------------------------------------------------------------------------
+seq_error_correct_by_false_doubles_MLE <- function(wt_path, input_count_path_raw, input_count_path_processed, output_file_path, seq_error_rate_path, codon_window){
+
+  ## load data (nucleotide-level counts from GATK)
+
+  # WT sequence
+  wt.seq <- readDNAStringSet(wt_path)
+
+  # for the seq error rates, build a vector to be filled
+  seq.error.names <- c()
+  for(i in 1:nchar(wt.seq)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]][i]
+    seq.error.names <- c(seq.error.names, paste0(i, ":", tmp.wt, ">", c("A", "T", "G", "C"))[-match(tmp.wt, c("A", "T", "G", "C"))])
+  }
+  seq.error.rate <- matrix(0, nrow = length(seq.error.names), ncol = 2)
+  rownames(seq.error.rate) <- seq.error.names
+  colnames(seq.error.rate) <- c("1nt counts/coverage", "1nt false counts/coverage")
+
+  # Set the column names
+  colnames <- c("counts", "cov", "mean_length_variant_reads", "varying_bases",
+                "base_mut", "varying_codons", "codon_mut", "aa_mut", "pos_mut")
+  input.counts.raw <- read.table(input_count_path_raw, sep = "\\t", header = FALSE, fill = TRUE, col.names = colnames)
+  input.counts.processed <- read.csv(input_count_path_processed)
+
+  ## append key columns
+  input.counts.processed\$counts_corrected <- input.counts.processed\$counts
+  input.counts.processed\$counts_per_cov_corrected <- input.counts.processed\$counts_per_cov
+
+  ## calculate some things upfront
+
+  ### highest accessible codon
+  max.codon <- max(as.numeric(str_split_fixed(input.counts.raw\$codon_mut, ":", 2)[,1]), na.rm = T)
+
+  ### total distribution of false double mutant coverage decay (%) vs. codon-codon distance
+  cat("Estimating the distance-dependent coverage decay of false double mutants...\\n")
+  all.false.doubles <- input.counts.raw
+  all.false.doubles <- all.false.doubles[which(all.false.doubles[,"varying_bases"] >= 3 & all.false.doubles[,"varying_bases"] < 5 & all.false.doubles[,"varying_codons"] == 2),,drop = F]
+  all.false.doubles\$codon_dist <- vapply(regmatches(all.false.doubles[,"codon_mut"], gregexpr("\\\\d+(?=:)", all.false.doubles[,"codon_mut"], perl = TRUE)), function(z) abs(diff(as.integer(z))), integer(1))
+
+  all.false.doubles.codons <- str_split_fixed(all.false.doubles\$codon_mut, ", ", 2)
+  all.false.doubles.codons[,1] <- as.integer(sub(":.*", "", all.false.doubles.codons[,1]))
+  all.false.doubles.codons[,2] <- as.integer(sub(":.*", "", all.false.doubles.codons[,2]))
+  median.high.conf.coverage.per.pos <- rep(NA, max(as.numeric(str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1])))
+  names(median.high.conf.coverage.per.pos) <- 1:max(as.numeric(str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]))
+  for(i in 1:length(median.high.conf.coverage.per.pos)){
+    median.high.conf.coverage.per.pos[i] <- median(input.counts.processed[which(names(median.high.conf.coverage.per.pos)[i] == str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]),"cov"])
+  }
+  all.false.doubles.codons[,1] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,1], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles.codons[,2] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,2], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles\$max_cov_possible <- as.numeric(apply(all.false.doubles.codons, 1, min))
+  all.false.doubles\$perc_cov_to_max <- 100 * all.false.doubles\$cov / all.false.doubles\$max_cov_possible
+
+  ## deepmutscan guard (not in the verbatim script): a run can legitimately contain NO observed false
+  ## double mutants -- a small, shallow or tightly-filtered library, or the nf-core test dataset, where
+  ## every variant is single-codon. The verbatim code then hands lm() an empty frame and dies with
+  ## "0 (non-NA) cases". With no false doubles anywhere there is also no evidence of codon-level
+  ## sequencing error to subtract, so the correct answer is a no-op: corrected == raw. The quadratic
+  ## needs >= 3 distinct codon distances to be estimable at all, so require that too.
+  .usable <- is.finite(log(all.false.doubles\$perc_cov_to_max))
+  if (nrow(all.false.doubles) == 0 || length(unique(all.false.doubles\$codon_dist[.usable])) < 3) {
+    cat("WARNING: only", sum(.usable), "usable false double mutant(s) found - the distance-dependent\\n",
+        "coverage decay is not estimable, so NO sequencing-error correction is applied and the\\n",
+        "corrected counts are identical to the raw counts.\\n")
+    write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+    write.csv(input.counts.processed, file = output_file_path, row.names = F)
+    return(invisible(NULL))
+  }
+
+  fit <- lm(log(all.false.doubles\$perc_cov_to_max) ~ all.false.doubles\$codon_dist + I(all.false.doubles\$codon_dist^2))
+  all.false.doubles\$pred_cov_perc <- exp(predict(fit))
+
+  ### convert this into a look-up table: "% max. possible coverage" depending on codon-codon distance
+  dual.codon.coverage.estimate <- matrix(NA, nrow = max(all.false.doubles\$codon_dist), ncol = 2)
+  colnames(dual.codon.coverage.estimate) <- c("Codon distance", "Estimate % max. possible coverage")
+  dual.codon.coverage.estimate[,"Codon distance"] <- 1:nrow(dual.codon.coverage.estimate)
+  ## deepmutscan fix (Maxi-approved deviation from the verbatim script): fill the coverage-decay
+  ## lookup from the fitted quadratic evaluated at EVERY integer codon distance, instead of match()ing
+  ## only the distances that happen to have an observed double. `pred_cov_perc = exp(predict(fit))`
+  ## depends solely on codon_dist, so this is bit-identical to the original at every observed distance
+  ## and merely fills the previously-NA gaps -- those NAs otherwise crash MLE and leave NA corrected
+  ## counts in EB (a distance with no observed double, e.g. around codon 288 on the GID1A min_counts=10
+  ## data).
+  .dccd <- dual.codon.coverage.estimate[,"Codon distance"]
+  .cf <- coef(fit)
+  dual.codon.coverage.estimate[,"Estimate % max. possible coverage"] <- exp(.cf[1] + .cf[2] * .dccd + .cf[3] * .dccd^2)
+
+  ## Process the GATK file, only look at single nucleotide variants
+  cat("Sequencing error correction of GATK counts...\\n")
+  for (i in grep("[,]", input.counts.processed[,"base_mut"], invert = T)){
+
+    ### algorithm:
+    ### 1.) isolate 1nt variant of interest
+    ### 2.) look for 2/3nt hits in a pre-specified window of codons upstream or downstream
+    ### -> if there are zero false double mutants: skip / set correction factor to zero
+    ### -> if there are any false double mutants: continue
+    ### 3.) generate a table for ALL possible 2/3nt variants in the selected window:
+    ### true high-conf. variant count | true high-conf. variant coverage | false double double variant count (often 0 or 1) | false double double variant coverage
+    ### 4.) Maximum likelihood estimation (MLE) over all events
+    ### 5.) Apply correction factor
+
+    ## 1.) isolate 1nt variant of interest
+    tmp.single <- input.counts.processed[i,"base_mut"]
+    seq.error.rate[tmp.single, "1nt counts/coverage"] <- input.counts.processed[i,"counts_per_cov"]
+
+    ## 2.) look for 2/3nt hits in up to 40 codons (120 bp) upstream or downstream
+    tmp.false.doubles <- input.counts.raw[grep(paste0("(?<!\\\\d)",input.counts.processed[i,"codon_mut"]), input.counts.raw[,"codon_mut"], perl = T),,drop = F]
+
+    ## subset multi-codon variants
+    tmp.false.doubles <- tmp.false.doubles[which(tmp.false.doubles[,"varying_bases"] >= 3 & tmp.false.doubles[,"varying_bases"] < 5 & tmp.false.doubles[,"varying_codons"] == 2),,drop = F]
+    if(nrow(tmp.false.doubles) == 0){
+      next
+    }
+
+    ## set and enforce distance threshold
+    tmp.ref.codon <- as.numeric(strsplit(input.counts.processed[i,"codon_mut"] , ":")[[1]][1])
+    tmp.min.from.ref <- max(c(1, tmp.ref.codon - c(codon_window - 1)))
+    tmp.max.from.ref <- min(c(max.codon, tmp.ref.codon + c(codon_window - 1)))
+    tmp.double.codon <- str_split_fixed(tmp.false.doubles\$codon_mut, ", ", 2)
+    tmp.double.codon.n1 <- as.integer(sub(":.*", "", tmp.double.codon[,1]))
+    tmp.double.codon.n2 <- as.integer(sub(":.*", "", tmp.double.codon[,2]))
+    tmp.double.codon.within.range <- (tmp.double.codon.n1 >= tmp.min.from.ref & tmp.double.codon.n1 <= tmp.max.from.ref) & (tmp.double.codon.n2 >= tmp.min.from.ref & tmp.double.codon.n2 <= tmp.max.from.ref)
+    tmp.false.doubles <- tmp.false.doubles[which(tmp.double.codon.within.range == T),,drop = F]
+    if(nrow(tmp.false.doubles) == 0){
+      next
+    }
+
+    ## 3.) generate a table for ALL possible 2/3nt variants in the selected window
+    tmp.summary.table.entries <- input.counts.processed
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) != tmp.ref.codon),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) >= tmp.min.from.ref &
+                                                                   as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) <= tmp.max.from.ref),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(tmp.summary.table.entries\$varying_bases > 1),]
+
+    ### fill the table
+    tmp.summary.table <- matrix(NA, ncol = 5, nrow = nrow(tmp.summary.table.entries))
+    colnames(tmp.summary.table) <- c("codon distance",
+                                     "true high-conf. variant count",
+                                     "true high-conf. variant coverage",
+                                     "false double double variant count",
+                                     "false double double variant coverage")
+    rownames(tmp.summary.table) <- paste0(c(tmp.summary.table.entries\$codon_mut),", ", c(tmp.summary.table.entries\$base_mut))
+
+    tmp.summary.table[,"codon distance"] <- abs(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) - tmp.ref.codon)
+    tmp.summary.table[,"true high-conf. variant count"] <- tmp.summary.table.entries\$counts
+    tmp.summary.table[,"true high-conf. variant coverage"] <- tmp.summary.table.entries\$cov
+    tmp.summary.table[,"false double double variant count"] <- 0
+
+    ## input the actual hits
+    tmp.double.codon <- str_split_fixed(tmp.false.doubles\$codon_mut, ", ", 2)
+    tmp.match1 <- match(tmp.double.codon[,1], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match1[is.na(tmp.match1)] <- 0
+    tmp.match2 <- match(tmp.double.codon[,2], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match2[is.na(tmp.match2)] <- 0
+    tmp.match <- c(tmp.match1 + tmp.match2)
+
+    ### remove those false doubles for which there are no matched true high confidence calls
+    if(any(tmp.match == 0)){
+      tmp.false.doubles <- tmp.false.doubles[-which(tmp.match == 0),,drop = F]
+      tmp.match <- tmp.match[-which(tmp.match == 0)]
+      if(nrow(tmp.false.doubles) == 0){
+        next
+      }
+    }
+
+    tmp.summary.table[tmp.match,"false double double variant count"] <- tmp.false.doubles[,"counts"]
+    tmp.summary.table[tmp.match,"false double double variant coverage"] <- tmp.false.doubles[,"cov"]
+    tmp.summary.table <- as.data.frame(tmp.summary.table)
+
+    ## 4.) Maximum likelihood estimation (MLE) over all events, iterating over codon positions
+
+    ### summarise data by position
+    tmp.summary.table.pos <- matrix(NA, nrow = length(unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])), ncol = 5)
+    colnames(tmp.summary.table.pos) <- colnames(tmp.summary.table)
+    rownames(tmp.summary.table.pos) <- unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])
+    tmp.summary.table.pos[,"codon distance"] <- abs(as.numeric(str_split_fixed(rownames(tmp.summary.table.pos), ":", 2)[,1]) - as.numeric(str_split_fixed(input.counts.processed[i,"codon_mut"], ":", 2)[,1]))
+    for(j in 1:nrow(tmp.summary.table.pos)){
+      tmp.name <- rownames(tmp.summary.table.pos)[j]
+      tmp.summary.table.pos[j,"true high-conf. variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant count"])
+      tmp.summary.table.pos[j,"true high-conf. variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant coverage"])
+      tmp.summary.table.pos[j,"false double double variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double double variant count"])
+      tmp.summary.table.pos[j,"false double double variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double double variant coverage"], na.rm = T)
+
+      ## infer a good proxy for the false double variant coverage with 0 counts (based estimated distance-dependent % coverage decay observed across all observed double mutants)
+      if(tmp.summary.table.pos[j,"false double double variant coverage"] == "-Inf"){
+        tmp.summary.table.pos[j,"false double double variant coverage"] <- c(dual.codon.coverage.estimate[abs(tmp.summary.table.pos[j,"codon distance"]),"Estimate % max. possible coverage"] / 100) * tmp.summary.table.pos[j,"true high-conf. variant coverage"]
+        tmp.summary.table.pos[j,"false double double variant coverage"] <- round(tmp.summary.table.pos[j,"false double double variant coverage"])
+      }
+    }
+    tmp.summary.table.pos <- as.data.frame(tmp.summary.table.pos)
+    e_MLE <- sum(tmp.summary.table.pos\$`false double double variant count`) /
+      sum(tmp.summary.table.pos\$`true high-conf. variant count` * c(tmp.summary.table.pos\$`false double double variant coverage` / tmp.summary.table.pos\$`true high-conf. variant coverage`))
+    seq.error.rate[tmp.single,"1nt false counts/coverage"] <- e_MLE
+    input.counts.processed[i,"counts_per_cov_corrected"] <- input.counts.processed[i,"counts_per_cov"] - e_MLE
+
+    ## 5.) Apply correction factor
+    input.counts.processed[i,"counts_corrected"] <- input.counts.processed[i,"counts"] - c(e_MLE * input.counts.processed[i,"cov"])
+
+    ## round to nearest integer, do not allow for negative counts
+    input.counts.processed[i,"counts_corrected"] <- round(input.counts.processed[i,"counts_corrected"])
+    if(input.counts.processed[i,"counts_corrected"] < 0){
+      input.counts.processed[i,"counts_corrected"] <- 0
+      input.counts.processed[i,"counts_per_cov_corrected"] <- 0
+    }
+
+  }
+
+  # Write the processed data
+  write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+  write.csv(input.counts.processed, file = output_file_path, row.names = F)
+
+}
+
+## -------------------------------------------------------------------------------------------------
+## EB estimator (verbatim)
+## -------------------------------------------------------------------------------------------------
+seq_error_correct_by_false_doubles_EB <- function(wt_path, input_count_path_raw, input_count_path_processed, output_file_path, seq_error_rate_path, codon_window){
+
+  ## load data (nucleotide-level counts from GATK)
+
+  # WT sequence
+  wt.seq <- readDNAStringSet(wt_path)
+
+  # for the seq error rates, build a vector to be filled
+  seq.error.names <- c()
+  for(i in 1:nchar(wt.seq)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]][i]
+    seq.error.names <- c(seq.error.names, paste0(i, ":", tmp.wt, ">", c("A", "T", "G", "C"))[-match(tmp.wt, c("A", "T", "G", "C"))])
+  }
+  seq.error.rate <- matrix(0, nrow = length(seq.error.names), ncol = 2)
+  rownames(seq.error.rate) <- seq.error.names
+  colnames(seq.error.rate) <- c("1nt counts/coverage", "1nt false counts/coverage")
+
+  # Set the column names
+  colnames <- c("counts", "cov", "mean_length_variant_reads", "varying_bases",
+                "base_mut", "varying_codons", "codon_mut", "aa_mut", "pos_mut")
+  input.counts.raw <- read.table(input_count_path_raw, sep = "\\t", header = FALSE, fill = TRUE, col.names = colnames)
+  input.counts.processed <- read.csv(input_count_path_processed)
+
+  ## append key columns
+  input.counts.processed\$counts_corrected <- input.counts.processed\$counts
+  input.counts.processed\$counts_per_cov_corrected <- input.counts.processed\$counts_per_cov
+
+  ## calculate some things upfront
+
+  ### highest accessible codon
+  max.codon <- max(as.numeric(str_split_fixed(input.counts.raw\$codon_mut, ":", 2)[,1]), na.rm = T)
+
+  ### total distribution of false double mutant coverage decay (%) vs. codon-codon distance
+  cat("Estimating the distance-dependent coverage decay of false double mutants...\\n")
+  all.false.doubles <- input.counts.raw
+  all.false.doubles <- all.false.doubles[which(all.false.doubles[,"varying_bases"] >= 3 & all.false.doubles[,"varying_bases"] < 5 & all.false.doubles[,"varying_codons"] == 2),,drop = F]
+  all.false.doubles\$codon_dist <- vapply(regmatches(all.false.doubles[,"codon_mut"], gregexpr("\\\\d+(?=:)", all.false.doubles[,"codon_mut"], perl = TRUE)), function(z) abs(diff(as.integer(z))), integer(1))
+
+  all.false.doubles.codons <- str_split_fixed(all.false.doubles\$codon_mut, ", ", 2)
+  all.false.doubles.codons[,1] <- as.integer(sub(":.*", "", all.false.doubles.codons[,1]))
+  all.false.doubles.codons[,2] <- as.integer(sub(":.*", "", all.false.doubles.codons[,2]))
+  median.high.conf.coverage.per.pos <- rep(NA, max(as.numeric(str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1])))
+  names(median.high.conf.coverage.per.pos) <- 1:max(as.numeric(str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]))
+  for(i in 1:length(median.high.conf.coverage.per.pos)){
+    median.high.conf.coverage.per.pos[i] <- median(input.counts.processed[which(names(median.high.conf.coverage.per.pos)[i] == str_split_fixed(input.counts.processed\$codon_mut, ":", 2)[,1]),"cov"])
+  }
+  all.false.doubles.codons[,1] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,1], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles.codons[,2] <- median.high.conf.coverage.per.pos[match(all.false.doubles.codons[,2], names(median.high.conf.coverage.per.pos))]
+  all.false.doubles\$max_cov_possible <- as.numeric(apply(all.false.doubles.codons, 1, min))
+  all.false.doubles\$perc_cov_to_max <- 100 * all.false.doubles\$cov / all.false.doubles\$max_cov_possible
+
+  ## deepmutscan guard (not in the verbatim script) - see the identical guard in the MLE estimator.
+  ## With no estimable coverage decay there is no evidence of codon-level sequencing error, so the
+  ## correct answer is a no-op: corrected == raw, rather than lm() dying on an empty frame.
+  .usable <- is.finite(log(all.false.doubles\$perc_cov_to_max))
+  if (nrow(all.false.doubles) == 0 || length(unique(all.false.doubles\$codon_dist[.usable])) < 3) {
+    cat("WARNING: only", sum(.usable), "usable false double mutant(s) found - the distance-dependent\\n",
+        "coverage decay is not estimable, so NO sequencing-error correction is applied and the\\n",
+        "corrected counts are identical to the raw counts.\\n")
+    write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+    write.csv(input.counts.processed, file = output_file_path, row.names = F)
+    return(invisible(NULL))
+  }
+
+  fit <- lm(log(all.false.doubles\$perc_cov_to_max) ~ all.false.doubles\$codon_dist + I(all.false.doubles\$codon_dist^2))
+  all.false.doubles\$pred_cov_perc <- exp(predict(fit))
+
+  ### convert this into a look-up table: "% max. possible coverage" depending on codon-codon distance
+  dual.codon.coverage.estimate <- matrix(NA, nrow = max(all.false.doubles\$codon_dist), ncol = 2)
+  colnames(dual.codon.coverage.estimate) <- c("Codon distance", "Estimate % max. possible coverage")
+  dual.codon.coverage.estimate[,"Codon distance"] <- 1:nrow(dual.codon.coverage.estimate)
+  ## deepmutscan fix (Maxi-approved deviation from the verbatim script): fill the coverage-decay
+  ## lookup from the fitted quadratic evaluated at EVERY integer codon distance, instead of match()ing
+  ## only the distances that happen to have an observed double. `pred_cov_perc = exp(predict(fit))`
+  ## depends solely on codon_dist, so this is bit-identical to the original at every observed distance
+  ## and merely fills the previously-NA gaps -- those NAs otherwise crash MLE and leave NA corrected
+  ## counts in EB (a distance with no observed double, e.g. around codon 288 on the GID1A min_counts=10
+  ## data).
+  .dccd <- dual.codon.coverage.estimate[,"Codon distance"]
+  .cf <- coef(fit)
+  dual.codon.coverage.estimate[,"Estimate % max. possible coverage"] <- exp(.cf[1] + .cf[2] * .dccd + .cf[3] * .dccd^2)
+
+  ## Process the GATK file, only look at single nucleotide variants
+  cat("Sequencing error correction of GATK counts...\\n")
+  out.summary <- matrix(NA, ncol = 7, nrow = length(grep("[,]", input.counts.processed[,"base_mut"], invert = T)))
+  colnames(out.summary) <- c("SNV", "Category",
+                             "False_double_variant_count", "False_double_variant_coverage",
+                             "True_high_conf_variant_count", "True_high_conf_variant_coverage",
+                             "Exposure_m")
+  out.summary[,"SNV"] <- input.counts.processed[grep("[,]", input.counts.processed[,"base_mut"], invert = T),"base_mut"]
+  out.summary[,"Category"] <- str_split_fixed(out.summary[,"SNV"], ":", 2)[,2]
+  category.translate <- cbind(c("C>A", "C>G", "C>T", "T>A", "T>C", "T>G"),
+                              c("G>T", "G>C", "G>A", "A>T", "A>G", "A>C"))
+  for(i in 1:nrow(out.summary)){
+    if(out.summary[i,"Category"] %in% category.translate[,2]){
+      out.summary[i,"Category"] <- category.translate[match(out.summary[i,"Category"],category.translate[,2]),1]
+    }
+  }
+
+  for (i in grep("[,]", input.counts.processed[,"base_mut"], invert = T)){
+
+    ### algorithm:
+    ### 1.) isolate 1nt variant of interest
+    ### 2.) look for 2/3nt hits in a pre-specified window of codons upstream or downstream
+    ### -> if there are zero false double mutants: skip / set correction factor to zero
+    ### -> if there are any false double mutants: continue
+    ### 3.) generate a table for ALL possible 2/3nt variants in the selected window:
+    ### true high-conf. variant count | true high-conf. variant coverage | false double double variant count (often 0 or 1) | false double double variant coverage
+    ### 4.) Iterate of all positions
+    ### 5.) Calculate the empirical Bayes
+    ### 6.) Apply correction factor
+
+    ## 1.) isolate 1nt variant of interest
+    tmp.single <- input.counts.processed[i,"base_mut"]
+    seq.error.rate[tmp.single, "1nt counts/coverage"] <- input.counts.processed[i,"counts_per_cov"]
+
+    ## 2.) look for 2/3nt hits in up to 40 codons (120 bp) upstream or downstream
+    tmp.false.doubles <- input.counts.raw[grep(paste0("(?<!\\\\d)",input.counts.processed[i,"codon_mut"]), input.counts.raw[,"codon_mut"], perl = T),,drop = F]
+
+    ## subset multi-codon variants
+    tmp.false.doubles <- tmp.false.doubles[which(tmp.false.doubles[,"varying_bases"] >= 3 & tmp.false.doubles[,"varying_bases"] < 5 & tmp.false.doubles[,"varying_codons"] == 2),,drop = F]
+    if(nrow(tmp.false.doubles) == 0){
+      next
+    }
+
+    ## set and enforce distance threshold
+    tmp.ref.codon <- as.numeric(strsplit(input.counts.processed[i,"codon_mut"] , ":")[[1]][1])
+    tmp.min.from.ref <- max(c(1, tmp.ref.codon - c(codon_window - 1)))
+    tmp.max.from.ref <- min(c(max.codon, tmp.ref.codon + c(codon_window - 1)))
+    tmp.double.codon <- str_split_fixed(tmp.false.doubles\$codon_mut, ", ", 2)
+    tmp.double.codon.n1 <- as.integer(sub(":.*", "", tmp.double.codon[,1]))
+    tmp.double.codon.n2 <- as.integer(sub(":.*", "", tmp.double.codon[,2]))
+    tmp.double.codon.within.range <- (tmp.double.codon.n1 >= tmp.min.from.ref & tmp.double.codon.n1 <= tmp.max.from.ref) & (tmp.double.codon.n2 >= tmp.min.from.ref & tmp.double.codon.n2 <= tmp.max.from.ref)
+    tmp.false.doubles <- tmp.false.doubles[which(tmp.double.codon.within.range == T),,drop = F]
+    if(nrow(tmp.false.doubles) == 0){
+      next
+    }
+
+    ## 3.) generate a table for ALL possible 2/3nt variants in the selected window
+    tmp.summary.table.entries <- input.counts.processed
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) != tmp.ref.codon),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) >= tmp.min.from.ref &
+                                                                   as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) <= tmp.max.from.ref),]
+    tmp.summary.table.entries <- tmp.summary.table.entries[which(tmp.summary.table.entries\$varying_bases > 1),]
+
+    ### fill the table
+    tmp.summary.table <- matrix(NA, ncol = 5, nrow = nrow(tmp.summary.table.entries))
+    colnames(tmp.summary.table) <- c("codon distance",
+                                     "true high-conf. variant count",
+                                     "true high-conf. variant coverage",
+                                     "false double double variant count",
+                                     "false double double variant coverage")
+    rownames(tmp.summary.table) <- paste0(c(tmp.summary.table.entries\$codon_mut),", ", c(tmp.summary.table.entries\$base_mut))
+
+    tmp.summary.table[,"codon distance"] <- abs(as.numeric(str_split_fixed(tmp.summary.table.entries\$codon_mut, ":", 2)[,1]) - tmp.ref.codon)
+    tmp.summary.table[,"true high-conf. variant count"] <- tmp.summary.table.entries\$counts
+    tmp.summary.table[,"true high-conf. variant coverage"] <- tmp.summary.table.entries\$cov
+    tmp.summary.table[,"false double double variant count"] <- 0
+
+    ## input the actual hits
+    tmp.double.codon <- str_split_fixed(tmp.false.doubles\$codon_mut, ", ", 2)
+    tmp.match1 <- match(tmp.double.codon[,1], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match1[is.na(tmp.match1)] <- 0
+    tmp.match2 <- match(tmp.double.codon[,2], str_split_fixed(rownames(tmp.summary.table), ", ", 2)[,1])
+    tmp.match2[is.na(tmp.match2)] <- 0
+    tmp.match <- c(tmp.match1 + tmp.match2)
+
+    ### remove those false doubles for which there are no matched true high confidence calls
+    if(any(tmp.match == 0)){
+      tmp.false.doubles <- tmp.false.doubles[-which(tmp.match == 0),,drop = F]
+      tmp.match <- tmp.match[-which(tmp.match == 0)]
+      if(nrow(tmp.false.doubles) == 0){
+        next
+      }
+    }
+
+    tmp.summary.table[tmp.match,"false double double variant count"] <- tmp.false.doubles[,"counts"]
+    tmp.summary.table[tmp.match,"false double double variant coverage"] <- tmp.false.doubles[,"cov"]
+    tmp.summary.table <- as.data.frame(tmp.summary.table)
+
+    ## 4.) Iterate over codon positions
+
+    ### summarise data by position
+    tmp.summary.table.pos <- matrix(NA, nrow = length(unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])), ncol = 5)
+    colnames(tmp.summary.table.pos) <- colnames(tmp.summary.table)
+    rownames(tmp.summary.table.pos) <- unique(str_split_fixed(rownames(tmp.summary.table), ">", 2)[,1])
+    tmp.summary.table.pos[,"codon distance"] <- abs(as.numeric(str_split_fixed(rownames(tmp.summary.table.pos), ":", 2)[,1]) - as.numeric(str_split_fixed(input.counts.processed[i,"codon_mut"], ":", 2)[,1]))
+    for(j in 1:nrow(tmp.summary.table.pos)){
+      tmp.name <- rownames(tmp.summary.table.pos)[j]
+      tmp.summary.table.pos[j,"true high-conf. variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant count"])
+      tmp.summary.table.pos[j,"true high-conf. variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"true high-conf. variant coverage"])
+      tmp.summary.table.pos[j,"false double double variant count"] <- sum(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double double variant count"])
+      tmp.summary.table.pos[j,"false double double variant coverage"] <- max(tmp.summary.table[grep(paste0("^",tmp.name), rownames(tmp.summary.table)),"false double double variant coverage"], na.rm = T)
+
+      ## infer a good proxy for the false double variant coverage with 0 counts (based estimated distance-dependent % coverage decay observed across all observed double mutants)
+      if(tmp.summary.table.pos[j,"false double double variant coverage"] == "-Inf"){
+        tmp.summary.table.pos[j,"false double double variant coverage"] <- c(dual.codon.coverage.estimate[abs(tmp.summary.table.pos[j,"codon distance"]),"Estimate % max. possible coverage"] / 100) * tmp.summary.table.pos[j,"true high-conf. variant coverage"]
+        tmp.summary.table.pos[j,"false double double variant coverage"] <- round(tmp.summary.table.pos[j,"false double double variant coverage"])
+      }
+    }
+    tmp.summary.table.pos <- as.data.frame(tmp.summary.table.pos)
+
+    ### fill the master table
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"False_double_variant_count"] <- sum(tmp.summary.table.pos\$`false double double variant count`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"False_double_variant_coverage"] <- sum(tmp.summary.table.pos\$`false double double variant coverage`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"True_high_conf_variant_count"] <- sum(tmp.summary.table.pos\$`true high-conf. variant count`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"True_high_conf_variant_coverage"] <- sum(tmp.summary.table.pos\$`true high-conf. variant coverage`)
+    out.summary[match(tmp.single,out.summary[,"SNV"]),"Exposure_m"] <- sum(tmp.summary.table.pos\$`true high-conf. variant count` * c(tmp.summary.table.pos\$`false double double variant coverage` / tmp.summary.table.pos\$`true high-conf. variant coverage`))
+
+  }
+
+  ## 5.) Calculate the empirical-Bayes sequencing error estimates
+  out.summary <- out.summary[-which(is.na(out.summary[,"False_double_variant_count"]) == T),]
+  out.summary <- as.data.frame(out.summary)
+  class(out.summary\$False_double_variant_count) <- "numeric"
+  class(out.summary\$False_double_variant_coverage) <- "numeric"
+  class(out.summary\$True_high_conf_variant_count) <- "numeric"
+  class(out.summary\$True_high_conf_variant_coverage) <- "numeric"
+  class(out.summary\$Exposure_m) <- "numeric"
+
+  ### calculate e_MLE across all
+  out.summary\$e_MLE <- out.summary\$False_double_variant_count / out.summary\$Exposure_m
+
+  ### calculate all the necessary rates, priors, etc.
+
+  #### global background rate
+  mu_global <- sum(out.summary\$False_double_variant_count) / sum(out.summary\$Exposure_m)
+  mu_global <- max(mu_global, 1e-12) ##
+
+  ##### marginal log-likelihood under Gamma-Poisson with fixed mean mu (ChatGPT function, adapted)
+  ## prior: e ~ Gamma(shape = nu * mu, rate = nu)
+  ## mean = mu, variance = mu / nu
+  gp_loglik_nu <- function(log_nu, y, m, mu) {
+    nu <- exp(log_nu)
+    a <- max(nu * mu, 1e-12)
+
+    # Negative-binomial marginal:
+    # y ~ NB(size = a, prob = nu / (nu + m))
+    ll <- lgamma(y + a) - lgamma(a) - lgamma(y + 1) +
+      a * log(nu / (nu + m)) +
+      y * log(m / (nu + m))
+
+    -sum(ll)
+  }
+  opt_global <- optimize(f = gp_loglik_nu,
+                         interval = c(log(1e-6), log(1e6)),
+                         y = out.summary\$False_double_variant_count,
+                         m = out.summary\$Exposure_m,
+                         mu = mu_global)
+  nu_global <- exp(opt_global\$minimum)
+
+  #### category-level rates, shrunk to global rate
+  mu_cat <- vector(mode = "list", length = 6)
+  names(mu_cat) <- category.translate[,1]
+  mu_cat_raw <- mu_cat
+  for(i in 1:length(mu_cat)){
+    mu_cat[[i]] <- out.summary[which(out.summary\$Category == names(mu_cat)[i]),]
+    mu_cat_raw[[i]] <- sum(mu_cat[[i]]\$False_double_variant_count) / sum(mu_cat[[i]]\$Exposure_m)
+    mu_cat_raw[[i]] <- pmax(mu_cat_raw[[i]], 1e-12)
+    mu_cat[[i]] <- c(sum(mu_cat[[i]]\$False_double_variant_count) + nu_global * mu_global) / c(sum(mu_cat[[i]]\$Exposure_m) + nu_global)
+    mu_cat[[i]] <- pmax(mu_cat[[i]], 1e-12)
+  }
+  mu_cat_raw <- do.call(c, mu_cat_raw)
+  mu_cat <- do.call(c, mu_cat)
+
+  ##### category-specific prior strength nu_cat (ChatGPT function, adapted)
+  ## with fallback to global if category is too small
+  nu_cat <- rep(NA, 6)
+  names(nu_cat) <- names(mu_cat)
+  for (i in 1:length(mu_cat)) {
+    cat_name <- names(mu_cat)[i]
+    idx <- out.summary\$Category == cat_name
+
+    yk <- out.summary\$False_double_variant_count[idx]
+    mk <- out.summary\$Exposure_m[idx]
+    mu_k <- mu_cat[i]
+
+    opt_k <- try(optimize(f = gp_loglik_nu,
+                          interval = c(log(1e-6), log(1e6)),
+                          y = yk,
+                          m = mk,
+                          mu = mu_k),
+                 silent = TRUE)
+
+    if (inherits(opt_k, "try-error")) {
+      nu_cat[i] <- nu_global
+    } else {
+      nu_cat[i] <- exp(opt_k\$minimum)
+    }
+  }
+
+  #### posterior mean for each specific SNV
+  out.summary\$mu_category <- rep(NA, nrow(out.summary))
+  out.summary\$nu_category <- rep(NA, nrow(out.summary))
+  for(i in 1:6){
+    out.summary\$mu_category[which(out.summary\$Category == names(mu_cat)[i])] <- mu_cat[i]
+    out.summary\$nu_category[which(out.summary\$Category == names(nu_cat)[i])] <- nu_cat[i]
+  }
+
+  ##### prior: e_j ~ Gamma(shape = nu_cat * mu_cat, rate = nu_cat)
+  out.summary\$prior_shape <- out.summary\$mu_category * out.summary\$nu_category
+  out.summary\$prior_rate <- out.summary\$nu_category
+
+  ##### posterior: e_j | data ~ Gamma(shape = prior_shape + F, rate = prior_rate [= nu_cat] + M)
+  out.summary\$post_shape <- out.summary\$prior_shape + out.summary\$False_double_variant_count
+  out.summary\$post_rate  <- out.summary\$nu_category + out.summary\$Exposure_m
+
+  ##### posterior mean = shrunk EB estimate
+  out.summary\$e_EB  <- out.summary\$post_shape / out.summary\$post_rate
+
+  ##### other posterior intervals
+  # out.summary\$e_EB_upper_1SD <- qgamma(0.68, shape = out.summary\$post_shape, rate = out.summary\$post_rate)
+  # out.summary\$e_EB_upper_2SD <- qgamma(0.95, shape = out.summary\$post_shape, rate = out.summary\$post_rate)
+  # out.summary\$e_EB_upper_3SD <- qgamma(0.997, shape = out.summary\$post_shape, rate = out.summary\$post_rate)
+
+  ## 6.) Apply correction factors systematically (use upper 95% percentile to correct, a.k.a. "conservative")
+  seq.error.rate[match(out.summary\$SNV, rownames(seq.error.rate)),"1nt false counts/coverage"] <- out.summary\$e_EB
+  input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_per_cov_corrected"] <- input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_per_cov"] - out.summary\$e_EB
+  input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_corrected"] <- input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts"] - c(out.summary\$e_EB * input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"cov"])
+
+  ## round to nearest integer, do not allow for negative counts
+  input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_corrected"] <- round(input.counts.processed[match(out.summary\$SNV, input.counts.processed\$base_mut),"counts_corrected"])
+  if(any(which(input.counts.processed[,"counts_corrected"] < 0))){
+    input.counts.processed[which(input.counts.processed[,"counts_corrected"] < 0),"counts_per_cov_corrected"] <- 0
+    input.counts.processed[which(input.counts.processed[,"counts_corrected"] < 0),"counts_corrected"] <- 0
+  }
+
+  # Write the processed data
+  write.csv(seq.error.rate, file = seq_error_rate_path, row.names = T)
+  write.csv(input.counts.processed, file = output_file_path, row.names = F)
+
+}
+
+## -------------------------------------------------------------------------------------------------
+## I/O wrappers preserved from the pipeline module (not part of Maxi's estimator logic)
+## -------------------------------------------------------------------------------------------------
+
+# propagate corrected counts onto the completed library table (used by e.g. logdiff)
+correct_library_completed <- function(completed_path, corrected_filtered_path, output_file_path){
+  completed <- read.csv(completed_path)
+  corrected <- read.csv(corrected_filtered_path)
+  idx <- match(completed\$codon_mut, corrected\$codon_mut)
+  has <- !is.na(idx)
+  completed\$counts[has] <- corrected\$counts[idx[has]]
+  completed\$counts_per_cov[has] <- corrected\$counts_per_cov[idx[has]]
+  write.csv(completed, file = output_file_path, row.names = FALSE)
+}
+
+# re-make the AA-level heatmap table from corrected counts (canonical column names)
+seq_error_correct_counts_for_heatmaps <- function(input_csv_path, aa_seq_file_path, output_csv_path, threshold = 3) {
+
+  raw_counts <- read.table(input_csv_path, sep = ",", header = TRUE, stringsAsFactors = FALSE)
+
+  wt_seq <- readLines(aa_seq_file_path)
+  wt_seq <- unlist(strsplit(wt_seq, ""))
+  wt_seq[wt_seq == "X"] <- "*"
+
+  # summarise corrected counts for each unique pos_mut
+  aggregated_counts_per_cov <- aggregate(
+    counts_per_cov_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_counts <- aggregate(
+    counts_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_data <- merge(aggregated_counts_per_cov, aggregated_counts, by = "pos_mut", all = TRUE)
+
+  # canonical output names so the count heatmaps consume this drop-in
+  names(aggregated_data)[names(aggregated_data) == "counts_per_cov_corrected"] <- "total_counts_per_cov"
+  names(aggregated_data)[names(aggregated_data) == "counts_corrected"] <- "total_counts"
+
+  aggregated_data\$wt_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\1", aggregated_data\$pos_mut)
+  aggregated_data\$position <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", aggregated_data\$pos_mut))
+  aggregated_data\$mut_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\3", aggregated_data\$pos_mut)
+  aggregated_data\$mut_aa[aggregated_data\$mut_aa == "X"] <- "*"
+
+  all_amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L",
+                       "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y", "*")
+  all_positions <- seq_along(wt_seq)
+  complete_data <- expand.grid(mut_aa = all_amino_acids, position = all_positions, stringsAsFactors = FALSE)
+
+  heatmap_data <- merge(complete_data, aggregated_data, by = c("mut_aa", "position"), all.x = TRUE, sort = FALSE)
+
+  heatmap_data\$total_counts_per_cov[is.na(heatmap_data\$total_counts_per_cov)] <- 0
+  heatmap_data\$wt_aa <- wt_seq[heatmap_data\$position]
+
+  low_count <- is.na(heatmap_data\$total_counts) | heatmap_data\$total_counts < threshold
+  heatmap_data\$total_counts_per_cov[low_count] <- NA
+  heatmap_data\$total_counts[low_count] <- NA
+
+  missing_pos_mut <- is.na(heatmap_data\$pos_mut)
+  heatmap_data\$pos_mut[missing_pos_mut] <- paste0(
+    heatmap_data\$wt_aa[missing_pos_mut],
+    heatmap_data\$position[missing_pos_mut],
+    heatmap_data\$mut_aa[missing_pos_mut]
+  )
+
+  out.order <- paste0(rep(1:max(heatmap_data\$position), each = 21),
+                      rep(c("A", "C", "D", "E", "F", "G", "H",
+                            "I", "K", "L","M", "N", "P", "Q",
+                            "R", "S", "T", "V", "W", "Y", "*"),
+                          max(heatmap_data\$position)))
+  heatmap_data <- heatmap_data[match(out.order, paste0(heatmap_data\$position, heatmap_data\$mut_aa)),]
+  rownames(heatmap_data) <- 1:nrow(heatmap_data)
+
+  write.csv(heatmap_data, file = output_csv_path, row.names = FALSE)
+
+}
+
+# Defensive coordinate-contract check (general, not GID1A-specific): base_mut positions must be
+# 1-based within the supplied reference, and the stated WT base must match the reference base there.
+# A mismatch means the reference and the counts are in different frames -> stop rather than
+# silently mis-index seq.error.rate.
+check_reference_frame <- function(wt_path, processed_path){
+  ref <- strsplit(as.character(readDNAStringSet(wt_path)[[1]]), "")[[1]]
+  proc <- read.csv(processed_path)
+  snv <- proc[grep("[,]", proc[,"base_mut"], invert = TRUE), "base_mut"]
+  if(length(snv) == 0) return(invisible(NULL))
+  pos <- suppressWarnings(as.integer(str_split_fixed(snv, ":", 2)[,1]))
+  wtbase <- substr(str_split_fixed(snv, ":", 2)[,2], 1, 1)
+  ok <- !is.na(pos) & pos >= 1 & pos <= length(ref)
+  if(any(!ok)){
+    stop("Reference/counts coordinate mismatch: ", sum(!ok), " single-nucleotide base_mut positions ",
+         "fall outside the reference (length ", length(ref), "). The reference fasta must be the same ",
+         "one the reads were aligned to, in the same coordinate frame as base_mut.")
+  }
+  mism <- ref[pos] != wtbase
+  if(any(mism, na.rm = TRUE)){
+    stop("Reference/counts coordinate mismatch: ", sum(mism, na.rm = TRUE), " single-nucleotide ",
+         "base_mut entries state a wild-type base that disagrees with the reference at that position ",
+         "(e.g. ", snv[which(mism)[1]], "). Check that --fasta matches the counts.")
+  }
+  invisible(NULL)
+}
+
+## -------------------------------------------------------------------------------------------------
+## Run
+## -------------------------------------------------------------------------------------------------
+method <- "$method"
+codon_window <- $codon_window
+
+# fail loudly if the supplied reference and the counts are in different coordinate frames
+check_reference_frame("$wt_fasta", "$filtered")
+
+intermediate <- "variantCounts_intermediate_corrected.csv"
+if (method == "eb") {
+  seq_error_correct_by_false_doubles_EB(
+    wt_path = "$wt_fasta",
+    input_count_path_raw = "$raw_counts",
+    input_count_path_processed = "$filtered",
+    output_file_path = intermediate,
+    seq_error_rate_path = "seq_error_rate.csv",
+    codon_window = codon_window
+  )
+} else {
+  seq_error_correct_by_false_doubles_MLE(
+    wt_path = "$wt_fasta",
+    input_count_path_raw = "$raw_counts",
+    input_count_path_processed = "$filtered",
+    output_file_path = intermediate,
+    seq_error_rate_path = "seq_error_rate.csv",
+    codon_window = codon_window
+  )
+}
+
+## corrected counts become the canonical columns so every count-dependent step uses them;
+## the originals are preserved as *_raw for the error-correction report
+ic <- read.csv(intermediate)
+ic\$counts_raw <- ic\$counts
+ic\$counts_per_cov_raw <- ic\$counts_per_cov
+ic\$counts <- ic\$counts_corrected
+ic\$counts_per_cov <- ic\$counts_per_cov_corrected
+write.csv(ic, file = "variantCounts_filtered_by_library_error_corrected.csv", row.names = FALSE)
+
+seq_error_correct_counts_for_heatmaps(
+  input_csv_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  aa_seq_file_path = "$aa_seq",
+  output_csv_path = "variantCounts_for_heatmaps_error_corrected.csv",
+  threshold = $min_counts
+)
+
+correct_library_completed(
+  completed_path = "$completed",
+  corrected_filtered_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  output_file_path = "library_completed_variantCounts_error_corrected.csv"
+)
+
+## --- versions ---
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+if (is.null(r_version)) r_version <- "unknown"
+stringr_version <- as.character(packageVersion("stringr"))
+biostrings_version <- as.character(packageVersion("Biostrings"))
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-stringr:', stringr_version),
+    paste('    r-Biostrings:', biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/environment.yml b/modules/local/dmsanalysis/error_correction_wildtype/environment.yml
new file mode 100644
index 0000000..9db6b17
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/environment.yml
@@ -0,0 +1,4 @@
+channels:
+  - conda-forge
+dependencies:
+  - conda-forge::r-base=4.5.1
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/main.nf b/modules/local/dmsanalysis/error_correction_wildtype/main.nf
new file mode 100644
index 0000000..0937129
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/main.nf
@@ -0,0 +1,38 @@
+process DMSANALYSIS_ERROR_CORRECTION_WILDTYPE {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(filtered, stageAs: 'input_counts.csv'), path(wt_counts, stageAs: 'wildtype_counts.csv'), path(completed)
+    path aa_seq
+    val min_counts
+
+    output:
+    tuple val(meta),
+        path("variantCounts_filtered_by_library_error_corrected.csv"),
+        path("variantCounts_for_heatmaps_error_corrected.csv"),
+        path("library_completed_variantCounts_error_corrected.csv"),
+        emit: corrected
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'error_correction_wildtype.R'
+
+    stub:
+    """
+    touch variantCounts_filtered_by_library_error_corrected.csv
+    touch variantCounts_for_heatmaps_error_corrected.csv
+    touch library_completed_variantCounts_error_corrected.csv
+    echo "DMSANALYSIS_ERROR_CORRECTION_WILDTYPE:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/meta.yml b/modules/local/dmsanalysis/error_correction_wildtype/meta.yml
new file mode 100644
index 0000000..8a38af2
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/meta.yml
@@ -0,0 +1,66 @@
+name: "dmsanalysis_error_correction_wildtype"
+description: "Sequencing-error correction of single-codon variant counts using an additional deep wildtype-only sequencing sample to measure the position-specific error profile, which is then subtracted from each library sample."
+keywords:
+  - deep mutational scanning
+  - error correction
+  - wildtype sequencing
+  - variant counting
+tools:
+  - "r-base":
+      description: "The R programming language (base only)"
+      homepage: "https://www.r-project.org/"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1', type:'input', replicate:1 ]`
+    - filtered:
+        type: file
+        description: Library-filtered single-codon variant counts (annotated CSV) for this sample.
+        pattern: "*.csv"
+    - wt_counts:
+        type: file
+        description: Library-filtered variant counts of the matching wildtype-only sample.
+        pattern: "*.csv"
+    - completed:
+        type: file
+        description: Completed library table (all possible variants) to also correct.
+        pattern: "*.csv"
+  - - aa_seq:
+        type: file
+        description: Reference amino-acid sequence (one line).
+  - - min_counts:
+        type: integer
+        description: Minimum count threshold for the amino-acid-level heatmap table.
+
+output:
+  - corrected:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - variantCounts_filtered_by_library_error_corrected.csv:
+          type: file
+          description: Library-filtered counts with added `counts_corrected` / `counts_per_cov_corrected` columns.
+          pattern: "variantCounts_filtered_by_library_error_corrected.csv"
+      - variantCounts_for_heatmaps_error_corrected.csv:
+          type: file
+          description: Amino-acid-level heatmap table built from corrected counts (canonical `total_counts` columns).
+          pattern: "variantCounts_for_heatmaps_error_corrected.csv"
+      - library_completed_variantCounts_error_corrected.csv:
+          type: file
+          description: Completed library table with corrected counts propagated (for logdiff etc.).
+          pattern: "library_completed_variantCounts_error_corrected.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
+maintainers:
+  - "@BenjaminWehnert1008"
diff --git a/modules/local/dmsanalysis/error_correction_wildtype/templates/error_correction_wildtype.R b/modules/local/dmsanalysis/error_correction_wildtype/templates/error_correction_wildtype.R
new file mode 100644
index 0000000..1e82965
--- /dev/null
+++ b/modules/local/dmsanalysis/error_correction_wildtype/templates/error_correction_wildtype.R
@@ -0,0 +1,159 @@
+#!/usr/bin/env Rscript
+
+## Wildtype-based sequencing error correction in nf-core/deepmutscan
+## An additional deep wildtype-only sequencing sample measures the position-specific error
+## profile, which is subtracted from each library sample's per-base coverage.
+
+## --- Helper functions ---
+
+# WT-based sequencing error correction (nucleotide level)
+seq_error_correct_by_WT_nt <- function(wt_seq_count_path, input_count_path, output_file_path){
+
+  WT.counts <- read.csv(wt_seq_count_path)
+  input.counts <- read.csv(input_count_path)
+
+  ## append key columns
+  input.counts\$counts_corrected <- input.counts\$counts
+  input.counts\$counts_per_cov_corrected <- input.counts\$counts_per_cov
+
+  for (i in 1:nrow(WT.counts)){
+
+    ## only look at variants observed in both the input and WT sequencing
+    tmp.id <- match(WT.counts[i,"base_mut"], input.counts[,"base_mut"])
+    if(is.na(tmp.id) == T){
+      next
+    }
+
+    ## subtract the observed per-base coverage in the WT sequencing
+    input.counts[tmp.id,"counts_per_cov_corrected"] <- input.counts[tmp.id,"counts_per_cov"] - WT.counts[i,"counts_per_cov"]
+
+    ## adjust the total counts by cross-multiplication
+    input.counts[tmp.id,"counts_corrected"] <- input.counts[tmp.id,"counts"] * c(input.counts[tmp.id,"counts_per_cov_corrected"] / input.counts[tmp.id,"counts_per_cov"])
+
+    ## round to nearest integer, do not allow for negative counts
+    input.counts[tmp.id,"counts_corrected"] <- round(input.counts[tmp.id,"counts_corrected"])
+    if(input.counts[tmp.id,"counts_corrected"] < 0){
+      input.counts[tmp.id,"counts_corrected"] <- 0
+      input.counts[tmp.id,"counts_per_cov_corrected"] <- 0
+    }
+
+  }
+
+  ## corrected counts become the canonical columns so every count-dependent step uses them;
+  ## the originals are preserved as *_raw for the error-correction report
+  input.counts\$counts_raw <- input.counts\$counts
+  input.counts\$counts_per_cov_raw <- input.counts\$counts_per_cov
+  input.counts\$counts <- input.counts\$counts_corrected
+  input.counts\$counts_per_cov <- input.counts\$counts_per_cov_corrected
+
+  write.csv(input.counts, file = output_file_path, row.names = F)
+
+}
+
+# propagate corrected counts onto the completed library table (used by e.g. logdiff)
+correct_library_completed <- function(completed_path, corrected_filtered_path, output_file_path){
+  completed <- read.csv(completed_path)
+  corrected <- read.csv(corrected_filtered_path)
+  idx <- match(completed\$codon_mut, corrected\$codon_mut)
+  has <- !is.na(idx)
+  completed\$counts[has] <- corrected\$counts[idx[has]]
+  completed\$counts_per_cov[has] <- corrected\$counts_per_cov[idx[has]]
+  write.csv(completed, file = output_file_path, row.names = FALSE)
+}
+
+# re-make the AA-level heatmap table from corrected counts (canonical column names)
+seq_error_correct_counts_for_heatmaps <- function(input_csv_path, aa_seq_file_path, output_csv_path, threshold = 3) {
+
+  raw_counts <- read.table(input_csv_path, sep = ",", header = TRUE, stringsAsFactors = FALSE)
+
+  wt_seq <- readLines(aa_seq_file_path)
+  wt_seq <- unlist(strsplit(wt_seq, ""))
+  wt_seq[wt_seq == "X"] <- "*"
+
+  # summarise corrected counts for each unique pos_mut
+  aggregated_counts_per_cov <- aggregate(
+    counts_per_cov_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_counts <- aggregate(
+    counts_corrected ~ pos_mut,
+    data = raw_counts,
+    FUN = function(x) sum(x, na.rm = TRUE)
+  )
+  aggregated_data <- merge(aggregated_counts_per_cov, aggregated_counts, by = "pos_mut", all = TRUE)
+
+  # canonical output names so the count heatmaps consume this drop-in
+  names(aggregated_data)[names(aggregated_data) == "counts_per_cov_corrected"] <- "total_counts_per_cov"
+  names(aggregated_data)[names(aggregated_data) == "counts_corrected"] <- "total_counts"
+
+  aggregated_data\$wt_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\1", aggregated_data\$pos_mut)
+  aggregated_data\$position <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", aggregated_data\$pos_mut))
+  aggregated_data\$mut_aa <- sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\3", aggregated_data\$pos_mut)
+  aggregated_data\$mut_aa[aggregated_data\$mut_aa == "X"] <- "*"
+
+  all_amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L",
+                       "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y", "*")
+  all_positions <- seq_along(wt_seq)
+  complete_data <- expand.grid(mut_aa = all_amino_acids, position = all_positions, stringsAsFactors = FALSE)
+
+  heatmap_data <- merge(complete_data, aggregated_data, by = c("mut_aa", "position"), all.x = TRUE, sort = FALSE)
+
+  heatmap_data\$total_counts_per_cov[is.na(heatmap_data\$total_counts_per_cov)] <- 0
+  heatmap_data\$wt_aa <- wt_seq[heatmap_data\$position]
+
+  low_count <- is.na(heatmap_data\$total_counts) | heatmap_data\$total_counts < threshold
+  heatmap_data\$total_counts_per_cov[low_count] <- NA
+  heatmap_data\$total_counts[low_count] <- NA
+
+  missing_pos_mut <- is.na(heatmap_data\$pos_mut)
+  heatmap_data\$pos_mut[missing_pos_mut] <- paste0(
+    heatmap_data\$wt_aa[missing_pos_mut],
+    heatmap_data\$position[missing_pos_mut],
+    heatmap_data\$mut_aa[missing_pos_mut]
+  )
+
+  out.order <- paste0(rep(1:max(heatmap_data\$position), each = 21),
+                      rep(c("A", "C", "D", "E", "F", "G", "H",
+                            "I", "K", "L","M", "N", "P", "Q",
+                            "R", "S", "T", "V", "W", "Y", "*"),
+                          max(heatmap_data\$position)))
+  heatmap_data <- heatmap_data[match(out.order, paste0(heatmap_data\$position, heatmap_data\$mut_aa)),]
+  rownames(heatmap_data) <- 1:nrow(heatmap_data)
+
+  write.csv(heatmap_data, file = output_csv_path, row.names = FALSE)
+
+}
+
+## --- Run ---
+seq_error_correct_by_WT_nt(
+  wt_seq_count_path = "$wt_counts",
+  input_count_path = "$filtered",
+  output_file_path = "variantCounts_filtered_by_library_error_corrected.csv"
+)
+
+seq_error_correct_counts_for_heatmaps(
+  input_csv_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  aa_seq_file_path = "$aa_seq",
+  output_csv_path = "variantCounts_for_heatmaps_error_corrected.csv",
+  threshold = $min_counts
+)
+
+correct_library_completed(
+  completed_path = "$completed",
+  corrected_filtered_path = "variantCounts_filtered_by_library_error_corrected.csv",
+  output_file_path = "library_completed_variantCounts_error_corrected.csv"
+)
+
+## --- versions ---
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+if (is.null(r_version)) r_version <- "unknown"
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/possible_mutations/environment.yml b/modules/local/dmsanalysis/possible_mutations/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/possible_mutations/main.nf b/modules/local/dmsanalysis/possible_mutations/main.nf
new file mode 100644
index 0000000..e98eb0f
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/main.nf
@@ -0,0 +1,33 @@
+process DMSANALYSIS_POSSIBLE_MUTATIONS {
+    tag "table /w all possible variants"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(wt_seq)
+    val pos_range
+    val mutagenesis_type
+    path custom_codon_library
+
+    output:
+    tuple val(meta), path("possible_mutations.csv"), emit: possible_mutations
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'possible_mutations.R'
+
+    stub:
+    """
+    touch possible_mutations.csv
+    echo "DMSANALYSIS_POSSIBLE_MUTATIONS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/possible_mutations/meta.yml b/modules/local/dmsanalysis/possible_mutations/meta.yml
new file mode 100644
index 0000000..4fbd8c3
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/meta.yml
@@ -0,0 +1,63 @@
+name: "dmsanalysis_possible_mutations"
+description: |
+  Generates a comprehensive table of all theoretically possible variants based on the
+  mutagenesis strategy (e.g., NNK, NNS) and the reference sequence. This table acts
+  as the ground truth for downstream filtering and analysis.
+keywords:
+  - deep mutational scanning
+  - dms
+  - mutagenesis
+  - library design
+  - variants
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'ref' ]`
+    - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type reference DNA sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions (ORF) in the format 'start-stop', e.g., '352-1383'
+  - - mutagenesis_type:
+        type: string
+        description: |
+          Type of mutagenic primers used.
+          Supported types: nnk, nns, nnh, nnn, nnk_nns, nnk_nns_nnh, or custom.
+  - - custom_codon_library:
+        type: file
+        description: |
+          Optional CSV file defining a custom codon library.
+          Only required if mutagenesis_type is set to 'custom'.
+
+output:
+  - possible_mutations:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - possible_mutations.csv:
+          type: file
+          description: CSV file containing all theoretically possible mutations for the experiment
+          pattern: "possible_mutations.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/dmsanalysis/possible_mutations/templates/possible_mutations.R b/modules/local/dmsanalysis/possible_mutations/templates/possible_mutations.R
new file mode 100644
index 0000000..13a0702
--- /dev/null
+++ b/modules/local/dmsanalysis/possible_mutations/templates/possible_mutations.R
@@ -0,0 +1,197 @@
+#!/usr/bin/env Rscript
+
+# ------------------------------------------------------------------------------
+# Script: Generate Programmed Codon Variants
+# Description: Generates all possible programmed codon mutations for a given
+#              wild-type sequence based on a specified mutagenesis strategy.
+# Input:
+#   - wt_seq_input: Wild-type sequence (string or path to FASTA file).
+#   - start_stop_pos: Target sequence range format "start-stop".
+#   - mutagenesis_type: Strategy ('nnk', 'nns', 'nnh', 'nnn', 'nnk_nns', 'nnk_nns_nnh', 'custom').
+#   - custom_codon_library_path: Path to custom library. Automatically detects
+#     if the file is a global list ("AAA, AAC...") or a position-wise CSV
+#     (requires a "Position" header).
+#   - output_file: Desired name/path for the output CSV.
+# Output: A CSV file containing all possible mutated codons per position.
+# ------------------------------------------------------------------------------
+
+suppressMessages(library(Biostrings))
+suppressMessages(library(methods))
+
+generate_possible_variants <- function(wt_seq_input, start_stop_pos, mutagenesis_type,
+                                       custom_codon_library_path, output_file) {
+
+  # Parse the start and stop positions from the input format "start-stop"
+  positions <- unlist(strsplit(start_stop_pos, "-"))
+  start_pos <- as.numeric(positions[1])
+  stop_pos <- as.numeric(positions[2])
+
+  # Load sequence from file or process as a direct string
+  if (file.exists(wt_seq_input)) {
+    seq_data <- Biostrings::readDNAStringSet(filepath = wt_seq_input)
+    wt_seq <- seq_data[[1]]
+  } else {
+    wt_seq <- Biostrings::DNAString(wt_seq_input)
+  }
+
+  # Extract the target coding sequence
+  coding_seq <- Biostrings::subseq(wt_seq, start = start_pos, end = stop_pos)
+  coding_seq <- as.character(coding_seq)
+
+  # Predefined codon dictionaries
+  nnk_codons <- c('AAG', 'AAT', 'ATG', 'ATT', 'AGG', 'AGT', 'ACG', 'ACT', 'TAG', 'TAT', 'TTG', 'TTT', 'TGG', 'TGT', 'TCG', 'TCT', 'GAG', 'GAT', 'GTG', 'GTT', 'GGG', 'GGT', 'GCG', 'GCT', 'CAG', 'CAT', 'CTG', 'CTT', 'CGG', 'CGT', 'CCG', 'CCT')
+  nns_codons <- c('AAG', 'AAC', 'ATG', 'ATC', 'AGG', 'AGC', 'ACG', 'ACC', 'TAG', 'TAC', 'TTG', 'TTC', 'TGG', 'TGC', 'TCG', 'TCC', 'GAG', 'GAC', 'GTG', 'GTC', 'GGG', 'GGC', 'GCG', 'GCC', 'CAG', 'CAC', 'CTG', 'CTC', 'CGG', 'CGC', 'CCG', 'CCC')
+  nnh_codons <- c('AAA', 'AAC', 'AAT', 'ATA', 'ATC', 'ATT', 'AGA', 'AGC', 'AGT', 'ACA', 'ACC', 'ACT', 'TAA', 'TAC', 'TAT', 'TTA', 'TTC', 'TTT', 'TGA', 'TGC', 'TGT', 'TCA', 'TCC', 'TCT', 'GAA', 'GAC', 'GAT', 'GTA', 'GTC', 'GTT', 'GGA', 'GGC', 'GGT', 'GCA', 'GCC', 'GCT', 'CAA', 'CAC', 'CAT', 'CTA', 'CTC', 'CTT', 'CGA', 'CGC', 'CGT', 'CCA', 'CCC', 'CCT')
+  nnn_codons <- c('AAA', 'AAC', 'AAG', 'AAT', 'ATA', 'ATC', 'ATG', 'ATT', 'AGA', 'AGC', 'AGG', 'AGT', 'ACA', 'ACC', 'ACG', 'ACT', 'TAA', 'TAC', 'TAG', 'TAT', 'TTA', 'TTC', 'TTG', 'TTT', 'TGA', 'TGC', 'TGG', 'TGT', 'TCA', 'TCC', 'TCG', 'TCT', 'GAA', 'GAC', 'GAG', 'GAT', 'GTA', 'GTC', 'GTG', 'GTT', 'GGA', 'GGC', 'GGG', 'GGT', 'GCA', 'GCC', 'GCG', 'GCT', 'CAA', 'CAC', 'CAG', 'CAT', 'CTA', 'CTC', 'CTG', 'CTT', 'CGA', 'CGC', 'CGG', 'CGT', 'CCA', 'CCC', 'CCG', 'CCT')
+
+  # --------------------------------------------------------------------------
+  # Custom Library Parsing with Auto-Detection
+  # --------------------------------------------------------------------------
+  is_position_wise <- FALSE
+  position_lookup <- list()
+  custom_codons <- NULL
+
+  if (mutagenesis_type == "custom") {
+    if (!file.exists(custom_codon_library_path) || is.null(custom_codon_library_path)) {
+      stop("Custom codons file must be provided and valid when using 'custom' mutagenesis_type.")
+    }
+
+    # Auto-detect format by inspecting the first line
+    first_line <- readLines(custom_codon_library_path, n = 1)
+
+    if (grepl("Position", first_line, ignore.case = TRUE)) {
+      # Format 1: Position-wise CSV file
+      is_position_wise <- TRUE
+
+      # Read line-by-line instead of read.csv to avoid strict column matching errors
+      lines <- readLines(custom_codon_library_path)
+
+      # Loop through lines, skipping the header (index 1)
+      for (line in lines[-1]) {
+        # Skip empty lines
+        if (trimws(line) == "") next
+
+        # Split the line by commas
+        parts <- trimws(unlist(strsplit(line, ",")))
+
+        # The first part is the position, everything else are the codons
+        pos_idx <- parts[1]
+        codon_vec <- parts[-1]
+
+        # Remove any accidental empty strings (e.g., from trailing commas)
+        codon_vec <- codon_vec[codon_vec != ""]
+
+        position_lookup[[pos_idx]] <- codon_vec
+      }
+    } else {
+      # Format 2: Global comma-separated list (Legacy compatibility)
+      custom_codons <- unlist(strsplit(readLines(custom_codon_library_path), ","))
+      custom_codons <- trimws(custom_codons)
+    }
+  }
+
+  # Helper function to split a DNA sequence into nucleotide triplets
+  split_into_codons <- function(seq) {
+    # Note: Double escaping is required for Perl regular expressions here
+    return(strsplit(seq, "(?<=.{3})", perl = TRUE)[[1]])
+  }
+
+  wt_codons <- split_into_codons(coding_seq)
+
+  # Initialize dataframe to store final variant results
+  # Note: \$ escaping is maintained for Nextflow compatibility
+  result <- data.frame(Codon_Number = integer(), wt_codon = character(), Variant = character(), stringsAsFactors = FALSE)
+
+  # Helper function to determine the target codon list per position
+  get_codon_list <- function(wt_codon, codon_index) {
+    if (mutagenesis_type == "nnk") {
+      return(nnk_codons)
+    } else if (mutagenesis_type == "nns") {
+      return(nns_codons)
+    } else if (mutagenesis_type == "nnh") {
+            return(nnh_codons)
+    } else if (mutagenesis_type == "nnn") {
+            return(nnn_codons)
+    } else if (mutagenesis_type == "nnk_nns") {
+      if (substr(wt_codon, 3, 3) == "T") return(nns_codons) else return(nnk_codons)
+    } else if (mutagenesis_type == "nnk_nns_nnh") {
+            if (substr(wt_codon, 3, 3) == "T") {
+                return(nns_codons)
+            } else if (substr(wt_codon, 3, 3) == "G"){
+                return(nnh_codons)
+            } else {
+                return(nnk_codons)
+            }
+    } else if (mutagenesis_type == "custom") {
+      if (is_position_wise) {
+        idx_str <- as.character(codon_index)
+        if (!is.null(position_lookup[[idx_str]])) {
+          return(position_lookup[[idx_str]])
+        } else {
+          return(NULL) # Skip positions not explicitly defined in the CSV
+        }
+      } else {
+        return(custom_codons)
+      }
+    } else {
+      stop("Invalid mutagenesis_type. Choose from 'nnk', 'nns', 'nnh', 'nnn', 'nnk_nns', 'nnk_nns_nnh', or 'custom'.")
+    }
+  }
+
+  # Iterate over each wild-type codon to assign programmed variants
+  for (i in seq_along(wt_codons)) {
+    wt_codon <- wt_codons[i]
+    codon_list <- get_codon_list(wt_codon, i)
+
+    # Skip iteration if no custom codons were assigned to this specific position
+    if (is.null(codon_list)) next
+
+    # Filter out the wild-type codon from the mutation list
+    possible_variants <- codon_list[codon_list != wt_codon]
+
+    for (variant in possible_variants) {
+      # Note: \$ escaping is maintained for Nextflow compatibility
+      result <- rbind(result, data.frame(Codon_Number = i, wt_codon = wt_codon, Variant = variant, stringsAsFactors = FALSE))
+    }
+  }
+
+  write.csv(result, output_file, row.names = FALSE)
+}
+
+# ------------------------------------------------------------------------------
+# Main Execution Block (Nextflow variable substitution)
+# ------------------------------------------------------------------------------
+
+# Replaces bash if/else logic. If Nextflow omits the optional file,
+# it passes "/NULL", which R translates to an actual NULL object.
+custom_lib_arg <- if ("$custom_codon_library" == "/NULL") {
+  NULL
+} else {
+  "$custom_codon_library"
+}
+
+generate_possible_variants(
+  wt_seq_input              = "$wt_seq",
+  start_stop_pos            = "$pos_range",
+  mutagenesis_type          = "$mutagenesis_type",
+  custom_codon_library_path = "$custom_codon_library",
+  output_file               = "possible_mutations.csv"
+)
+
+# ------------------------------------------------------------------------------
+# Versioning Generation
+# ------------------------------------------------------------------------------
+
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+biostrings_version <- as.character(packageVersion("Biostrings"))
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    biostrings:', biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/dmsanalysis/process_variant_counts/environment.yml b/modules/local/dmsanalysis/process_variant_counts/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/dmsanalysis/process_variant_counts/main.nf b/modules/local/dmsanalysis/process_variant_counts/main.nf
new file mode 100644
index 0000000..c8a6347
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/main.nf
@@ -0,0 +1,41 @@
+process DMSANALYSIS_PROCESS_VARIANT_COUNTS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    publishDir "${params.outdir}/intermediate_files", mode: 'copy'
+
+    input:
+    tuple val(meta), path(variantCounts)
+    path possible_mutations
+    path aa_seq
+    val min_counts
+
+    output:
+    tuple val(meta),
+        path("annotated_variantCounts.csv"),
+        path("variantCounts_filtered_by_library.csv"),
+        path("library_completed_variantCounts.csv"),
+        path("variantCounts_for_heatmaps.csv"),
+        emit: processed_variantCounts
+
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'process_variant_counts.R'
+
+    stub:
+    """
+    touch annotated_variantCounts.csv variantCounts_filtered_by_library.csv library_completed_variantCounts.csv variantCounts_for_heatmaps.csv
+    echo "DMSANALYSIS_PROCESS_VARIANT_COUNTS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/dmsanalysis/process_variant_counts/meta.yml b/modules/local/dmsanalysis/process_variant_counts/meta.yml
new file mode 100644
index 0000000..c72060b
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/meta.yml
@@ -0,0 +1,69 @@
+name: "dmsanalysis_process_variant_counts"
+description: "Processes the raw variant count table, filters it against expected libraries, and prepares data for downstream visualization and fitness calculation."
+keywords:
+  - deep mutational scanning
+  - variant counting
+  - variant filtering
+  - dms
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts:
+        type: file
+        description: TSV file containing the raw variant counts
+        pattern: "*.{tsv,csv}"
+  - - possible_mutations:
+        type: file
+        description: CSV file containing all theoretically possible mutations based on library design
+        pattern: "*.{csv}"
+  - - aa_seq:
+        type: file
+        description: FASTA or text file containing the reference amino acid sequence
+  - - min_counts:
+        type: integer
+        description: Minimum count threshold to consider a variant valid
+
+output:
+  - processed_variantCounts:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - annotated_variantCounts.csv:
+          type: file
+          description: Raw variant counts annotated with mutation details
+          pattern: "annotated_variantCounts.csv"
+      - variantCounts_filtered_by_library.csv:
+          type: file
+          description: Variant counts filtered to include only those present in the provided library
+          pattern: "variantCounts_filtered_by_library.csv"
+      - library_completed_variantCounts.csv:
+          type: file
+          description: Full library table including zero-count variants
+          pattern: "library_completed_variantCounts.csv"
+      - variantCounts_for_heatmaps.csv:
+          type: file
+          description: Processed counts formatted specifically for heatmap visualization
+          pattern: "variantCounts_for_heatmaps.csv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/dmsanalysis/process_variant_counts/templates/process_variant_counts.R b/modules/local/dmsanalysis/process_variant_counts/templates/process_variant_counts.R
new file mode 100644
index 0000000..e1871b3
--- /dev/null
+++ b/modules/local/dmsanalysis/process_variant_counts/templates/process_variant_counts.R
@@ -0,0 +1,364 @@
+#!/usr/bin/env Rscript
+
+# four parts condensed into one R script
+
+#########
+# process raw counts
+#########
+
+# input: counts variantcounts tsv file, output_path
+# output: csv with column names. Creates additional counts_per_cov column. Fills pos_mut column for synonymous mutations. Sorted out variants that have mutations, but do not show up in the specifying columns -> was affecting roughly 30 low-count variants out of over 15000 in Taylor's data
+
+library("dplyr")
+process_raw_counts <- function(counts_file_path, output_csv_path) {
+
+  # Set the column names
+  colnames <- c("counts", "cov", "mean_length_variant_reads", "varying_bases",
+                          "base_mut", "varying_codons", "codon_mut", "aa_mut", "pos_mut")
+
+  # Read the variant-count file into a data frame
+  counts_raw <- read.table(counts_file_path, sep = "\\t", header = FALSE, fill = TRUE, col.names = colnames)
+
+  # Filter out rows where 'aa_mut' is empty or NA
+  counts_raw <- counts_raw[!(counts_raw\$aa_mut == "" | is.na(counts_raw\$aa_mut)), ]
+
+  # Handle synonymous mutations: where aa_mut starts with "S" and pos_mut is either NA or ""
+  counts_raw <- counts_raw %>%
+    rowwise() %>%
+    mutate(
+      pos_mut = ifelse(
+        (is.na(pos_mut) | pos_mut == "") & grepl("^S:", aa_mut),
+        # Construct the new 'pos_mut' entry for synonymous mutations
+        paste0(
+          sub("S:([A-Z])>[A-Z]", "\\\\1", aa_mut),  # Get the original amino acid from 'aa_mut'
+          sub("^(\\\\d+):.*", "\\\\1", codon_mut),    # Get the position from 'codon_mut'
+          sub("S:[A-Z]>([A-Z])", "\\\\1", aa_mut)   # Get the mutated amino acid from 'aa_mut'
+        ),
+        pos_mut  # Keep the existing 'pos_mut' if it's not NA or ""
+      )
+    ) %>%
+    ungroup() %>%
+    mutate(counts_per_cov = counts / cov)
+
+  # Write the cleaned data frame to a CSV file
+  write.csv(counts_raw, file = output_csv_path, row.names = FALSE)
+}
+
+#####
+# run function
+#####
+process_raw_counts(
+  counts_file_path = "$variantCounts",
+  output_csv_path = "annotated_variantCounts.csv"
+)
+
+
+
+
+
+
+
+
+#########
+# filter counts by codon library
+#########
+
+# Input: pre_processed_raw_counts_path, mutation library .csv path (former "possible_NNK_mutations.csv"), output_path
+# Output: counts table filtered for only single-codon mutations that are part of the library
+
+library("dplyr")
+library("stringr")
+
+filter_counts_by_codon_library <- function(counts_file_path, codon_library_path, output_file_path) {
+  # Load the variant-count table from the provided file path
+  counts_table <- read.csv(counts_file_path)
+
+  # Load the codon library from the provided .csv file
+  codon_library <- read.csv(codon_library_path)
+
+  # Ensure the codon library has the expected columns
+  if (!all(c("Codon_Number", "wt_codon", "Variant") %in% colnames(codon_library))) {
+    stop("Codon library must contain columns 'Codon_Number', 'wt_codon', and 'Variant'.")
+  }
+
+  # Filter the variant-count table
+  filtered_counts <- counts_table %>%
+    filter(varying_codons == 1) %>% # Keep rows with single-codon mutations
+    rowwise() %>%
+    filter({
+      # Extract the position and mutated codon
+      codon_position <- as.numeric(sub(":.*", "", codon_mut)) # Extract position before ':'
+      mutated_codon <- sub(".*>", "", codon_mut)             # Extract codon after '>'
+
+      # Check if the position and codon are valid
+      is_in_library <- any(
+        codon_library\$Codon_Number == codon_position &
+          (codon_library\$Variant == mutated_codon |  # Check Variant column
+             codon_library\$wt_codon == mutated_codon)  # Check wt_codon column
+      )
+      is_in_library
+    }) %>%
+    ungroup() %>%
+    # Apply additional filtering based on mutation distances
+    rowwise() %>%
+    filter({
+      # Split base_mut into individual mutations
+      mutations <- unlist(strsplit(base_mut, ",\\\\s*"))  # Splits by comma and removes extra spaces
+      # Extract numeric positions from each mutation string
+      positions <- as.numeric(str_extract(mutations, "^[0-9]+"))
+
+      # Calculate the distance between the first and last position
+      distance <- max(positions, na.rm = TRUE) - min(positions, na.rm = TRUE)
+
+      # Keep rows where the distance is <= 2
+      distance <= 2
+    }) %>%
+    ungroup()
+
+  # Write the filtered variant-count table to the output file path
+  write.csv(filtered_counts, file = output_file_path, row.names = FALSE)
+}
+
+#####
+# run function
+#####
+filter_counts_by_codon_library(
+  counts_file_path = "annotated_variantCounts.csv",
+  codon_library_path = "$possible_mutations",
+  output_file_path = "variantCounts_filtered_by_library.csv"
+)
+
+
+
+
+#########
+# complete filtered counts
+#########
+
+# input: NNK_codon_library_filtered_counts.csv-path, prefiltered_counts.csv-path (containing only NNK mutations), output_folder-path
+# output: completed counts_file with all possible variants (even if not measured in sequencing) -> NA in counts and counts_per_cov to 0.0000001 to deal with log-scale in following calculations
+
+library(dplyr)
+library(Biostrings)  # Required for codon-to-amino-acid translation
+
+# Function to calculate Hamming distance (varying_bases)
+hamming_distance <- function(wt_codon, variant_codon) {
+  sum(strsplit(wt_codon, "")[[1]] != strsplit(variant_codon, "")[[1]])
+}
+
+# Function to get amino acid from codon
+get_amino_acid <- function(codon) {
+  codon_table <- GENETIC_CODE
+  aa <- codon_table[[toupper(codon)]]
+  if (is.null(aa)) {
+    return(NA)  # Handle cases where codon is not valid
+  }
+  return(aa)
+}
+
+# Function to calculate mutation type (aa_mut) and pos_mut
+mutation_details <- function(wt_codon, variant_codon, codon_number) {
+  wt_aa <- get_amino_acid(wt_codon)
+  variant_aa <- get_amino_acid(variant_codon)
+
+  # If amino acids are different, it's a missense mutation; otherwise, synonymous
+  if (wt_aa != variant_aa) {
+    mutation_type <- "M"  # Missense mutation
+  } else {
+    mutation_type <- "S"  # Synonymous mutation
+  }
+
+  # aa_mut: Type of mutation and amino acid changes (e.g., M:D>S)
+  aa_mut <- paste0(mutation_type, ":", wt_aa, ">", variant_aa)
+
+  # pos_mut: Wild-type AA, codon position, mutated AA (e.g., D2Q)
+  pos_mut <- paste0(wt_aa, codon_number, variant_aa)
+
+  return(list(aa_mut = aa_mut, pos_mut = pos_mut))
+}
+
+complete_prefiltered_counts <- function(possible_nnk_path, prefiltered_counts_path, output_file_path) {
+
+  # Load the possible NNK mutations CSV
+  possible_nnk <- read.csv(possible_nnk_path)
+
+  # Load the prefiltered variant-count CSV
+  prefiltered_counts <- read.csv(prefiltered_counts_path)
+
+  # Create codon_mut column in possible_NNK_mutations in the format 'Codon_Number:wt_codon>Variant'
+  possible_nnk <- possible_nnk %>%
+    mutate(codon_mut = paste0(Codon_Number, ":", wt_codon, ">", Variant))
+
+  # Merge both dataframes based on the codon_mut column (full join to include all)
+  merged_data <- full_join(prefiltered_counts, possible_nnk, by = "codon_mut")
+
+  # Fill missing values in counts_per_cov and counts with 0.0000001
+  merged_data <- merged_data %>%
+    mutate(counts_per_cov = ifelse(is.na(counts_per_cov), 0.0000001, counts_per_cov),
+           counts = ifelse(is.na(counts), 0.000001, counts))
+
+  # Calculate Hamming distance (varying_bases) and mutation details (aa_mut, pos_mut)
+  merged_data <- merged_data %>%
+    rowwise() %>%
+    mutate(varying_bases = hamming_distance(wt_codon, Variant),
+           mutation_info = list(mutation_details(wt_codon, Variant, Codon_Number))) %>%
+    mutate(aa_mut = mutation_info\$aa_mut,  # Extract aa_mut
+           pos_mut = mutation_info\$pos_mut) %>%  # Extract pos_mut
+    ungroup() %>%
+    select(-mutation_info)  # Remove the temporary list column
+
+  # Save the merged data to a new CSV file
+  write.csv(merged_data, file = output_file_path, row.names = FALSE)
+}
+
+#####
+# run function
+#####
+complete_prefiltered_counts(
+  possible_nnk_path = "$possible_mutations",
+  prefiltered_counts_path = "variantCounts_filtered_by_library.csv",
+  output_file_path = "library_completed_variantCounts.csv"
+)
+
+
+
+
+
+
+
+
+
+#########
+# prepare counts data for counts heatmap
+#########
+
+# input: prefiltered variant-count path (filtered for codon library), aa-seq file path, output path, threshold (for minimum counts to recognize variant)
+# output: csv file serving as basis for counts_per_cov_heatmap function
+
+suppressMessages(library(dplyr))
+suppressMessages(library(ggplot2))
+suppressMessages(library(tidyr))
+suppressMessages(library(reshape2))
+suppressMessages(library(scales))
+
+prepare_counts_data_for_counts_heatmaps <- function(counts_file_path, aa_seq_file_path, output_csv_path, threshold = 3) {
+  # Load the raw variant-count data
+  raw_counts <- read.table(counts_file_path, sep = ",", header = TRUE)
+
+  # Read the wild-type amino acid sequence from the text file
+  wt_seq <- readLines(aa_seq_file_path)
+  wt_seq <- unlist(strsplit(wt_seq, ""))  # Split the sequence into individual amino acids
+
+  # Summarize counts-per-cov for each unique aa mutation in pos_mut
+  aggregated_data <- raw_counts %>%
+    group_by(pos_mut) %>%
+    summarize(total_counts_per_cov = sum(counts_per_cov, na.rm = TRUE),
+              total_counts = sum(counts, na.rm = TRUE))  # Also sum the counts
+
+  # Extract the wild-type position and mutations from 'pos_mut'
+  aggregated_data <- aggregated_data %>%
+    mutate(
+      wt_aa = sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\1", pos_mut),  # Wild-type amino acid (e.g., S)
+      position = as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", pos_mut)),  # Position (e.g., 3)
+      mut_aa = sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\3", pos_mut)   # Mutant amino acid (e.g., R)
+    )
+
+  # Replace 'X' with '*', indicating the stop codon
+  aggregated_data <- aggregated_data %>%
+    mutate(mut_aa = ifelse(mut_aa == "X", "*", mut_aa))
+
+  # Replace 'X' with '*' in the wild-type amino acid sequence as well
+  wt_seq <- ifelse(wt_seq == "X", "*", wt_seq)
+
+  # Define all 20 standard amino acids and the stop codon "*"
+  all_amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L",
+                       "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y", "*")
+
+  # Create a list of all positions in the wild-type sequence
+  all_positions <- 1:length(wt_seq)
+
+  # Create a complete grid of all possible combinations of positions and amino acids
+  complete_data <- expand.grid(mut_aa = all_amino_acids, position = all_positions)
+
+  # Merge the summarized data with the complete grid (filling missing entries with 0)
+  heatmap_data <- complete_data %>%
+    left_join(aggregated_data, by = c("mut_aa", "position")) %>%
+    mutate(total_counts_per_cov = ifelse(is.na(total_counts_per_cov), 0, total_counts_per_cov),
+           wt_aa = wt_seq[position])  # Assign the wild-type amino acid
+
+  # Set variants with counts < threshold to NA
+  heatmap_data <- heatmap_data %>%
+    mutate(
+      total_counts_per_cov = ifelse(total_counts < threshold, NA, total_counts_per_cov),
+      total_counts = ifelse(total_counts < threshold, NA, total_counts)
+    )
+
+  # Fill pos_mut column
+  heatmap_data <- heatmap_data %>%
+    mutate(
+      pos_mut = ifelse(is.na(pos_mut),
+                       paste0(wt_aa, position, mut_aa),
+                       pos_mut)
+    )
+
+  # Save the aggregated data to a CSV file
+  write.csv(heatmap_data, file = output_csv_path, row.names = FALSE)
+  print(paste("Aggregated data saved to:", output_csv_path))
+}
+
+
+#####
+# run function
+#####
+prepare_counts_data_for_counts_heatmaps(
+  counts_file_path = "variantCounts_filtered_by_library.csv",
+  aa_seq_file_path = "$aa_seq",
+  output_csv_path = "variantCounts_for_heatmaps.csv",
+  threshold = $min_counts
+)
+
+
+
+
+
+
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+stringr_version <- as.character(packageVersion("stringr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+tidyr_version <- as.character(packageVersion("tidyr"))
+reshape2_version <- as.character(packageVersion("reshape2"))
+scales_version <- as.character(packageVersion("scales"))
+
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+if (length(stringr_version) == 0) stringr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(tidyr_version) == 0) tidyr_version <- "unknown"
+if (length(reshape2_version) == 0) reshape2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-Biostrings:', Biostrings_version),
+    paste('    r-stringr:', stringr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-tidyr:', tidyr_version),
+    paste('    r-reshape2:', reshape2_version),
+    paste('    r-scales:', scales_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/counts_to_fitness/environment.yml b/modules/local/fitness/counts_to_fitness/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/counts_to_fitness/main.nf b/modules/local/fitness/counts_to_fitness/main.nf
new file mode 100644
index 0000000..1423e99
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/main.nf
@@ -0,0 +1,32 @@
+process COUNTS_TO_FITNESS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path wt_seq
+    val pos_range
+
+    output:
+    tuple val(meta), path("${meta.id}_fitness_input.tsv"), emit: fitness_input
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'counts_to_fitness.R'
+
+    stub:
+    """
+    touch ${meta.id}_fitness_input.tsv
+    echo "COUNTS_TO_FITNESS:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/fitness/counts_to_fitness/meta.yml b/modules/local/fitness/counts_to_fitness/meta.yml
new file mode 100644
index 0000000..e0aae49
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/meta.yml
@@ -0,0 +1,57 @@
+name: "counts_to_fitness"
+description: Reformats the library-filtered variant count table into a standardized TSV compatible with downstream fitness calculation tools, ensuring mutation nomenclature is consistent with the reference sequence.
+keywords:
+  - deep mutational scanning
+  - dms
+  - variant counting
+  - format conversion
+  - fitness
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+  - "bioconductor-biostrings":
+      description: "Efficient manipulation of biological strings"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts already filtered against the design library
+        pattern: "*.{csv}"
+  - - wt_seq:
+        type: file
+        description: FASTA file containing the wild-type DNA reference sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions (ORF) in the format 'start-stop'
+
+output:
+  - fitness_input:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "${meta.id}_fitness_input.tsv":
+          type: file
+          description: A standardized TSV file ready for input into fitness calculation modules
+          pattern: "*_fitness_input.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/counts_to_fitness/templates/counts_to_fitness.R b/modules/local/fitness/counts_to_fitness/templates/counts_to_fitness.R
new file mode 100644
index 0000000..b234058
--- /dev/null
+++ b/modules/local/fitness/counts_to_fitness/templates/counts_to_fitness.R
@@ -0,0 +1,103 @@
+#!/usr/bin/env Rscript
+
+suppressMessages(library(Biostrings))
+
+generate_fitness_input <- function(wt_seq_path, counts_file, pos_range, output_file_path) {
+  # Parse the position range
+  positions <- unlist(strsplit(pos_range, "-"))
+  start_pos <- as.numeric(positions[1])
+  stop_pos <- as.numeric(positions[2])
+
+  # Load the wild-type sequence
+  seq_data <- Biostrings::readDNAStringSet(filepath = wt_seq_path)
+  wt_seq <- seq_data[[1]]  # Extract the sequence
+  wt_seq <- subseq(wt_seq, start = start_pos, end = stop_pos)
+
+  # Convert wt_seq to a character string
+  wt_seq <- as.character(wt_seq)
+
+  # Split the wild-type sequence into codons (groups of 3 bases)
+  wt_codons <- substring(wt_seq, seq(1, nchar(wt_seq), 3), seq(3, nchar(wt_seq), 3))
+
+  # Helper function to process variant-count CSVs into count data
+  process_counts_file <- function(counts_csv) {
+    # Load the input variant-count CSV file
+    counts_data <- read.csv(counts_csv, stringsAsFactors = FALSE)
+
+    # Initialize a data frame for results
+    results <- data.frame(
+      nt_seq = character(),
+      count = numeric(),
+      stringsAsFactors = FALSE
+    )
+
+    # Iterate over each row in the input data
+    for (i in 1:nrow(counts_data)) {
+      # Extract the mutation info
+      codon_mut <- counts_data\$codon_mut[i]
+
+      if("counts_corrected" %in% colnames(counts_data)){
+        counts <- counts_data\$counts_corrected[i]
+      }else{
+        counts <- counts_data\$counts[i]
+      }
+
+      # Create a mutable copy of the wild-type codons
+      mutated_codons <- wt_codons
+
+      # Apply the mutation
+      mutations <- strsplit(codon_mut, ", ")[[1]]
+      for (mutation in mutations) {
+        codon_position <- as.numeric(sub(":.*", "", mutation))
+        new_codon <- sub(".*>", "", mutation)
+        # Replace the codon at the specified position
+        mutated_codons[codon_position] <- new_codon
+      }
+
+      # Convert the mutated codons back to a sequence string
+      mutated_seq_string <- paste(mutated_codons, collapse = "")
+
+      # Add the result to the data frame
+      results <- rbind(results, data.frame(nt_seq = mutated_seq_string, count = counts))
+    }
+
+    return(results)
+  }
+
+  # Process the variant-count file
+  cat("Processing variant-count file...\\n")
+  processed_data <- process_counts_file(counts_file)
+
+  # Write the processed data to a file without column names
+  write.table(processed_data, file = output_file_path, sep = "\\t", row.names = FALSE, col.names = FALSE, quote = FALSE)
+}
+
+#####
+# run function
+#####
+generate_fitness_input(
+  wt_seq_path = "$wt_seq",
+  counts_file = "$variantCounts_filtered_by_library",
+  pos_range = "$pos_range",
+  output_file_path = "${meta.id}_fitness_input.tsv"
+)
+
+#####
+# create versions.yml
+#####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/find_synonymous_mutation/environment.yml b/modules/local/fitness/find_synonymous_mutation/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/find_synonymous_mutation/main.nf b/modules/local/fitness/find_synonymous_mutation/main.nf
new file mode 100644
index 0000000..2ea78e6
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/main.nf
@@ -0,0 +1,31 @@
+process FIND_SYNONYMOUS_MUTATION {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    tuple val(sample), path(counts_merged)   // from MERGE_COUNTS.out.merged_counts
+    path wt_fasta                            // broadcast singleton
+    val pos_range                            // "start-end", broadcast singleton
+
+  output:
+    tuple val(sample), path("synonymous_wt.txt"), emit: synonymous_wt
+    path "versions.yml", emit: versions
+
+  script:
+  template 'find_syn_mutation.R'
+
+  stub:
+  """
+  touch synonymous_wt.txt
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/find_synonymous_mutation/meta.yml b/modules/local/fitness/find_synonymous_mutation/meta.yml
new file mode 100644
index 0000000..8ec3ab3
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/meta.yml
@@ -0,0 +1,58 @@
+name: "find_synonymous_mutation"
+description: Identifies synonymous mutations within the merged count table, prioritizing those with high counts (typically 2nt changes) to serve as a reference for normalizing fitness scores.
+keywords:
+  - deep mutational scanning
+  - dms
+  - synonymous mutation
+  - fitness calculation
+  - normalization
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+  - "bioconductor-biostrings":
+      description: "Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - counts_merged:
+        type: file
+        description: TSV/CSV file containing variant counts merged across samples or replicates
+        pattern: "*.{tsv,csv}"
+  - - wt_fasta:
+        type: file
+        description: FASTA file containing the wild-type reference DNA sequence
+        pattern: "*.{fasta,fa}"
+  - - pos_range:
+        type: string
+        description: Start and stop codon positions (ORF) in the format 'start-stop', e.g., '352-1383'
+
+output:
+  - synonymous_wt:
+      - sample:
+          type: map
+          description: |
+            Groovy Map containing sample information
+      - synonymous_wt.txt:
+          type: file
+          description: Text file identifying the chosen synonymous mutation to be used as a wild-type reference
+          pattern: "synonymous_wt.txt"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/find_synonymous_mutation/templates/find_syn_mutation.R b/modules/local/fitness/find_synonymous_mutation/templates/find_syn_mutation.R
new file mode 100644
index 0000000..a0ee07a
--- /dev/null
+++ b/modules/local/fitness/find_synonymous_mutation/templates/find_syn_mutation.R
@@ -0,0 +1,119 @@
+#!/usr/bin/env Rscript
+
+suppressMessages(library(Biostrings))
+
+# Pick a synonymous "WT substitute" for DiMSum normalization using a fixed coding window.
+# Inputs:
+#   wt_fasta          : path to FASTA (single WT sequence)
+#   counts_merged_tsv : path to merged counts (columns: nt_seq, input1..N, output1..M)
+#   pos_range         : "start-end" (1-based, inclusive), e.g. "352-1383"
+# Returns:
+#   character scalar: chosen nt sequence
+#
+# Preference:
+#   1) AA-identical (fully synonymous) AND exactly 2 nt mismatches vs WT, both within ONE codon.
+#   2) If none, AA-identical AND exactly 1 nt mismatch vs WT.
+#   3) If more than one, pick highest mean of input counts.
+#   4) If still none, stop with an error.
+
+pick_synonymous_wt_from_range <- function(wt_fasta, counts_merged_tsv, pos_range) {
+  ## ---- parse range ----
+  pr <- strsplit(as.character(pos_range), "-", fixed = TRUE)[[1]]
+  if (length(pr) != 2L) stop("pos_range must be 'start-end', got: ", pos_range)
+  start_pos <- as.integer(pr[1]); end_pos <- as.integer(pr[2])
+  if (is.na(start_pos) || is.na(end_pos) || start_pos < 1L || end_pos < start_pos)
+    stop("Invalid pos_range: ", pos_range)
+
+  ## ---- WT window ----
+  wt_set <- Biostrings::readDNAStringSet(wt_fasta)
+  if (length(wt_set) != 1L) stop("WT FASTA must contain exactly one sequence.")
+  wt_subseq <- Biostrings::subseq(wt_set[[1]], start = start_pos, end = end_pos)
+  wt_seq_chr <- as.character(wt_subseq)
+  wt_len <- nchar(wt_seq_chr)
+  if ((wt_len %% 3) != 0) stop("Provided window length is not divisible by 3: ", wt_len)
+  wt_aa  <- Biostrings::translate(wt_subseq, if.fuzzy.codon = "X")
+  wt_chars <- strsplit(wt_seq_chr, "", fixed = TRUE)[[1]]
+
+  ## ---- counts ----
+  df <- utils::read.delim(counts_merged_tsv, sep = "\\t", header = TRUE,
+                          stringsAsFactors = FALSE, check.names = FALSE)
+  if (!"nt_seq" %in% names(df)) stop("counts_merged_tsv must have a 'nt_seq' column.")
+
+  df\$nt_seq <- toupper(df\$nt_seq)
+  keep_len <- nchar(df\$nt_seq) == wt_len
+  if (!any(keep_len)) stop("No sequences match WT window length (", wt_len, ").")
+  if (!all(keep_len)) df <- df[keep_len, , drop = FALSE]
+
+  # input columns & mean (works with 1+ replicates)
+  input_cols <- grep("^input", names(df), value = TRUE)
+  if (length(input_cols) == 0L) stop("No input columns found (expect names starting with 'input').")
+  input_mat <- as.data.frame(lapply(df[, input_cols, drop = FALSE], function(x) as.numeric(as.character(x))))
+  input_mean <- if (length(input_cols) == 1L) input_mat[[1]] else rowMeans(as.matrix(input_mat), na.rm = TRUE)
+
+  ## ---- synonymous filter ----
+  var_set <- Biostrings::DNAStringSet(df\$nt_seq)
+  var_aa  <- Biostrings::translate(var_set, if.fuzzy.codon = "X")
+  syn_idx <- which(as.character(var_aa) == as.character(wt_aa))
+  if (length(syn_idx) == 0L) stop("No fully-synonymous variants found relative to WT translation.")
+
+  # helpers
+  mismatch_positions <- function(seq_nt_chars) which(seq_nt_chars != wt_chars) # 1-based positions
+  codon_index <- function(pos_vec) floor((pos_vec - 1L) / 3L)                  # 0-based codon bin
+
+  # preference 1: exactly 2 mismatches, both within the same codon
+  cand_two_one <- Filter(function(i) {
+    vchars <- strsplit(df\$nt_seq[i], "", fixed = TRUE)[[1]]
+    pos <- mismatch_positions(vchars)
+    length(pos) == 2L && length(unique(codon_index(pos))) == 1L
+  }, syn_idx)
+
+  choose_best <- function(idx_vec) idx_vec[ which.max(input_mean[idx_vec]) ]
+
+  if (length(cand_two_one) > 0L) {
+    best_i <- choose_best(cand_two_one)
+    return(as.character(df\$nt_seq[best_i]))
+  }
+
+  # preference 2 (fallback): exactly 1 mismatch (still synonymous)
+  cand_one <- Filter(function(i) {
+    vchars <- strsplit(df\$nt_seq[i], "", fixed = TRUE)[[1]]
+    length(mismatch_positions(vchars)) == 1L
+  }, syn_idx)
+
+  if (length(cand_one) > 0L) {
+    best_i <- choose_best(cand_one)
+    return(as.character(df\$nt_seq[best_i]))
+  }
+
+  stop("No suitable synonymous variant found: neither 2-in-1-codon nor 1-nt synonymous candidates present.")
+}
+
+#####
+# run function
+#####
+seq <- pick_synonymous_wt_from_range(
+	wt_fasta = "$wt_fasta",
+	counts_merged_tsv = "$counts_merged",
+	pos_range = "$pos_range"
+	)
+write(seq, file='synonymous_wt.txt')
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_QC/environment.yml b/modules/local/fitness/fitness_QC/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_QC/main.nf b/modules/local/fitness/fitness_QC/main.nf
new file mode 100644
index 0000000..78420eb
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/main.nf
@@ -0,0 +1,31 @@
+process FITNESS_QC {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    tuple val(sample), path(fitness_estimation_tsv)   // from FITNESS_CALCULATION
+
+  output:
+    tuple val(sample), path("fitness_estimation_count_correlation.pdf"), emit: counts_corr_pdf
+    tuple val(sample), path("fitness_estimation_fitness_correlation.pdf"), emit: fitness_corr_pdf
+    path "versions.yml", emit: versions
+
+  script:
+  template 'fitness_QC.R'
+
+  stub:
+  """
+    touch fitness_estimation_count_correlation.pdf
+    touch fitness_estimation_fitness_correlation.pdf
+    cat > versions.yml <<'EOF'
+    FITNESS_PLOTS:
+      stub-version: "0.0.0"
+    EOF
+  """
+}
diff --git a/modules/local/fitness/fitness_QC/meta.yml b/modules/local/fitness/fitness_QC/meta.yml
new file mode 100644
index 0000000..309e225
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/meta.yml
@@ -0,0 +1,54 @@
+name: "fitness_qc"
+description: Generates quality control visualizations for fitness estimation results, including correlation plots of raw counts and calculated fitness scores to assess experimental reproducibility.
+keywords:
+  - deep mutational scanning
+  - dms
+  - quality control
+  - correlation
+  - fitness
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - fitness_estimation_tsv:
+        type: file
+        description: TSV file containing calculated fitness estimates and associated statistics
+        pattern: "*.{tsv}"
+
+output:
+  - counts_corr_pdf:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_count_correlation.pdf:
+          type: file
+          description: PDF plot showing the correlation of variant counts between samples/replicates
+          pattern: "fitness_estimation_count_correlation.pdf"
+  - fitness_corr_pdf:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_fitness_correlation.pdf:
+          type: file
+          description: PDF plot showing the correlation of fitness scores between samples/replicates
+          pattern: "fitness_estimation_fitness_correlation.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/fitness_QC/templates/fitness_QC.R b/modules/local/fitness/fitness_QC/templates/fitness_QC.R
new file mode 100644
index 0000000..6df7dfe
--- /dev/null
+++ b/modules/local/fitness/fitness_QC/templates/fitness_QC.R
@@ -0,0 +1,128 @@
+#!/usr/bin/env Rscript
+
+## fitness QC plots for nf-core/deepmutscan
+## 28.10.2025
+## maximilian.stammnitz@crg.eu
+
+## lower panels: scatter + x=y (log-log version for counts)
+panel_xy_abline_counts <- function(x, y, ...) {
+  op <- par("xpd"); on.exit(par(xpd = op), add = TRUE)
+  par(xpd = FALSE)
+  points(x, y, pch = 16, cex = 0.1, ...)
+  abline(a = 0, b = 1, lty = 2, col = "grey50")
+}
+
+## upper panels: Pearson r (log-log-transformed)
+panel_cor_counts <- function(x, y, digits = 2, prefix = "r = ", cex.text = 1.4, ...) {
+  r <- suppressWarnings(cor(log(x), log(y), use = "pairwise.complete.obs", method = "pearson"))
+  lab <- if (is.finite(r)) bquote(italic(r) == .(round(r, digits))) else bquote(italic(r) == NA)
+
+  ## Save/restore full graphics state we touch
+  op <- par(c("usr", "xpd", "xlog", "ylog"))
+  on.exit(par(op), add = TRUE)
+
+  ## Draw in normalized 0..1 panel coords with logs OFF so text is visible
+  par(xlog = FALSE, ylog = FALSE, xpd = FALSE, usr = c(0, 1, 0, 1))
+  text(0.5, 0.5, labels = lab, cex = cex.text, font = 1, col = "black")
+}
+
+## lower panels: scatter + x=y (linear version for fitness)
+panel_xy_abline_fitness <- function(x, y, ...) {
+  op <- par("xpd"); on.exit(par(xpd = op), add = TRUE)
+  par(xpd = FALSE)
+  points(x, y, pch = 16, cex = 0.5, ...)
+  abline(a = 0, b = 1, lty = 2, col = "grey50")
+}
+
+## upper panels: Pearson r (linear)
+panel_cor_fitness <- function(x, y, digits = 2, prefix = "r = ", cex.text = 1.4, ...) {
+  r <- suppressWarnings(cor(x, y, use = "pairwise.complete.obs", method = "pearson"))
+  lab <- if (is.finite(r)) bquote(italic(r) == .(round(r, digits))) else bquote(italic(r) == NA)
+
+  ## Save/restore full graphics state we touch
+  op <- par(c("usr", "xpd", "xlog", "ylog"))
+  on.exit(par(op), add = TRUE)
+
+  ## Draw in normalized 0..1 panel coords with logs OFF so text is visible
+  par(xlog = FALSE, ylog = FALSE, xpd = FALSE, usr = c(0, 1, 0, 1))
+  text(0.5, 0.5, labels = lab, cex = cex.text, font = 1, col = "black")
+}
+
+#' Plot input/output count correlations and fitness replicate correlations
+#'
+#' @param fitness_table_path Path to the input table (fitness_estimation.tsv)
+#' @param out_counts_corr_pdf Path to write the counts correlation PDF
+#' @param out_fitness_corr_pdf Path to write the fitness correlation PDF
+#'
+#' @return Invisibly returns TRUE; writes the two PDFs.
+run_fitness_plots <- function(fitness_table_path,
+                              out_counts_corr_pdf,
+                              out_fitness_corr_pdf) {
+
+  merged.counts.fitness <- read.table(fitness_table_path, sep = "\\t", header = TRUE, check.names = FALSE)
+
+  ## identify the right samples
+  inputs  <- grep("input",  colnames(merged.counts.fitness))
+  outputs <- grep("output", colnames(merged.counts.fitness))
+
+  ## 5. Plot input vs. output counts ##
+  #####################################
+  pdf(out_counts_corr_pdf, height = 9, width = 14)
+  pairs(merged.counts.fitness[, c(inputs, outputs)] + 1,  ## use a pseudo-count
+        lower.panel = panel_xy_abline_counts,
+        upper.panel = panel_cor_counts,
+        cex.text = 2,
+        log = "xy")
+  dev.off()
+
+  ## 6. Plot fitness correlations ##
+  ##################################
+  fitness.repl <- grep("rescaled_fitness", colnames(merged.counts.fitness))
+
+  if (length(fitness.repl) > 1) {
+    pdf(out_fitness_corr_pdf, height = 9, width = 14)
+    pairs(merged.counts.fitness[, fitness.repl],
+          lower.panel = panel_xy_abline_fitness,
+          upper.panel = panel_cor_fitness,
+          cex.text = 2,
+          xlim = c(-3, 1),
+          ylim = c(-3, 1))
+    dev.off()
+  } else {
+    ## If only one (or zero) rescaled_fitness columns exist, still create an empty placeholder
+    ## so Nextflow finds the declared output.
+    pdf(out_fitness_corr_pdf, height = 9, width = 14)
+    plot.new()
+    title("No replicate fitness columns found (need ≥2 'rescaled_fitness...')\\nCreated placeholder PDF.")
+    dev.off()
+  }
+
+  invisible(TRUE)
+}
+
+
+#####
+# run function
+#####
+run_fitness_plots(
+  fitness_table_path = "$fitness_estimation_tsv",
+  out_counts_corr_pdf = "fitness_estimation_count_correlation.pdf",
+  out_fitness_corr_pdf = "fitness_estimation_fitness_correlation.pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+
+if (is.null(r_version)) r_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_calculation/environment.yml b/modules/local/fitness/fitness_calculation/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_calculation/main.nf b/modules/local/fitness/fitness_calculation/main.nf
new file mode 100644
index 0000000..5bf6208
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/main.nf
@@ -0,0 +1,29 @@
+process FITNESS_CALCULATION {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    tuple val(sample), path(counts_merged)
+    path(exp_design)
+    path(syn_wt_txt)
+
+  output:
+    tuple val(sample), path("fitness_estimation.tsv"), emit: fitness_estimation
+    path "versions.yml", emit: versions
+
+  script:
+  template 'fitness_calculation.R'
+
+  stub:
+  """
+    touch fitness_estimation.tsv
+    echo "FITNESS_CALCULATION:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+  """
+}
diff --git a/modules/local/fitness/fitness_calculation/meta.yml b/modules/local/fitness/fitness_calculation/meta.yml
new file mode 100644
index 0000000..6be779d
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/meta.yml
@@ -0,0 +1,54 @@
+name: "fitness_calculation"
+description: Calculates fitness scores for Deep Mutational Scanning (DMS) variants. It typically uses a log-enrichment ratio approach, comparing variant frequencies in selection (output) versus baseline (input) samples, normalized to a synonymous wild-type reference.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness calculation
+  - log-ratio
+  - enrichment
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - counts_merged:
+        type: file
+        description: TSV or CSV file containing the consolidated variant counts across all replicates
+        pattern: "*.{tsv,csv}"
+  - - exp_design:
+        type: file
+        description: CSV or text file defining the experimental design, including input/output mappings and replicates
+        pattern: "*.{csv,txt}"
+  - - syn_wt_txt:
+        type: file
+        description: Text file identifying the synonymous wild-type variant used as the normalization baseline
+        pattern: "*.txt"
+
+output:
+  - fitness_estimation:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation.tsv:
+          type: file
+          description: TSV file containing the final calculated fitness scores for all variants
+          pattern: "fitness_estimation.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/fitness_calculation/templates/fitness_calculation.R b/modules/local/fitness/fitness_calculation/templates/fitness_calculation.R
new file mode 100644
index 0000000..4b0966b
--- /dev/null
+++ b/modules/local/fitness/fitness_calculation/templates/fitness_calculation.R
@@ -0,0 +1,358 @@
+#!/usr/bin/env Rscript
+
+## default fitness estimation for nf-core/deepmutscan
+## 18.02.2026
+## maximilian.stammnitz@crg.eu
+
+## 0. Libraries ##
+##################
+
+suppressPackageStartupMessages({
+  library(Biostrings)
+})
+
+## --- Helper functions ---
+
+# calculate nt hamming distances from the specified WT
+compute_nt_hamming <- function(merged.counts, wt.seq) {
+  merged.counts <- cbind("nt_ham" = rep(NA, nrow(merged.counts)), merged.counts)
+  for (i in 1:nrow(merged.counts)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(merged.counts\$nt_seq[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      merged.counts\$nt_ham[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      merged.counts\$nt_ham[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  merged.counts
+}
+
+# translate sequences and add aa_seq
+add_aa_seq <- function(merged.counts) {
+  merged.counts <- cbind("aa_seq" = as.character(translate(DNAStringSet(merged.counts\$nt_seq))), merged.counts)
+  merged.counts
+}
+
+# calculate AA hamming distances from the WT
+compute_aa_hamming <- function(merged.counts, wt.seq.aa) {
+  merged.counts <- cbind("aa_ham" = rep(NA, nrow(merged.counts)), merged.counts)
+  for (i in 1:nrow(merged.counts)){
+    tmp.wt <- strsplit(as.character(wt.seq.aa), "")[[1]]
+    tmp.mut <- strsplit(as.character(merged.counts\$aa_seq[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      merged.counts\$aa_ham[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      merged.counts\$aa_ham[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  merged.counts
+}
+
+# name the mutations
+name_mutations <- function(merged.counts, wt.seq.aa) {
+  merged.counts <- cbind("wt_aa" = rep(NA, nrow(merged.counts)),
+                         "pos" = rep(NA, nrow(merged.counts)),
+                         "mut_aa" = rep(NA, nrow(merged.counts)), merged.counts)
+  for (i in 1:nrow(merged.counts)){
+    if(merged.counts\$aa_ham[i] == 0){
+      next
+    }else{
+      tmp.wt <- strsplit(as.character(wt.seq.aa), "")[[1]]
+      tmp.mut <- strsplit(as.character(merged.counts\$aa_seq[i]), "")[[1]]
+      merged.counts\$pos[i] <- which(tmp.mut != tmp.wt)
+      merged.counts\$'wt_aa'[i] <- tmp.wt[merged.counts\$pos[i]]
+      merged.counts\$'mut_aa'[i] <- tmp.mut[merged.counts\$pos[i]]
+      rm(tmp.mut, tmp.wt)
+    }
+  }
+  merged.counts
+}
+
+# find stops, WT and WT; aggregate counts of variants which are identical on the aa (but not nt) level
+aggregate_by_aa <- function(merged.counts) {
+  ## find stops, WT and WT
+  merged.counts <- cbind(merged.counts,
+                         "wt" = rep(NA, nrow(merged.counts)),
+                         "stop" = rep(NA, nrow(merged.counts)))
+  merged.counts\$wt[which(merged.counts\$nt_ham == 0)] <- TRUE
+  merged.counts\$stop[which(merged.counts\$'mut_aa' == "*")] <- TRUE
+
+  ## aggregate counts of variants which are identical on the aa (but not nt) level
+  ## exception: wildtype ones
+  ## thereby shrinking the matrix
+  uniq.aa.vars <- unique(merged.counts\$aa_seq)
+  uniq.aa.vars <- uniq.aa.vars[-which(uniq.aa.vars == merged.counts\$aa_seq[which(merged.counts\$wt == TRUE)])]
+  for(i in 1:length(uniq.aa.vars)){
+    tmp.aa_seq <- uniq.aa.vars[i]
+    hits <- which(as.character(merged.counts\$aa_seq) == tmp.aa_seq)
+    if(length(hits) == 1){
+      rm(tmp.aa_seq, hits)
+      next
+    }else{
+      for(j in grep("input|output", colnames(merged.counts))){
+        merged.counts[hits[1],j] <- sum(merged.counts[hits,j], na.rm = TRUE)
+      }
+      merged.counts[hits[1], "nt_seq"] <- paste(merged.counts[hits, "nt_seq"], collapse = ", ")
+      merged.counts <- merged.counts[-hits[-1],]
+      rm(tmp.aa_seq, hits)
+      next
+    }
+  }
+  merged.counts
+}
+
+# calculate bimodal fitness distribution
+density_peaks <- function(x, adjust = 1, ...) {
+
+  ## obtain density distribution
+  d <- density(x, adjust = adjust, n = 5000, na.rm = T, ...)
+  y <- d\$y
+  idx <- which(diff(sign(diff(y))) == -2) + 1
+  if (length(idx) == 0) return(numeric(0))
+
+  ## order density peaks by height (y value); take top 2
+  idx <- idx[order(y[idx], decreasing = TRUE)]
+  peaks_x <- d\$x[idx]
+  peaks_x[seq_len(min(2, length(peaks_x)))]
+
+}
+
+# 3. Raw fitness calculations ##
+calc_raw_fitness <- function(merged.counts, exp.design) {
+  ## how many fitness replicates are there
+  reps <- length(unique(exp.design\$experiment_replicate))
+  for (i in 1:reps){
+    merged.counts <- cbind(merged.counts, rep(NA, nrow(merged.counts)))
+    colnames(merged.counts)[ncol(merged.counts)] <- paste0("raw_fitness_rep", i)
+  }
+
+  ## calculate raw fitness of all variants vs. WT variant
+  for (i in 1:reps){
+
+    ### collect counts
+    tmp.input.counts <- merged.counts[,paste0("input", i)]
+    tmp.output.counts <- merged.counts[,paste0("output", i)]
+
+    ### add pseudo-count to zero-outputs (if the corresponding input count is non-zero)
+    tmp.output.counts[which(tmp.output.counts == 0 & tmp.input.counts != 0)] <- 1
+
+    ### take logs
+    tmp.wt.log.ratio <- log(tmp.output.counts[which(merged.counts\$wt == TRUE)] /
+                              tmp.input.counts[which(merged.counts\$wt == TRUE)])
+    tmp.fitness <- log(tmp.output.counts /
+                         tmp.input.counts) - tmp.wt.log.ratio
+
+    ### uncertain values to NA
+    tmp.fitness[which(is.na(tmp.fitness) == TRUE)] <- NA
+    tmp.fitness[which(tmp.fitness == "Inf")] <- NA
+
+    ### add to table
+    merged.counts[,c(ncol(merged.counts) - reps + i)] <- tmp.fitness
+
+    ### clean up
+    rm(tmp.fitness, tmp.wt.log.ratio, tmp.output.counts, tmp.input.counts)
+  }
+
+  list(merged.counts = merged.counts, reps = reps)
+}
+
+# 4. Fitness and error refinements ##
+rescale_and_summarize <- function(merged.counts, reps) {
+  ## center the raw fitness distributions on 0 (median of wildtype synonymous) and -1 (median of stops)
+  for (i in 1:reps){
+
+    merged.counts <- cbind(merged.counts, rep(NA, nrow(merged.counts)))
+    colnames(merged.counts)[ncol(merged.counts)] <- paste0("rescaled_fitness_rep", i)
+
+    ### anchors: median fitness of synonymous (aa_ham == 0) -> 0, of stops -> -1
+    raw_col <- ncol(merged.counts) - reps
+    tmp.raw <- merged.counts[,raw_col]
+    tmp.wt.fitness.med   <- median(tmp.raw[which(merged.counts\$aa_ham == 0)], na.rm = TRUE)
+    tmp.stop.fitness.med <- median(tmp.raw[which(merged.counts\$stop == TRUE)], na.rm = TRUE)
+
+    ### if an anchor is missing, or the two anchors coincide (e.g. on sparse data,
+    ### where lm() would return an NA slope), fall back to the modes of the
+    ### (bimodal) fitness distribution to define the missing/degenerate anchor
+    if(is.na(tmp.wt.fitness.med) | is.na(tmp.stop.fitness.med) |
+       (!is.na(tmp.wt.fitness.med) & !is.na(tmp.stop.fitness.med) & tmp.wt.fitness.med == tmp.stop.fitness.med)){
+      tmp.peaks <- sort(density_peaks(x = tmp.raw))
+      tmp.hi <- if(!is.na(tmp.wt.fitness.med)) tmp.wt.fitness.med else if(length(tmp.peaks) >= 1) tmp.peaks[length(tmp.peaks)] else NA
+      if(!is.na(tmp.stop.fitness.med) & (is.na(tmp.hi) || tmp.stop.fitness.med != tmp.hi)){
+        tmp.lo <- tmp.stop.fitness.med
+      }else{
+        tmp.cand <- tmp.peaks[!is.na(tmp.peaks) & tmp.peaks != tmp.hi]
+        tmp.lo <- if(length(tmp.cand) >= 1) tmp.cand[1] else NA
+      }
+      tmp.wt.fitness.med <- tmp.hi
+      tmp.stop.fitness.med <- tmp.lo
+      rm(tmp.peaks, tmp.hi)
+    }
+
+    ### two distinct anchors -> linear rescale; a single usable anchor -> WT-centre only
+    if(!is.na(tmp.wt.fitness.med) & !is.na(tmp.stop.fitness.med) & tmp.wt.fitness.med != tmp.stop.fitness.med){
+      lm.rescale <- lm(c(0, -1) ~ c(tmp.wt.fitness.med, tmp.stop.fitness.med))
+      merged.counts[,ncol(merged.counts)] <- tmp.raw * lm.rescale\$coefficients[[2]] + lm.rescale\$coefficients[[1]]
+      rm(lm.rescale)
+    }else if(!is.na(tmp.wt.fitness.med)){
+      merged.counts[,ncol(merged.counts)] <- tmp.raw - tmp.wt.fitness.med
+    }
+
+    rm(raw_col, tmp.raw, tmp.wt.fitness.med, tmp.stop.fitness.med)
+  }
+
+  ## calculate fitness mean and standard deviation across replicates
+  merged.counts <- cbind(merged.counts,
+                         "mean fitness" = rep(NA, nrow(merged.counts)),
+                         "fitness sd" = rep(NA, nrow(merged.counts)))
+
+  if(reps == 1){
+
+    merged.counts\$'mean fitness' <- merged.counts[,ncol(merged.counts) - 2]
+
+  }else if(reps > 1){
+
+    merged.counts\$'mean fitness' <- apply(merged.counts[,c(ncol(merged.counts) - 1 - reps):c(ncol(merged.counts) - 2)],
+                                          1,
+                                          mean,
+                                          na.rm = TRUE)
+    merged.counts\$'fitness sd' <- apply(merged.counts[,c(ncol(merged.counts) - 1 - reps):c(ncol(merged.counts) - 2)],
+                                        1,
+                                        sd,
+                                        na.rm = TRUE)
+
+  }
+
+  merged.counts
+}
+
+## --- Main function ---
+
+#' Run default fitness estimation with configurable I/O paths
+#'
+#' @param counts_path Path to counts_merged.tsv
+#' @param design_path Path to experimentalDesign.tsv
+#' @param wt_seq_path Path to synonymous_wt.txt (single line DNA sequence)
+#' @param output_path Path to write fitness_estimation.tsv
+#'
+#' @return Invisibly returns the final data.frame; writes the output to output_path.
+run_fitness_estimation <- function(counts_path,
+                                   design_path,
+                                   wt_seq_path,
+                                   output_path) {
+  ## 1. Import key files ##
+  #########################
+
+  merged.counts <- read.table(counts_path, sep = "\t", header = TRUE, check.names = FALSE)
+  exp.design   <- read.table(design_path, sep = "\t", header = TRUE, check.names = FALSE)
+  wt.seq       <- DNAString(as.character(read.table(wt_seq_path)))
+  wt.seq.aa    <- translate(wt.seq)
+
+  ## 2. Pre-processing the count table ##
+  #######################################
+
+  ## calculate nt hamming distances from the specified WT
+  merged.counts <- compute_nt_hamming(merged.counts, wt.seq)
+
+  ## translate sequences
+  merged.counts <- add_aa_seq(merged.counts)
+
+  ## calculate AA hamming distances from the WT
+  merged.counts <- compute_aa_hamming(merged.counts, wt.seq.aa)
+
+  ## name the mutations
+  merged.counts <- name_mutations(merged.counts, wt.seq.aa)
+
+  ## find stops, WT and WT; aggregate AA-identical variants (except WT)
+  merged.counts <- aggregate_by_aa(merged.counts)
+
+  ## 3. Raw fitness calculations ##
+  #################################
+  fitness_res <- calc_raw_fitness(merged.counts, exp.design)
+  merged.counts <- fitness_res\$merged.counts
+  reps <- fitness_res\$reps
+
+  ## 4. Fitness and error refinements ##
+  ######################################
+  merged.counts <- rescale_and_summarize(merged.counts, reps)
+
+  ## clean up
+  rm(reps)
+
+  ## export
+  write.table(merged.counts, output_path,
+              col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t", na = "")
+
+  invisible(merged.counts)
+}
+
+
+## 5. Version ##
+################
+
+# sessionInfo()
+# R version 4.5.1 (2025-06-13)
+# Platform: aarch64-apple-darwin20
+# Running under: macOS Sonoma 14.6.1
+#
+# Matrix products: default
+# BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
+# LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
+#
+# locale:
+# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+#
+# time zone: Europe/Madrid
+# tzcode source: internal
+#
+# attached base packages:
+# [1] stats4    stats     graphics  grDevices utils     datasets  methods   base
+#
+# other attached packages:
+# [1] Biostrings_2.76.0   GenomeInfoDb_1.44.2 XVector_0.48.0      IRanges_2.42.0      S4Vectors_0.46.0
+# [6] BiocGenerics_0.54.0 generics_0.1.4
+#
+# loaded via a namespace (and not attached):
+# [1] httr_1.4.7              compiler_4.5.1          R6_2.6.1                tools_4.5.1
+# [5] GenomeInfoDbData_1.2.14 rstudioapi_0.17.1       crayon_1.5.3            UCSC.utils_1.4.0
+# [9] jsonlite_2.0.0
+
+
+
+#####
+# run function
+#####
+run_fitness_estimation(
+  counts_path = "$counts_merged",
+  design_path = "$exp_design",
+  wt_seq_path = "$syn_wt_txt",
+  output_path = "fitness_estimation.tsv"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_experimental_design/environment.yml b/modules/local/fitness/fitness_experimental_design/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_experimental_design/main.nf b/modules/local/fitness/fitness_experimental_design/main.nf
new file mode 100644
index 0000000..b1793ba
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/main.nf
@@ -0,0 +1,29 @@
+process EXPDESIGN_FITNESS {
+  tag "experimentalDesign"
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    path samplesheet_csv
+
+  output:
+    path "experimentalDesign.tsv", emit: experimental_design
+    path "versions.yml",          emit: versions
+
+  script:
+  template 'dimsum_experimentalDesign.R'
+
+  stub:
+  """
+  touch experimentalDesign.tsv
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/fitness_experimental_design/meta.yml b/modules/local/fitness/fitness_experimental_design/meta.yml
new file mode 100644
index 0000000..74d5c93
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/meta.yml
@@ -0,0 +1,37 @@
+name: "expdesign_fitness"
+description: Transforms a standard nf-core samplesheet into a tab-separated experimental design file that defines replicates, selection conditions, and input/output relationships for fitness calculation.
+keywords:
+  - deep mutational scanning
+  - dms
+  - experimental design
+  - samplesheet
+  - dimsum
+  - mutscan
+tools:
+  - "r-base":
+      description: "R is a free software environment for statistical computing and graphics"
+      homepage: "https://www.r-project.org/"
+      documentation: "https://cran.r-project.org/manuals.html"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - samplesheet_csv:
+        type: file
+        description: The primary nf-core pipeline samplesheet containing metadata for all sequencing libraries
+        pattern: "*.{csv}"
+
+output:
+  - experimental_design:
+      - experimentalDesign.tsv:
+          type: file
+          description: A TSV file formatted specifically for DiMSum or mutscan experimental design requirements
+          pattern: "experimentalDesign.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/fitness_experimental_design/templates/dimsum_experimentalDesign.R b/modules/local/fitness/fitness_experimental_design/templates/dimsum_experimentalDesign.R
new file mode 100644
index 0000000..475353d
--- /dev/null
+++ b/modules/local/fitness/fitness_experimental_design/templates/dimsum_experimentalDesign.R
@@ -0,0 +1,111 @@
+#!/usr/bin/env Rscript
+
+# Make a DiMSum experimental design from a deepmutscan samplesheet.
+# - samplesheet_csv: path to CSV with columns sample,type,replicate,file1,file2
+# - out_path: where to write the TSV (default "experimentalDesign.tsv")
+# Returns: the experimental design as a data.frame
+make_dimsum_experimental_design <- function(samplesheet_csv, out_path = "experimentalDesign.tsv") {
+  # ---- read & normalize ----
+  ss <- read.csv(samplesheet_csv, stringsAsFactors = FALSE, check.names = FALSE)
+  names(ss) <- tolower(names(ss))
+
+  # tolerate missing file2 column (single-end)
+  if (!"file2" %in% names(ss)) ss\$file2 <- ""
+
+  required <- c("sample", "type", "replicate", "file1", "file2")
+  missing  <- setdiff(required, names(ss))
+  if (length(missing) > 0) stop("Samplesheet missing columns: ", paste(missing, collapse = ", "))
+
+  # coerce types
+  ss\$replicate <- as.integer(ss\$replicate)
+
+  # ---- keep only the samples DiMSum scores ----
+  # A samplesheet legitimately carries other row types: `wildtype` (required by
+  # --error_correction wildtype) and `quality`. They are real samples, but they are not part of the
+  # selection design - they feed error correction and QC - and MERGE_COUNTS drops them too. Leaving
+  # them in gave them an empty `selection_id`, which DiMSum reads back as NA and then dies in
+  # dimsum__check_experiment_design on `sum(exp_design[,"selection_id"] < 0)` with the opaque
+  # "missing value where TRUE/FALSE needed".
+  ss <- ss[ss\$type %in% c("input", "output"), , drop = FALSE]
+  if (nrow(ss) == 0) {
+    stop("Samplesheet has no 'input'/'output' rows - DiMSum needs both to compute fitness.")
+  }
+
+  # ---- order rows: by sample (if multiple), input before output, then replicate ----
+  # Sorting happens before naming, because the names are derived from the row order below.
+  multi_base <- length(unique(ss\$sample)) > 1
+  type_rank  <- match(ss\$type, c("input", "output"))
+  ord <- if (multi_base) {
+    order(ss\$sample, type_rank, ss\$replicate)
+  } else {
+    order(type_rank, ss\$replicate)
+  }
+  ss <- ss[ord, , drop = FALSE]
+
+  # ---- sample_name: must equal the count column MERGE_COUNTS writes for this row ----
+  # merge_counts.R names its columns positionally - paste0("input", seq_len(n_in)) over the files
+  # sorted by replicate - so the name has to come from each row's POSITION within its type, not from
+  # the samplesheet's `replicate` value. The two only coincide when the replicates happen to be a
+  # contiguous 1..N; replicates 1,2,4 would otherwise produce "input4" for a column named "input3"
+  # and DiMSum could not match them. DiMSum also requires sample_name to be alphanumeric and to
+  # start with a letter, so the parts are joined without a separator.
+  idx <- stats::ave(seq_len(nrow(ss)), ss\$sample, ss\$type, FUN = seq_along)
+  # NOTE: for multiple biological samples these names still will not match the counts file, because
+  # one design is built from the whole samplesheet and broadcast to every per-sample DiMSum run
+  # (see subworkflows/local/calculate_fitness/main.nf). Multi-sample + DiMSum is a separate,
+  # known-broken case; single-sample runs are correct.
+  sample_name <- if (multi_base) paste0(ss\$sample, ss\$type, idx) else paste0(ss\$type, idx)
+
+  # ---- build DiMSum columns ----
+  experiment_replicate <- ss\$replicate           # pairs an input with an output; need not be 1..N
+  selection_id <- ifelse(ss\$type == "input", 0L, 1L)
+  # assume one selection batch; DiMSum requires this to be blank for input samples
+  selection_replicate <- ifelse(ss\$type == "output", 1L, NA_integer_)
+  # assume one technical batch
+  technical_replicate <- rep(1L, nrow(ss))
+
+  pair1 <- basename(ss\$file1)
+  # keep empty string for single-end / missing file2
+  pair2 <- ifelse(is.na(ss\$file2) | ss\$file2 == "", "", basename(ss\$file2))
+
+  ed <- data.frame(
+    sample_name          = sample_name,
+    experiment_replicate = experiment_replicate,
+    selection_id         = selection_id,
+    selection_replicate  = selection_replicate,
+    technical_replicate  = technical_replicate,
+    pair1                = pair1,
+    pair2                = pair2,
+    stringsAsFactors     = FALSE
+  )
+  rownames(ed) <- NULL
+
+  # ---- write & return ----
+  write.table(ed, file = out_path, sep = "\\t", row.names = FALSE, col.names = TRUE, quote = FALSE, na = "")
+  return(ed)
+}
+
+#####
+# run function
+#####
+make_dimsum_experimental_design(
+  samplesheet_csv = "$samplesheet_csv",
+  out_path = "experimentalDesign.tsv"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+
+if (is.null(r_version)) r_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/fitness_heatmap/environment.yml b/modules/local/fitness/fitness_heatmap/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/fitness_heatmap/main.nf b/modules/local/fitness/fitness_heatmap/main.nf
new file mode 100644
index 0000000..4106ba6
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/main.nf
@@ -0,0 +1,30 @@
+process FITNESS_HEATMAP {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    tuple val(sample), path(fitness_estimation_tsv)   // from FITNESS_CALCULATION
+    tuple val(sample2), path(wt_seq)   		      // WT sequence
+
+  output:
+    tuple val(sample), path("fitness_heatmap.pdf"), emit: fitness_heatmap
+    path "versions.yml", emit: versions
+
+  script:
+    template 'fitness_heatmap.R'
+
+  stub:
+  """
+    touch fitness_heatmap.pdf
+    cat > versions.yml <<'EOF'
+    FITNESS_HEATMAP:
+      stub-version: "0.0.0"
+    EOF
+  """
+}
diff --git a/modules/local/fitness/fitness_heatmap/meta.yml b/modules/local/fitness/fitness_heatmap/meta.yml
new file mode 100644
index 0000000..7b22869
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/meta.yml
@@ -0,0 +1,59 @@
+name: "fitness_heatmap"
+description: Generates a fitness landscape heatmap from calculated fitness scores, displaying the functional impact of amino acid substitutions at each position of the reference sequence.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness
+  - visualization
+  - heatmap
+  - mutation landscape
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+  - "bioconductor-biostrings":
+      description: "Efficient manipulation of biological strings in R"
+      homepage: "https://bioconductor.org/packages/Biostrings"
+      licence: ["Artistic-2.0"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information for the fitness data
+          e.g. `[ id:'test' ]`
+    - fitness_estimation_tsv:
+        type: file
+        description: TSV file containing calculated fitness scores and statistics
+        pattern: "*.{tsv}"
+  - - sample2:
+        type: map
+        description: |
+          Groovy Map containing metadata for the reference sequence
+    - wt_seq:
+        type: file
+        description: FASTA file or text file containing the wild-type reference sequence
+        pattern: "*.{fasta,fa,txt}"
+
+output:
+  - fitness_heatmap:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_heatmap.pdf:
+          type: file
+          description: PDF heatmap visualizing the fitness scores across the protein sequence
+          pattern: "fitness_heatmap.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/fitness_heatmap/templates/fitness_heatmap.R b/modules/local/fitness/fitness_heatmap/templates/fitness_heatmap.R
new file mode 100644
index 0000000..e860382
--- /dev/null
+++ b/modules/local/fitness/fitness_heatmap/templates/fitness_heatmap.R
@@ -0,0 +1,312 @@
+#!/usr/bin/env Rscript
+
+suppressPackageStartupMessages({
+  library(methods)
+  library(dplyr)
+  library(ggplot2)
+  library(grid)        # for unit()
+})
+
+# ---------- helper functions ----------
+find_col <- function(df, candidates) {
+  norm <- function(x) gsub("[^a-z0-9]+", "_", tolower(x))
+  nms <- colnames(df); nn <- norm(nms)
+  for (cand in candidates) {
+    hit <- which(nn == norm(cand))
+    if (length(hit) == 1) return(nms[hit])
+  }
+  stop(sprintf("Could not find any of columns: %s", paste(candidates, collapse = ", ")))
+}
+
+get_rescaled_cols <- function(df) {
+  nms  <- colnames(df)
+  hits <- grep("^rescaled[_ ]?fitness", nms, ignore.case = TRUE, value = TRUE)
+  if (!length(hits)) stop("No 'rescaled_fitness' columns found.")
+  idx <- suppressWarnings(as.integer(gsub(".*?([0-9]+)\$", "\\\\1", hits))) 	# additional backslashs to make it groovy readable
+  hits[order(is.na(idx), idx, hits)]
+}
+
+# Find "mean fitness" column (if present)
+find_mean_col <- function(df) {
+  nms <- colnames(df)
+  key <- tolower(gsub("[^a-z0-9]+", "_", nms))
+  hit <- which(key == "mean_fitness")
+  if (length(hit) == 1) nms[hit] else NULL
+}
+
+# NEW: read WT amino acid sequence from .txt file (single line)
+read_wt_seq_aa_txt <- function(path) {
+  if (is.null(path)) stop("wt_seq_aa_txt_path must be provided.")
+  x <- readLines(path, warn = FALSE)
+  x <- x[nzchar(x)]
+  if (!length(x)) stop("WT AA TXT is empty.")
+  aa <- toupper(gsub("\\\\s+", "", x[which.max(nchar(x))]))
+  # Keep only valid amino acid letters (including stop '*')
+  aa <- gsub("[^ACDEFGHIKLMNPQRSTVWY*]", "", aa)
+  if (!nchar(aa)) stop("WT AA TXT contains no valid AA letters.")
+  aa
+}
+
+# Build full AA×position grid for positions 1..wt_len (from WT sequence),
+# join fitness values; any missing combos stay NA (-> grey).
+# Positions > wt_len are considered padded and will be white.
+build_heatmap_long <- function(df,
+                               wt_aa_col,
+                               pos_col,
+                               mut_aa_col,
+                               fitness_col,
+                               positions_per_row = 75,
+                               wt_seq_aa,
+                               fill_missing_as_zero = FALSE) {
+
+  # authoritative WT length from provided sequence
+  letters <- strsplit(wt_seq_aa, "", fixed = TRUE)[[1]]
+  wt_len  <- length(letters)
+
+  # normalize data
+  df0 <- df %>%
+    transmute(
+      position = suppressWarnings(as.numeric(.data[[pos_col]])),
+      wt_aa_in = .data[[wt_aa_col]],
+      mut_aa   = .data[[mut_aa_col]],
+      fitness  = suppressWarnings(as.numeric(.data[[fitness_col]]))
+    ) %>%
+    filter(is.finite(position))
+
+  # drop any rows that claim positions beyond WT length
+  if (nrow(df0) && any(df0\$position > wt_len, na.rm = TRUE)) {
+    dropped <- sum(df0\$position > wt_len, na.rm = TRUE)
+    warning(sprintf("Dropping %d row(s) with position > WT length (%d).", dropped, wt_len))
+    df0 <- df0 %>% filter(position <= wt_len)
+  }
+
+  # pad to next multiple of 75 (by rows)
+  rem       <- wt_len %% positions_per_row
+  pad_need  <- if (rem == 0) 0 else positions_per_row - rem
+  max_paded <- wt_len + pad_need
+
+  # full grid: positions 1..max_paded (so the tail exists), AA set of 21
+  all_positions   <- seq_len(max_paded)
+  all_amino_acids <- c("A","C","D","E","F","G","H","I","K","L",
+                       "M","N","P","Q","R","S","T","V","W","Y","*")
+
+  grid_df <- expand.grid(position = all_positions,
+                         mut_aa   = all_amino_acids,
+                         KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE) %>%
+    mutate(is_padded = position > wt_len)
+
+  # join fitness only for real positions (<= wt_len)
+  fit_df <- df0 %>% select(position, mut_aa, fitness)
+  d <- grid_df %>%
+    left_join(fit_df, by = c("position","mut_aa"))
+
+  if (fill_missing_as_zero) {
+    d\$fitness[is.na(d\$fitness) & d\$position <= wt_len] <- 0
+  }
+
+  # authoritative WT AA per real position; tail gets placeholder 'Y'
+  wt_map <- tibble(position = seq_len(wt_len), wt_aa = letters)
+  d <- d %>%
+    left_join(wt_map, by = "position") %>%
+    mutate(wt_aa = ifelse(is.na(wt_aa) & position > wt_len, "Y", wt_aa))
+
+  # layout fields
+  d <- d %>%
+    mutate(
+      row_group = ((position - 1) %/% positions_per_row) + 1,
+      wt_aa_pos = paste0(wt_aa, position),
+      wt_aa_pos = factor(wt_aa_pos, levels = unique(wt_aa_pos)),
+      synonymous = mut_aa == wt_aa
+    )
+
+  # IMPORTANT: use WT length as the true end of the protein
+  d\$max_pos <- wt_len
+  d
+}
+
+syn_segments <- function(d, positions_per_row = 75) {
+  amino_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+  d %>%
+    mutate(
+      mut_aa = factor(mut_aa, levels = amino_order),
+      x = as.numeric(factor(wt_aa_pos, levels = levels(wt_aa_pos))) -
+          ((row_group - 1) * positions_per_row),
+      y = as.numeric(factor(mut_aa, levels = amino_order))
+    ) %>%
+    filter(synonymous, position <= max_pos)
+}
+
+# Draw one solid white rectangle per row group covering the padded tail region
+white_tail_rects <- function(d, positions_per_row = 75) {
+  wt_len <- unique(d\$max_pos)[1]
+  if (!is.finite(wt_len)) return(dplyr::tibble()[0,])
+
+  # if perfectly divisible by 75, there is no tail to cover
+  if (wt_len %% positions_per_row == 0) return(dplyr::tibble()[0,])
+
+  # which facet (row group) contains the last real position?
+  last_group     <- ((wt_len - 1) %/% positions_per_row) + 1
+  last_local_idx <- ((wt_len - 1) %% positions_per_row) + 1  # 1..75 within the facet
+
+  tibble::tibble(
+    row_group = last_group,
+    xmin = last_local_idx + 0.5 - 0.025,     # tiny epsilon to avoid hairlines
+    xmax = positions_per_row + 0.5 + 0.025,
+    ymin = 0.5  - 0.025,
+    ymax = 21.5 + 0.025
+  )
+}
+
+plot_heatmap <- function(d, title_text, positions_per_row = 75) {
+  amino_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+  d <- d %>% mutate(mut_aa = factor(mut_aa, levels = amino_order))
+
+  min_f <- suppressWarnings(min(d\$fitness, na.rm = TRUE)); if (!is.finite(min_f)) min_f <- 0
+  max_f <- suppressWarnings(max(d\$fitness, na.rm = TRUE)); if (!is.finite(max_f)) max_f <- 0
+  max_orig_pos <- unique(d\$max_pos)[1]
+
+  syn  <- syn_segments(d, positions_per_row)
+  rect <- white_tail_rects(d, positions_per_row)
+
+  ggplot(d, aes(x = wt_aa_pos, y = mut_aa, fill = fitness)) +
+    scale_fill_gradientn(
+      colours = c("#D73027", "#F0F0F0", "#4575B4"),
+      values  = if ((abs(min_f) + max_f) > 0) c(0, abs(min_f)/(abs(min_f)+max_f), 1) else c(0, 0.5, 1),
+      na.value = "grey35",
+      limits   = c(min_f, max_f)
+    ) +
+    scale_x_discrete(
+      labels = function(x) {
+        num <- suppressWarnings(as.numeric(gsub("[^0-9]", "", x)))
+        ifelse(num > max_orig_pos, " ", x)
+      },
+      expand = expansion(mult = c(0, 0))  # no extra margin area
+    ) +
+    geom_tile() +
+    # Solid white block covering the tail (no pattern / no seams)
+    { if (nrow(rect)) geom_rect(data = rect, inherit.aes = FALSE,
+                                aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
+                                fill = "white", color = NA) } +
+    geom_segment(
+      data = syn,
+      aes(x = x - 0.485, xend = x + 0.485, y = y - 0.485, yend = y + 0.485),
+      linewidth = 0.2, inherit.aes = FALSE, color = "grey10"
+    ) +
+    theme_minimal() +
+    labs(title = title_text, x = "Wild-type amino acid", y = "Mutant amino acid", fill = "Fitness") +
+    theme(
+      plot.title   = element_text(size = 16, face = "bold"),
+      axis.text.x  = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 10),
+      axis.text.y  = element_text(size = 10),
+      axis.title.x = element_text(size = 14),
+      axis.title.y = element_text(size = 14),
+      legend.title = element_text(size = 12),
+      legend.text  = element_text(size = 10),
+      panel.grid.major = element_blank(),
+      panel.grid.minor = element_blank(),
+      strip.text = element_blank(),
+      strip.background = element_blank(),
+      panel.spacing = grid::unit(0.2, "lines")
+    ) +
+    facet_wrap(~ row_group, scales = "free_x", ncol = 1)
+}
+
+# ---------- main callable ----------
+# fitness_table_path : path to fitness_estimation.tsv
+# wt_seq_aa_txt_path : path to TXT file containing WT AA sequence (one line)
+# output_pdf_path    : output PDF (default "fitness_heatmap.pdf")
+# positions_per_row  : default 75
+run_fitness_rescaled_heatmaps <- function(fitness_table_path,
+                                          wt_seq_aa_txt_path,
+                                          output_pdf_path = "fitness_heatmap.pdf",
+                                          positions_per_row = 75) {
+
+  df <- read.table(
+    fitness_table_path, sep = "\t", header = TRUE,
+    check.names = FALSE, quote = "", comment.char = ""
+  )
+
+  wt_aa_col     <- find_col(df, c("wt aa", "wt_aa", "wt"))
+  pos_col       <- find_col(df, c("pos", "position"))
+  mut_aa_col    <- find_col(df, c("mut aa", "mut_aa", "aa"))
+  rescaled_cols <- get_rescaled_cols(df)
+
+  wt_seq_aa <- read_wt_seq_aa_txt(wt_seq_aa_txt_path)
+
+  plots <- list()
+
+  ## 1) Mean first – use existing "mean fitness" column if available
+  mean_col <- find_mean_col(df)
+  if (is.null(mean_col)) {
+    df\$`rescaled_fitness_mean` <- if (length(rescaled_cols) == 1) df[[rescaled_cols[1]]] else rowMeans(df[, rescaled_cols], na.rm = TRUE)
+    mean_col <- "rescaled_fitness_mean"
+  }
+  long_df_mean <- build_heatmap_long(df, wt_aa_col, pos_col, mut_aa_col, mean_col,
+                                     positions_per_row, wt_seq_aa)
+  plots[[length(plots) + 1]] <- list(
+    title = sprintf("Fitness — mean of %d replicate(s)", length(rescaled_cols)),
+    data  = long_df_mean
+  )
+
+  ## 2) Then individual replicates
+  for (i in seq_along(rescaled_cols)) {
+    col <- rescaled_cols[i]
+    long_df <- build_heatmap_long(df, wt_aa_col, pos_col, mut_aa_col, col,
+                                  positions_per_row, wt_seq_aa)
+    plots[[length(plots) + 1]] <- list(
+      title = sprintf("Fitness — rep%d", i),
+      data  = long_df
+    )
+  }
+
+  # Device height: (#row groups × 4)
+  page_heights <- vapply(plots, function(p) max(p\$data\$row_group, na.rm = TRUE), numeric(1))
+  device_height <- max(4, as.numeric(page_heights) * 4, na.rm = TRUE)
+
+  grDevices::pdf(output_pdf_path, width = 16, height = device_height)
+  on.exit(try(grDevices::dev.off(), silent = TRUE), add = TRUE)
+  for (p in plots) print(plot_heatmap(p\$data, p\$title, positions_per_row))
+  invisible(TRUE)
+}
+
+####
+# run function
+####
+run_fitness_rescaled_heatmaps(
+  fitness_table_path = "$fitness_estimation_tsv",
+  wt_seq_aa_txt_path = "$wt_seq",
+  output_pdf_path    = "fitness_heatmap.pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+methods_version <- as.character(packageVersion("methods"))
+grid_version <- as.character(packageVersion("grid"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(methods_version) == 0) methods_version <- "unknown"
+if (length(grid_version) == 0) grid_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-methods:', methods_version),
+    paste('    r-grid:', grid_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/merge_counts/environment.yml b/modules/local/fitness/merge_counts/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/fitness/merge_counts/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/fitness/merge_counts/main.nf b/modules/local/fitness/merge_counts/main.nf
new file mode 100644
index 0000000..c85d2f4
--- /dev/null
+++ b/modules/local/fitness/merge_counts/main.nf
@@ -0,0 +1,29 @@
+process MERGE_COUNTS {
+  tag "${sample.sample}"
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    tuple val(sample), path(input_counts), path(output_counts)
+
+  output:
+    tuple val(sample), path("counts_merged.tsv"), emit: merged_counts
+    path "versions.yml", emit: versions
+
+  script:
+  template 'merge_counts.R'
+
+  stub:
+  """
+  touch counts_merged.tsv
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/merge_counts/meta.yml b/modules/local/fitness/merge_counts/meta.yml
new file mode 100644
index 0000000..87e035a
--- /dev/null
+++ b/modules/local/fitness/merge_counts/meta.yml
@@ -0,0 +1,51 @@
+name: "merge_counts"
+description: Consolidates variant counts from multiple input (baseline) and output (selection) files into a single merged TSV table, organized by sample or replicate.
+keywords:
+  - deep mutational scanning
+  - dms
+  - count merging
+  - variant table
+  - preprocessing
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - metas:
+        type: list
+        description: A list of metadata maps corresponding to the input and output count files
+    - input_counts:
+        type: list
+        description: A list of paths to count files representing the baseline/input population
+    - output_counts:
+        type: list
+        description: A list of paths to count files representing the selection/output population
+
+output:
+  - merged_counts:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - counts_merged.tsv:
+          type: file
+          description: A consolidated TSV file containing merged counts for all provided inputs and outputs
+          pattern: "counts_merged.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/merge_counts/templates/merge_counts.R b/modules/local/fitness/merge_counts/templates/merge_counts.R
new file mode 100644
index 0000000..8e39be8
--- /dev/null
+++ b/modules/local/fitness/merge_counts/templates/merge_counts.R
@@ -0,0 +1,112 @@
+#!/usr/bin/env Rscript
+
+# --------------------------------------------------------------
+# 1. SETUP & INPUTS
+# --------------------------------------------------------------
+
+raw_inputs_str  <- "$input_counts"
+raw_outputs_str <- "$output_counts"
+
+parse_paths <- function(x) {
+  if (x == "" || x == "[]") return(character(0))
+  # Split at spaces
+  parts <- strsplit(x, "\\\\s+")[[1]]
+  # Remove empty strings
+  parts[parts != ""]
+}
+
+input_paths  <- parse_paths(raw_inputs_str)
+output_paths <- parse_paths(raw_outputs_str)
+
+message("Found ", length(input_paths),  " input files.")
+message("Found ", length(output_paths), " output files.")
+
+# --------------------------------------------------------------
+# 2. MERGE LOGIC
+# --------------------------------------------------------------
+
+# Helper to read a 2-col TSV without header
+read_counts <- function(fp) {
+  utils::read.table(
+    fp, header = FALSE, sep = "\\t", quote = "",
+    col.names = c("nt_seq", "count"),
+    colClasses = c("character", "numeric"),
+    comment.char = "", check.names = FALSE
+  )
+}
+
+# Read all inputs / outputs
+input_list  <- lapply(input_paths,  read_counts)
+output_list <- lapply(output_paths, read_counts)
+
+# Collect universe of sequences
+all_seqs <- unique(c(
+  unlist(lapply(input_list,  function(x) x\$nt_seq)),
+  unlist(lapply(output_list, function(x) x\$nt_seq))
+))
+
+# Pre-allocate output frame
+n_in  <- length(input_list)
+n_out <- length(output_list)
+
+col_names <- c(
+  "nt_seq",
+  if (n_in  > 0) paste0("input",  seq_len(n_in))  else character(0),
+  if (n_out > 0) paste0("output", seq_len(n_out)) else character(0)
+)
+
+# Initialize dataframe with 0 counts
+out <- data.frame(
+  nt_seq = all_seqs,
+  matrix(0, nrow = length(all_seqs), ncol = n_in + n_out),
+  stringsAsFactors = FALSE, check.names = FALSE
+)
+names(out) <- col_names
+
+# Fill inputs
+if (n_in > 0) {
+  for (i in seq_len(n_in)) {
+    df <- input_list[[i]]
+    # Match sequences: WICHTIG \$ escaping
+    idx <- match(df\$nt_seq, out\$nt_seq)
+    # Assign counts: WICHTIG \$ escaping
+    out[idx, paste0("input", i)] <- df\$count
+  }
+}
+
+# Fill outputs
+if (n_out > 0) {
+  for (j in seq_len(n_out)) {
+    df <- output_list[[j]]
+    # WICHTIG \$ escaping
+    idx <- match(df\$nt_seq, out\$nt_seq)
+    out[idx, paste0("output", j)] <- df\$count
+  }
+}
+
+# Write Output
+utils::write.table(
+  out,
+  file = "counts_merged.tsv",
+  sep = "\\t",
+  row.names = FALSE,
+  col.names = TRUE,
+  quote = FALSE
+)
+
+# --------------------------------------------------------------
+# 3. VERSIONING
+# --------------------------------------------------------------
+
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+if (is.null(r_version)) r_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/fitness/run_dimsum/environment.yml b/modules/local/fitness/run_dimsum/environment.yml
new file mode 100644
index 0000000..1c93270
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  # Every pin here mirrors the paired container exactly. r-base must be pinned: with r-dimsum alone the
+  # solver picks an ancient R (4.0.5), not the container's 4.4.2. ggplot2/GGally must ALSO be pinned:
+  # DiMSum 1.4 builds its merge/diagnostics reports through GGally, which breaks under ggplot2 4.0 (the
+  # solver floats to 4.0.3 on R 4.4.2) with "object 'dimsum_meta_new_report' not found" and aborts the
+  # run; the container holds ggplot2 at 3.5.1 / GGally at 2.2.1, so we do the same.
+  - bioconda::r-dimsum=1.4
+  - conda-forge::r-base=4.4.2
+  - conda-forge::r-data.table=1.16.4
+  - bioconda::bioconductor-biostrings=2.74.0
+  - conda-forge::r-ggplot2=3.5.1
+  - conda-forge::r-ggally=2.2.1
diff --git a/modules/local/fitness/run_dimsum/main.nf b/modules/local/fitness/run_dimsum/main.nf
new file mode 100644
index 0000000..9c7d11c
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/main.nf
@@ -0,0 +1,54 @@
+process RUN_DIMSUM {
+  tag { sample.sample }
+  label 'process_single'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+      ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/92/9298c391f285d7f2c1155f6a519a96c4f7591971780ba4f10569558282f40b6f/data'
+      : 'community.wave.seqera.io/library/bioconductor-biostrings_r-dimsum_r-base_r-data.table_pruned:cca13eed371a9d84' }"
+
+  input:
+    tuple val(sample), path(counts_merged)
+    path(wt_txt)
+    path(exp_design)
+
+  output:
+    path "dimsum_results**", emit: results_dir
+    path "versions.yml", emit: versions
+
+  script:
+  """
+  set -euo pipefail
+
+  # DiMSum expects the sequence string, not a file path
+  WT=\$(tr -d ' \r\\n\\t' < "$wt_txt")
+
+  DiMSum \
+      --experimentDesignPath "$exp_design" \
+      --wildtypeSequence "\$WT" \
+      --countPath "$counts_merged" \
+      --startStage 4 \
+      --stopStage 5 \
+      --fitnessErrorModel F \
+      --retainIntermediateFiles T \
+      --projectName "dimsum_results" \
+      --fastqFileDir . \
+
+  R_VERSION=\$(R --version | head -n 1 | sed -E 's/^R version ([0-9.]+).*/\\1/')
+  cat <<-END_VERSIONS > versions.yml
+  DIMSUM_RUN:
+    r-base: \$R_VERSION
+END_VERSIONS
+  """
+
+  stub:
+  """
+  mkdir -p dimsum_results
+  touch dimsum_results/report.html
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/run_dimsum/meta.yml b/modules/local/fitness/run_dimsum/meta.yml
new file mode 100644
index 0000000..3e889f9
--- /dev/null
+++ b/modules/local/fitness/run_dimsum/meta.yml
@@ -0,0 +1,49 @@
+name: "run_dimsum"
+description: Runs DiMSum to estimate fitness and functionality scores from Deep Mutational Scanning (DMS) count data.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness
+  - dimsum
+tools:
+  - "dimsum":
+      description: "An error-model-enriched pipeline for analyzing deep mutational scanning data"
+      homepage: "https://github.com/lehner-lab/DiMSum"
+      documentation: "https://github.com/lehner-lab/DiMSum/blob/master/README.md"
+      doi: "10.1186/s12859-020-03709-3"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information.
+          Matches the structure used in the pipeline (e.g., [ id:'test' ])
+    - counts_merged:
+        type: file
+        description: A TSV file containing variant counts merged across samples
+        pattern: "*.{tsv,csv}"
+  - - wt_txt:
+        type: file
+        description: A text file containing the wild-type DNA/amino acid sequence string
+        pattern: "*.{txt}"
+  - - exp_design:
+        type: file
+        description: DiMSum-specific experimental design file describing replicates and conditions
+        pattern: "*.{txt,csv}"
+
+output:
+  - results_dir:
+      - dimsum_results**:
+          type: directory
+          description: Directory containing the full suite of DiMSum output files, including fitness estimates and plots
+          pattern: "dimsum_results*"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/run_mutscan/environment.yml b/modules/local/fitness/run_mutscan/environment.yml
new file mode 100644
index 0000000..a8b1c61
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - bioconda::bioconductor-mutscan=1.0.0
+  - conda-forge::r-base=4.5.1
diff --git a/modules/local/fitness/run_mutscan/main.nf b/modules/local/fitness/run_mutscan/main.nf
new file mode 100644
index 0000000..3024b0b
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/main.nf
@@ -0,0 +1,33 @@
+process RUN_MUTSCAN {
+  tag "${sample.sample}"
+  label 'process_medium'
+
+  conda "${moduleDir}/environment.yml"
+
+  container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+  input:
+    tuple val(sample), path(counts_merged)
+    path(syn_wt_txt)
+    path(exp_design)
+
+  output:
+    tuple val(sample), path("fitness_estimation_mutscan_edgeR.tsv"), emit: fitness_mutscan_edgeR
+    tuple val(sample), path("fitness_estimation_mutscan_limma.tsv"), emit: fitness_mutscan_limma
+    tuple val(sample), path("*.pdf"), emit: qc_plots
+    path "versions.yml", emit: versions
+
+  script:
+  template 'fitness_calculation_mutscan.R'
+
+  stub:
+  """
+  touch fitness_estimation_mutscan_edgeR.tsv fitness_estimation_mutscan_limma.tsv mutscan_stub.pdf
+  cat <<-END_VERSIONS > versions.yml
+  "${task.process}":
+      r-base: "0.0.0"
+  END_VERSIONS
+  """
+}
diff --git a/modules/local/fitness/run_mutscan/meta.yml b/modules/local/fitness/run_mutscan/meta.yml
new file mode 100644
index 0000000..f80e038
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/meta.yml
@@ -0,0 +1,81 @@
+name: "run_mutscan"
+description: Calculates fitness and functionality scores using the mutscan R package, employing edgeR and limma-voom statistical frameworks for robust estimation from input and output variant counts.
+keywords:
+  - deep mutational scanning
+  - dms
+  - fitness estimation
+  - mutscan
+  - edger
+  - limma
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+  - "edger":
+      description: "Empirical Analysis of Digital Gene Expression Data in R"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/edgeR.html"
+      doi: "10.1093/bioinformatics/btp616"
+      licence: ["GPL-2.0-or-later"]
+  - "limma":
+      description: "Linear Models for Microarray Data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/limma.html"
+      doi: "10.1093/nar/gkv007"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - sample:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test' ]`
+    - counts_merged:
+        type: file
+        description: TSV/CSV file containing variant counts merged across samples or replicates
+        pattern: "*.{tsv,csv}"
+  - - syn_wt_txt:
+        type: file
+        description: Text file identifying the synonymous wild-type mutation used as the reference baseline
+        pattern: "*.txt"
+  - - exp_design:
+        type: file
+        description: Experimental design file defining replicates, conditions, and groupings for statistical analysis
+        pattern: "*.{csv,txt}"
+
+output:
+  - fitness_mutscan_edgeR:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_mutscan_edgeR.tsv:
+          type: file
+          description: Fitness estimation results generated using the edgeR framework
+          pattern: "fitness_estimation_mutscan_edgeR.tsv"
+  - fitness_mutscan_limma:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - fitness_estimation_mutscan_limma.tsv:
+          type: file
+          description: Fitness estimation results generated using the limma framework
+          pattern: "fitness_estimation_mutscan_limma.tsv"
+  - qc_plots:
+      - sample:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.pdf":
+          type: file
+          description: Quality control plots generated by mutscan during fitness estimation
+          pattern: "*.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/fitness/run_mutscan/templates/fitness_calculation_mutscan.R b/modules/local/fitness/run_mutscan/templates/fitness_calculation_mutscan.R
new file mode 100644
index 0000000..8a196ff
--- /dev/null
+++ b/modules/local/fitness/run_mutscan/templates/fitness_calculation_mutscan.R
@@ -0,0 +1,371 @@
+#!/usr/bin/env Rscript
+
+## mutscan (Soneson et al., Genome Biology 2023) fitness estimation for nf-core/deepmutscan
+## 19.03.2026
+
+## 0. Libraries ##
+##################
+
+suppressPackageStartupMessages({
+  library(mutscan)
+  library(Biostrings)
+})
+
+## --- Helper functions ---
+
+# calculate nt hamming distances from the specified WT
+nbrMutBases <- function(x, wt.seq) {
+  x <- cbind(x, "nbrMutBases" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$nbrMutBases[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      x\$nbrMutBases[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  x\$nbrMutBases <- as.character(x\$nbrMutBases)
+  return(x)
+}
+
+# calculate codon hamming distances from the specified WT
+nbrMutCodons <- function(x, wt.seq) {
+  x <- cbind(x, "nbrMutCodons" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$nbrMutCodons[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else if(length(which(tmp.mut != tmp.wt)) == 1){
+      x\$nbrMutCodons[i] <- 1
+      rm(tmp.mut, tmp.wt)
+    }else if(length(which(tmp.mut != tmp.wt)) > 1){
+      mut.pos <- which(tmp.mut != tmp.wt)
+      x\$nbrMutCodons[i] <- length(unique(ceiling(mut.pos / 3)))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  x\$nbrMutCodons <- as.character(x\$nbrMutCodons)
+  return(x)
+}
+
+# translate sequences and add aa_seq
+sequenceAA <- function(x) {
+  x <- cbind(x, "sequenceAA" = as.character(translate(DNAStringSet(x\$sequence))))
+  return(x)
+}
+
+# calculate AA hamming distances from the WT
+nbrMutAAs <- function(x, wt.seq) {
+  x <- cbind(x, "nbrMutAAs" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(translate(wt.seq)), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequenceAA[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$nbrMutAAs[i] <- 0
+      rm(tmp.mut, tmp.wt)
+      next
+    }else{
+      x\$nbrMutAAs[i] <- length(which(tmp.mut != tmp.wt))
+      rm(tmp.mut, tmp.wt)
+      next
+    }
+  }
+  x\$nbrMutAAs <- as.character(x\$nbrMutAAs)
+  return(x)
+}
+
+# mutant base labels
+mutantNameBase <- function(x, wt.seq){
+  x <- cbind(x, "mutantNameBase" = rep(NA, nrow(x)))
+  for(i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "")[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "")[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$mutantNameBase[i] <- "f.0.WT"
+      rm(tmp.mut, tmp.wt)
+      next
+    }else if(length(which(tmp.mut != tmp.wt)) > 0){
+      mut.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantNameBase[i] <- paste0("f.", mut.pos, ".", tmp.mut[mut.pos], collapse = "_")
+      rm(tmp.mut, tmp.wt, mut.pos)
+      next
+    }
+  }
+  return(x)
+}
+
+# mutant codon labels
+mutantNameCodon <- function(x, wt.seq){
+  x <- cbind(x, "mutantNameCodon" = rep(NA, nrow(x)))
+  for(i in 1:nrow(x)){
+    tmp.wt <- strsplit(as.character(wt.seq), "(?<=.{3})", perl = TRUE)[[1]]
+    tmp.mut <- strsplit(as.character(x\$sequence[i]), "(?<=.{3})", perl = TRUE)[[1]]
+    if(length(which(tmp.mut != tmp.wt)) == 0){
+      x\$mutantNameCodon[i] <- "f.0.WT"
+      rm(tmp.mut, tmp.wt)
+      next
+    }else if(length(which(tmp.mut != tmp.wt)) > 0){
+      mut.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantNameCodon[i] <- paste0("f.", mut.pos, ".", tmp.mut[mut.pos], collapse = "_")
+      rm(tmp.mut, tmp.wt, mut.pos)
+      next
+    }
+  }
+  return(x)
+}
+
+# name the mutations (AA level)
+mutantNameAA <- function(x, wt.seq) {
+  x <- cbind(x, "mutantNameAA" = rep(NA, nrow(x)))
+  for (i in 1:nrow(x)){
+    if(x\$nbrMutAAs[i] == 0){
+      x\$mutantNameAA[i] <- "f.0.WT"
+    }else{
+      tmp.wt <- strsplit(as.character(translate(wt.seq)), "")[[1]]
+      tmp.mut <- strsplit(as.character(x\$sequenceAA[i]), "")[[1]]
+      tmp.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantNameAA[i] <- paste0("f.", tmp.pos, ".", tmp.mut[tmp.pos])
+      rm(tmp.mut, tmp.wt,tmp.pos)
+    }
+  }
+  return(x)
+}
+
+# categorise the mutation types
+mutationTypes <- function(x){
+  x <- cbind(x, "mutationTypes" = rep(NA, nrow(x)))
+  x[grep("WT", x\$mutantNameAA), "mutationTypes"] <- "silent" ## silent/synonymous
+  x[grep("[*]", x\$mutantNameAA), "mutationTypes"] <- "stop" ## stop
+  x[-grep("[*]|WT", x\$mutantNameAA), "mutationTypes"] <- "nonsynonymous" ## nonsynonymous
+  x
+}
+
+# mutant name
+mutantName <- function(x, wt.seq){
+  x <- cbind("mutantName" = rep(NA, nrow(x)), x)
+  for (i in 1:nrow(x)){
+    if(x\$nbrMutAAs[i] == 0){
+      x\$mutantName[i] <- "WT"
+    }else{
+      tmp.wt <- strsplit(as.character(translate(wt.seq)), "")[[1]]
+      tmp.mut <- strsplit(as.character(x\$sequenceAA[i]), "")[[1]]
+      tmp.pos <- which(tmp.mut != tmp.wt)
+      x\$mutantName[i] <- paste0(tmp.wt[tmp.pos], tmp.pos,tmp.mut[tmp.pos])
+      rm(tmp.mut, tmp.wt,tmp.pos)
+    }
+  }
+  return(x)
+}
+
+# mutscan 'summaryTable' building (from merged counts TSV file)
+mutscan.summaryTable.from.counts <- function(x, sample, wt.seq){
+
+  ### pre-process
+  x <- x[,c(1, grep(sample, colnames(x)))]
+  colnames(x) <- c("sequence", "nbrReads")
+  x <- cbind(x, "maxNbrReads" = x[,"nbrReads"], "nbrUmis" = x[,"nbrReads"])
+
+  ### nbrMutBases
+  x <- nbrMutBases(x, wt.seq)
+
+  ### nbrMutCodons
+  x <- nbrMutCodons(x, wt.seq)
+
+  ### sequenceAA
+  x <- sequenceAA(x)
+
+  ### nbrMutAAs
+  x <- nbrMutAAs(x, wt.seq)
+
+  ### varLengths
+  x <- cbind(x, "varLengths" = as.character(nchar(x\$sequence)))
+
+  ### mutantNameBase
+  x <- mutantNameBase(x, wt.seq)
+
+  ### mutantNameCodon
+  x <- mutantNameCodon(x, wt.seq)
+
+  ### mutantNameBaseHGVS (would ideally look like this: "f:c", "f:c.32_33delinsAC", etc.)
+  x <- cbind(x, "mutantNameBaseHGVS" = x\$mutantNameCodon)
+
+  ### mutantNameAA
+  x <- mutantNameAA(x, wt.seq)
+
+  ### mutantNameAAHGVS (would ideally look like this: "f:p" or "f:p.(Leu11His)")
+  x <- cbind(x, "mutantNameAAHGVS" = x\$mutantNameAA)
+
+  ### mutationTypes: silent, stop, nonsynonymous
+  x <- mutationTypes(x)
+
+  ### mutantName
+  x <- mutantName(x, wt.seq)
+
+  ### re-order columns
+  x <- x[,c(1:7,9:16,8)]
+
+  ### output
+  return(x)
+}
+
+## --- Main function ---
+
+#' Run mutscan fitness estimation with configurable I/O paths
+#'
+#' @param counts_path Path to counts_merged.tsv
+#' @param design_path Path to experimentalDesign.tsv
+#' @param wt_seq_path Path to synonymous_wt.txt (single line DNA sequence)
+#' @param output_path Path to write fitness_estimation.tsv
+#'
+#' @return Invisibly returns the final data.frame; writes the output to output_path.
+run_mutscan_fitness_estimation <- function(counts_path,
+                                           design_path,
+                                           wt_seq_path,
+                                           output_path_edgeR,
+                                           output_path_limma){
+
+  ## 1. Import key files ##
+  #########################
+
+  merged.counts <- read.table(counts_path, sep = "\t", header = T, check.names = F)
+  exp.design <- read.table(design_path, sep = "\t", header = T, check.names = F)
+  wt.seq <- DNAString(as.character(read.table(wt_seq_path)))
+
+  ## 2. Variant count matrix reformatting ##
+  ##########################################
+
+  var.tables <- vector(mode = "list", length = nrow(exp.design))
+  names(var.tables) <- exp.design[,"sample_name"]
+  var.tables <- lapply(var.tables, function(x){x <- vector(mode = "list", length = 4);
+  names(x) <- c("summaryTable", "filterSummary", "errorStatistics", "parameters");
+  return(x)})
+
+  # mutscan 'summaryTable'
+  for(i in 1:length(var.tables)){
+    print(i)
+    var.tables[[i]]\$summaryTable <- mutscan.summaryTable.from.counts(merged.counts, names(var.tables)[i], wt.seq)
+  }
+
+  # mutscan 'filterSummary' (fill with minimal decoy)
+  var.tables <- lapply(var.tables, function(x){x\$filterSummary <- data.frame(NA); return(x)})
+
+  # mutscan 'errorStatistics' (fill with minimal decoy)
+  var.tables <- lapply(var.tables, function(x){x\$errorStatistics <- NA; return(x)})
+
+  # mutscan 'parameters' (fill with minimal decoy)
+  var.tables <- lapply(var.tables, function(x){x\$parameters\$mutNameDelimiter <- '.'; return(x)})
+
+  ## 3. summarizeExperiment object ##
+  ###################################
+
+  # mutscan 'coldata' object (from experimental design TSV file)
+  condition <- exp.design\$selection_id
+  condition[which(condition == '0')] <- "input"
+  condition[which(condition == '1')] <- "output"
+  coldata <- as.data.frame(cbind("Name" = exp.design\$sample_name,
+                                 "Condition" = condition,
+                                 "Replicate" = exp.design\$experiment_replicate))
+  class(coldata\$Replicate) <- "integer"
+
+  # mutscan 'summarizeExperiment' object
+  se <- summarizeExperiment(x = var.tables,
+                            coldata = coldata,
+                            countType = "reads")
+
+  ## 4. logFC calculations ##
+  ###########################
+
+  # mutscan 'model.matrix' object to calculate logFC values
+  model.design <- model.matrix(~ Replicate + Condition, data = se@colData)
+
+  # edgeR
+  logFC.edgeR <- calculateRelativeFC(se = se,
+                                     design = model.design,
+                                     coef = "Conditionoutput",
+                                     WTrows = "WT",
+                                     selAssay = "counts",
+                                     pseudocount = 1,
+                                     method = "edgeR")
+
+  # limma
+  logFC.limma <- calculateRelativeFC(se = se,
+                                     design = model.design,
+                                     coef = "Conditionoutput",
+                                     WTrows = "WT",
+                                     selAssay = "counts",
+                                     pseudocount = 1,
+                                     method = "limma")
+
+  ## 5. QC plots ##
+  #################
+
+  # raw counts comparison
+  pdf('mutscan_counts_corr.pdf', height = 9, width = 14)
+  print(plotPairs(se, selAssay = "counts", addIdentityLine = TRUE))
+  dev.off()
+
+  # edgeR volcano plot
+  pdf('mutscan_edgeR_volcano.pdf', height = 9, width = 14)
+  print(plotVolcano(logFC.edgeR, pointSize = "large"))
+  dev.off()
+
+  # limma volcano plot
+  pdf('mutscan_limma_volcano.pdf', height = 9, width = 14)
+  print(plotVolcano(logFC.limma, pointSize = "large"))
+  dev.off()
+
+  ## 6. Data export ##
+  ####################
+
+  write.table(logFC.edgeR, output_path_edgeR,
+              col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t", na = "")
+  write.table(logFC.limma, output_path_limma,
+              col.names = TRUE, row.names = FALSE, quote = FALSE, sep = "\t", na = "")
+
+  invisible(logFC.edgeR)
+  invisible(logFC.limma)
+
+}
+
+#####
+# run function
+#####
+run_mutscan_fitness_estimation(
+  counts_path = "$counts_merged",
+  design_path = "$exp_design",
+  wt_seq_path = "$syn_wt_txt",
+  output_path_edgeR = "fitness_estimation_mutscan_edgeR.tsv",
+  output_path_limma = "fitness_estimation_mutscan_limma.tsv"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+mutscan_version <- as.character(packageVersion("mutscan"))
+Biostrings_version <- as.character(packageVersion("Biostrings"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(mutscan_version) == 0) mutscan_version <- "unknown"
+if (length(Biostrings_version) == 0) Biostrings_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-mutscan:', mutscan_version),
+    paste('    r-Biostrings:', Biostrings_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/structure/variant_effect_inspection_tool/environment.yml b/modules/local/structure/variant_effect_inspection_tool/environment.yml
new file mode 100644
index 0000000..a72f7ec
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::python=3.12.3
+  - conda-forge::pandas=2.2.1
+  - conda-forge::numpy=1.26.4
diff --git a/modules/local/structure/variant_effect_inspection_tool/main.nf b/modules/local/structure/variant_effect_inspection_tool/main.nf
new file mode 100644
index 0000000..8935225
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/main.nf
@@ -0,0 +1,42 @@
+// Build the single, self-contained interactive variant effect inspection tool (HTML).
+// Aggregates per-residue metrics (fitness, pLDDT accuracy, coverage, counts, counts/cov, error bias),
+// aligns the wildtype sequence onto the structure, and inlines 3Dmol.js + PDB + data into
+// one portable HTML file (plus a structure_data.json sidecar).
+process VARIANT_EFFECT_INSPECTION_TOOL {
+    tag "${meta.sample ?: meta.id}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://depot.galaxyproject.org/singularity/pandas:2.2.1'
+        : 'quay.io/biocontainers/pandas:2.2.1' }"
+
+    input:
+    tuple val(meta), path(pdb), path(fitness_tsv)
+    path(variant_counts, stageAs: 'vc*/*')   // per-sample files share a basename -> stage in numbered dirs
+    path aa_seq
+    path threedmol_js
+    path template_html
+
+    output:
+    tuple val(meta), path("variant_effect_inspection_tool.html"), emit: viewer
+    tuple val(meta), path("structure_data.json")                , emit: data
+    path "versions.yml"                                         , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'build_variant_effect_inspection_tool.py'
+
+    stub:
+    """
+    touch variant_effect_inspection_tool.html structure_data.json
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        python: "0.0.0"
+        pandas: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/structure/variant_effect_inspection_tool/meta.yml b/modules/local/structure/variant_effect_inspection_tool/meta.yml
new file mode 100644
index 0000000..5582e00
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/meta.yml
@@ -0,0 +1,75 @@
+name: "variant_effect_inspection_tool"
+description: Build a single, self-contained interactive variant effect inspection tool (HTML) that projects per-residue fitness, counts, coverage and error-correction bias onto the wildtype 3D structure using 3Dmol.js.
+keywords:
+  - deep mutational scanning
+  - variant effect
+  - 3D structure
+  - visualisation
+tools:
+  - "3Dmol.js":
+      description: "Object-oriented, WebGL-based JavaScript library for molecular visualisation."
+      homepage: "https://3dmol.csb.pitt.edu/"
+      documentation: "https://3dmol.csb.pitt.edu/doc/"
+      licence: ["BSD-3-Clause"]
+  - "pandas":
+      description: "Fast, powerful data structures for data analysis in Python."
+      homepage: "https://pandas.pydata.org/"
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1' ]`
+    - pdb:
+        type: file
+        description: Wildtype 3D structure (PDB); per-residue confidence read from the B-factor column.
+        pattern: "*.pdb"
+    - fitness_tsv:
+        type: file
+        description: Per-sample default fitness estimation table.
+        pattern: "*.tsv"
+  - - variant_counts:
+        type: file
+        description: Per-sample (error-corrected) library-filtered variant count tables.
+        pattern: "*.csv"
+  - - aa_seq:
+        type: file
+        description: Reference amino-acid sequence (one line).
+  - - threedmol_js:
+        type: file
+        description: Bundled 3Dmol.js library (inlined into the HTML).
+        pattern: "*.js"
+  - - template_html:
+        type: file
+        description: HTML template into which the structure, data and 3Dmol.js are injected.
+        pattern: "*.html"
+
+output:
+  - viewer:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - variant_effect_inspection_tool.html:
+          type: file
+          description: Self-contained interactive variant effect inspection tool.
+          pattern: "variant_effect_inspection_tool.html"
+  - data:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - structure_data.json:
+          type: file
+          description: Sidecar with the per-residue/per-variant data projected onto the structure.
+          pattern: "structure_data.json"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
diff --git a/modules/local/structure/variant_effect_inspection_tool/templates/build_variant_effect_inspection_tool.py b/modules/local/structure/variant_effect_inspection_tool/templates/build_variant_effect_inspection_tool.py
new file mode 100755
index 0000000..227a56e
--- /dev/null
+++ b/modules/local/structure/variant_effect_inspection_tool/templates/build_variant_effect_inspection_tool.py
@@ -0,0 +1,334 @@
+#!/usr/bin/env python3
+"""
+Build a single self-contained, interactive variant effect inspection tool (HTML) for nf-core/deepmutscan.
+
+Nextflow ``template`` script for the VARIANT_EFFECT_INSPECTION_TOOL module (staged from this module's
+``templates/`` folder). Reads the wildtype structure (PDB), the fitness estimation table and the
+per-sample variant-count tables, aggregates per-residue/per-variant metrics, and writes one portable
+HTML file with 3Dmol.js, the PDB coordinates and all data inlined (no external/CDN/sidecar
+dependency), plus a structure_data.json sidecar and versions.yml.
+
+Note: this is a Nextflow template, so input paths / the sample name are filled in as Nextflow
+variables and every backslash meant for Python is written doubled (as in the pipeline's R templates).
+"""
+import json
+import platform
+import re
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+THREE_TO_ONE = {
+    "ALA": "A", "ARG": "R", "ASN": "N", "ASP": "D", "CYS": "C", "GLN": "Q", "GLU": "E",
+    "GLY": "G", "HIS": "H", "ILE": "I", "LEU": "L", "LYS": "K", "MET": "M", "PHE": "F",
+    "PRO": "P", "SER": "S", "THR": "T", "TRP": "W", "TYR": "Y", "VAL": "V",
+}
+AA_ORDER = list("ACDEFGHIKLMNPQRSTVWY")
+MUT_RE = re.compile(r"^([A-Z])(\\d+)([A-Z*])\$")
+
+
+def parse_pdb(pdb_path):
+    """Return (pdb_text, residues) where residues = [{resnum, aa, plddt}] from CA atoms."""
+    text = Path(pdb_path).read_text()
+    residues = []
+    seen = set()
+    for line in text.splitlines():
+        if not line.startswith("ATOM"):
+            continue
+        atom_name = line[12:16].strip()
+        if atom_name != "CA":
+            continue
+        try:
+            resnum = int(line[22:26])
+            bfac = float(line[60:66])
+        except ValueError:
+            continue
+        if resnum in seen:
+            continue
+        seen.add(resnum)
+        resname = line[17:20].strip().upper()
+        residues.append({"resnum": resnum, "aa": THREE_TO_ONE.get(resname, "X"), "plddt": bfac})
+    residues.sort(key=lambda r: r["resnum"])
+    return text, residues
+
+
+def best_alignment(wt_seq, struct_residues):
+    """Find offset so that wt position i (1-based) maps to struct_residues[off + i - 1].
+
+    Scans all offsets, maximising identity. Returns (offset0, identity_fraction) where the
+    structure index for wt index j (0-based) is off0 + j.
+    """
+    struct_seq = "".join(r["aa"] for r in struct_residues)
+    n, m = len(wt_seq), len(struct_seq)
+    best_off, best_matches = 0, -1
+    for off in range(-n + 1, m):
+        matches = 0
+        for j in range(n):
+            k = off + j
+            if 0 <= k < m and struct_seq[k] == wt_seq[j]:
+                matches += 1
+        if matches > best_matches:
+            best_matches, best_off = matches, off
+    identity = best_matches / max(1, n)
+    return best_off, identity
+
+
+def r6(x):
+    """Round to 6 dp so output is byte-stable regardless of floating-point summation order."""
+    return round(x, 6) if isinstance(x, (int, float)) else x
+
+
+def to_float(x):
+    try:
+        f = float(x)
+        return f if f == f else None  # drop NaN
+    except (TypeError, ValueError):
+        return None
+
+
+REP_RE = re.compile(r"^rescaled_fitness_rep(\\d+)\$", re.I)
+OUT_RE = re.compile(r"^output(\\d+)\$", re.I)
+
+
+def load_fitness(path):
+    """Per-position variant records from the default fitness_estimation.tsv.
+
+    Captures the mean fitness, its SD and every per-replicate (rescaled) fitness, so the viewer can
+    recompute the mean/SD over a user-chosen subset of replicates. That subsetting is exact rather
+    than approximate: the fitness step derives each `rescaled_fitness_rep<N>` from that replicate's
+    own input/output counts and its own synonymous/stop anchors, so a replicate's value never
+    depends on which other replicates were in the run.
+
+    Returns (per_pos, rep_ids). Any number of replicates is handled; missing values are tolerated.
+    """
+    df = pd.read_csv(path, sep="\\t", dtype=str, keep_default_na=False)
+
+    def col(name):
+        return next((c for c in df.columns if c.strip().lower() == name), None)
+
+    def numbered(rx):
+        return sorted((c for c in df.columns if rx.match(c.strip())),
+                      key=lambda c: int(rx.match(c.strip()).group(1)))
+
+    fit_col, sd_col = col("mean fitness"), col("fitness sd")
+    rep_cols = numbered(REP_RE)
+    out_cols = numbered(OUT_RE)
+    rep_ids = [REP_RE.match(c.strip()).group(1) for c in rep_cols]
+
+    per_pos = {}
+    for _, row in df.iterrows():
+        pos = to_float(row.get("pos", ""))
+        wt = (row.get("wt_aa", "") or "").strip()
+        mut = (row.get("mut_aa", "") or "").strip()
+        if pos is None or not wt or not mut:
+            continue
+        pos = int(pos)
+        is_stop = mut == "*" or (row.get("stop", "").strip() not in ("", "0"))
+        per_pos.setdefault(pos, {"wt": wt, "variants": []})["variants"].append({
+            "mut": mut,
+            "fitness": to_float(row.get(fit_col, "")) if fit_col else None,
+            "sd": to_float(row.get(sd_col, "")) if sd_col else None,
+            "reps": [to_float(row.get(c, "")) for c in rep_cols],
+            "n_out": sum((to_float(row.get(c, "")) or 0) for c in out_cols),
+            "syn": mut == wt, "stop": bool(is_stop),
+        })
+    return per_pos, rep_ids
+
+
+def load_counts(paths):
+    """Aggregate coverage / counts / counts_per_cov across all sample CSVs.
+
+    Returns two dicts: per position ``{pos: {...}}`` (used to colour the structure) and per variant
+    ``{(pos, mut): {...}}`` (used for the substitution-specific bee-swarm distributions).
+    """
+    frames = []
+    for p in paths:
+        try:
+            frames.append(pd.read_csv(p))
+        except Exception as exc:  # pragma: no cover - defensive
+            print(f"WARN: could not read {p}: {exc}", file=sys.stderr)
+    if not frames:
+        return {}, {}
+    df = pd.concat(frames, ignore_index=True)
+    # The "A1M"-style WT/pos/mut label lives in the `pos_mut` column
+    # (`aa_mut` holds e.g. "M:A>M"). Fall back to aa_mut only if pos_mut is absent.
+    label_col = "pos_mut" if "pos_mut" in df.columns else ("aa_mut" if "aa_mut" in df.columns else None)
+    if label_col is None:
+        return {}, {}
+    cols = (("coverage", "cov"), ("counts", "counts"), ("counts_per_cov", "counts_per_cov"),
+            ("counts_raw", "counts_raw"))
+    pos_acc, var_acc = {}, {}
+    for _, row in df.iterrows():
+        m = MUT_RE.match(str(row.get(label_col, "")).strip())
+        if not m:
+            continue
+        pos, mut = int(m.group(2)), m.group(3)
+        pa = pos_acc.setdefault(pos, {k: [] for k, _ in cols})
+        va = var_acc.setdefault((pos, mut), {k: [] for k, _ in cols})
+        for key, src in cols:
+            v = to_float(row.get(src, ""))
+            if v is not None:
+                pa[key].append(v)
+                va[key].append(v)
+
+    def meaned(acc):
+        return {k: {kk: (sum(vv) / len(vv) if vv else None) for kk, vv in a.items()}
+                for k, a in acc.items()}
+
+    return meaned(pos_acc), meaned(var_acc)
+
+
+class args:
+    # Filled in by Nextflow when this template is staged and run by the VARIANT_EFFECT_INSPECTION_TOOL process.
+    pdb = "$pdb"
+    fitness = "$fitness_tsv"
+    aa_seq = "$aa_seq"
+    counts = "$variant_counts".split()          # collected per-sample CSVs (staged as vc*/*)
+    threedmol = "$threedmol_js"
+    template = "$template_html"
+    sample = "${meta.sample ?: meta.id}"
+    out_html = "variant_effect_inspection_tool.html"
+    out_json = "structure_data.json"
+
+
+def main():
+
+    wt_seq = Path(args.aa_seq).read_text().strip().replace("\\n", "").replace("\\r", "")
+    pdb_text, struct_residues = parse_pdb(args.pdb)
+    off0, identity = best_alignment(wt_seq, struct_residues)
+    struct_by_index = struct_residues  # 0-based list
+
+    fit_per_pos, rep_ids = load_fitness(args.fitness)
+    # per-position aggregate (structure colour) and per-variant values (substitution-specific swarm)
+    counts_per_pos, counts_per_var = load_counts(sorted(args.counts))  # sorted -> deterministic
+
+    # ---- candidate metrics (dropped automatically when a dataset has no data for them) ----
+    # scope="variant": has a per-substitution value (used in the bee-swarm); the position mean is
+    # still what colours the structure. reverse=True flips the colour ramp (low -> red, high -> blue).
+    METRIC_SPECS = [
+        ("fitness", dict(label="Fitness (nf-core/deepmutscan)", type="diverging", scope="variant",
+                         reverse=True,
+                         desc="Fitness per substitution; the position mean is shown on the structure")),
+        ("fitness_sd", dict(label="Fitness uncertainty (SD)", type="sequential", scope="variant",
+                            desc="Standard deviation of the fitness estimate (lower = more confident)")),
+        ("accuracy", dict(label="Prediction accuracy (pLDDT)", type="plddt", scope="residue", fixed=[0, 100],
+                          desc="Per-residue model confidence from the structure B-factor")),
+        ("coverage", dict(label="Coverage", type="sequential", scope="variant",
+                          desc="Sequencing coverage of this residue (the same for all substitutions at one residue)")),
+        ("counts", dict(label="Counts", type="sequential", scope="variant",
+                        desc="Variant read counts per substitution (position mean shown on the structure)")),
+        ("counts_per_cov", dict(label="Counts / coverage", type="sequential", scope="variant",
+                                desc="Counts normalised by coverage, per substitution (position mean shown on the structure)")),
+        ("n_substitutions", dict(label="Measured substitutions", type="sequential", scope="residue",
+                                 desc="Number of substitutions with a measured fitness at the position")),
+        ("error_bias", dict(label="Positional error bias", type="sequential", scope="residue",
+                            desc="Fraction of counts removed by sequencing-error correction at this position")),
+    ]
+    variant_metrics = {k for k, s in METRIC_SPECS if s["scope"] == "variant"}
+
+    def mean(xs):
+        return (sum(xs) / len(xs)) if xs else None
+
+    residues = []
+    for i, wt_aa in enumerate(wt_seq):  # i is 0-based; position = i + 1
+        pos = i + 1
+        k = off0 + i
+        srec = struct_by_index[k] if 0 <= k < len(struct_by_index) else None
+        variants = fit_per_pos.get(pos, {}).get("variants", [])
+        miss = [v for v in variants if not v["syn"] and not v["stop"]]
+        fit_vals = [v["fitness"] for v in miss if v["fitness"] is not None]
+        sd_vals = [v["sd"] for v in miss if v["sd"] is not None]
+        cp = counts_per_pos.get(pos, {})
+        craw, cnow = cp.get("counts_raw"), cp.get("counts")
+        error_bias = (1.0 - cnow / craw) if (craw not in (None, 0) and cnow is not None) else None
+        res_metrics = {
+            "fitness": r6(mean(fit_vals)),
+            "fitness_sd": r6(mean(sd_vals)),
+            "accuracy": r6(srec["plddt"]) if srec else None,
+            "coverage": r6(cp.get("coverage")),
+            "counts": r6(cp.get("counts")),
+            "counts_per_cov": r6(cp.get("counts_per_cov")),
+            "n_substitutions": (len(fit_vals) or None),
+            "error_bias": r6(error_bias),
+        }
+        vlist = []
+        for v in sorted(variants, key=lambda v: AA_ORDER.index(v["mut"]) if v["mut"] in AA_ORDER else 99):
+            vc = counts_per_var.get((pos, v["mut"]), {})
+            vlist.append({
+                "mut": v["mut"], "syn": v["syn"], "stop": v["stop"], "n_out": r6(v["n_out"]),
+                # per-replicate fitness, so the viewer can re-derive mean/SD for a chosen subset
+                "reps": [r6(x) if x is not None else None for x in v["reps"]],
+                "metrics": {
+                    "fitness": r6(v["fitness"]), "fitness_sd": r6(v["sd"]),
+                    "coverage": r6(vc.get("coverage")), "counts": r6(vc.get("counts")),
+                    "counts_per_cov": r6(vc.get("counts_per_cov")),
+                },
+            })
+        residues.append({
+            "pos": pos, "wt": wt_aa,
+            "resnum": srec["resnum"] if srec else None,
+            "mapped": bool(srec and srec["aa"] == wt_aa),
+            "metrics": res_metrics, "variants": vlist,
+        })
+
+    # keep only metrics that actually have data (generality across diverse datasets)
+    metrics = {}
+    for key, spec in METRIC_SPECS:
+        vals = [r["metrics"].get(key) for r in residues if r["metrics"].get(key) is not None]
+        if not vals:
+            continue
+        if "fixed" in spec:
+            mrange = spec["fixed"]
+        elif spec["type"] == "diverging":
+            a = max(abs(min(vals)), abs(max(vals))) or 1
+            mrange = [r6(-a), r6(a)]
+        else:
+            lo, hi = min(vals), max(vals)
+            mrange = [r6(lo), r6(hi if hi != lo else lo + 1)]
+        metrics[key] = {"label": spec["label"], "type": spec["type"], "scope": spec["scope"],
+                        "range": mrange, "desc": spec["desc"]}
+        if spec.get("reverse"):
+            metrics[key]["reverse"] = True
+
+    present = set(metrics)
+    for r in residues:
+        r["metrics"] = {k: v for k, v in r["metrics"].items() if k in present}
+        for v in r["variants"]:
+            v["metrics"] = {k: val for k, val in v["metrics"].items()
+                            if k in present and k in variant_metrics}
+
+    data = {
+        "sample": args.sample,
+        "sequence": wt_seq,
+        "alignment": {"offset": off0, "identity": round(identity, 4),
+                      "n_residues_structure": len(struct_residues)},
+        "replicates": rep_ids,
+        "metrics": metrics,
+        "residues": residues,
+    }
+
+    Path(args.out_json).write_text(json.dumps(data, indent=2))
+
+    threedmol_js = Path(args.threedmol).read_text()
+    template = Path(args.template).read_text()
+    html = (template
+            .replace("/*__THREEDMOL_JS__*/", threedmol_js)
+            .replace('"__PDB_TEXT__"', json.dumps(pdb_text))
+            .replace("/*__DATA_JSON__*/", json.dumps(data))
+            .replace("__SAMPLE__", args.sample))
+    Path(args.out_html).write_text(html)
+
+    Path("versions.yml").write_text(
+        '"${task.process}":\\n'
+        + "    python: " + platform.python_version() + "\\n"
+        + "    pandas: " + pd.__version__ + "\\n"
+        + "    numpy: " + np.__version__ + "\\n"
+    )
+    print(f"Wrote {args.out_html} (alignment offset={off0}, identity={identity:.2%}, "
+          f"{len(residues)} residues)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/modules/local/variantcounting/environment.yml b/modules/local/variantcounting/environment.yml
new file mode 100644
index 0000000..9397a3f
--- /dev/null
+++ b/modules/local/variantcounting/environment.yml
@@ -0,0 +1,14 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::python=3.12.13
+  - bioconda::pysam=0.24.0
+  - conda-forge::pyarrow=24.0.0
+  - conda-forge::numpy=2.5.1
+  - conda-forge::biopython=1.87
+  # polars-lts-cpu (no AVX2 requirement) so the amd64 image also runs under
+  # emulation on Apple Silicon; the mutscan container is amd64-only.
+  - pip
+  - pip:
+      - polars-lts-cpu==1.33.1
diff --git a/modules/local/variantcounting/main.nf b/modules/local/variantcounting/main.nf
new file mode 100644
index 0000000..5ee7eec
--- /dev/null
+++ b/modules/local/variantcounting/main.nf
@@ -0,0 +1,40 @@
+process VARIANTCOUNTING {
+    tag "$meta.id"
+    label 'process_high'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/a6/a66af652f7b1c6d9dab9f081f5676bd9452d653eed43034f20a2cf172921a4cf/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_pysam_biopython_pruned:fb9a2095922ddd59' }"
+
+    input:
+    tuple val(meta), path(bam), path(bai)
+    path fasta
+    val reading_frame
+    val min_counts
+    val base_qual
+    val min_flank
+
+    output:
+    tuple val(meta), path("*.variant_counts.tsv"), emit: variant_counts
+    path "variant_counts_columns.tsv"            , emit: columns
+    path "versions.yml"                          , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'count_variants.py'
+
+    stub:
+    """
+    touch ${meta.id}.variant_counts.tsv
+    touch variant_counts_columns.tsv
+
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        variantcounting: stub
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/variantcounting/meta.yml b/modules/local/variantcounting/meta.yml
new file mode 100644
index 0000000..5669b02
--- /dev/null
+++ b/modules/local/variantcounting/meta.yml
@@ -0,0 +1,74 @@
+name: "variantcounting"
+description: Count single- and multi-nucleotide variants per read against a reference ORF, producing a GATK-AnalyzeSaturationMutagenesis-compatible variant count table.
+keywords:
+  - deep mutational scanning
+  - variant calling
+  - saturation mutagenesis
+  - counting
+tools:
+  - pysam:
+      description: Python interface for reading/manipulating SAM/BAM alignment files (htslib).
+      homepage: https://pysam.readthedocs.io
+      licence: ["MIT"]
+  - polars:
+      description: Fast DataFrame library.
+      homepage: https://pola.rs
+      licence: ["MIT"]
+  - biopython:
+      description: Tools for biological computation (sequence translation).
+      homepage: https://biopython.org
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: Groovy Map containing sample information, e.g. `[ id:'sample1' ]`
+    - bam:
+        type: file
+        description: Coordinate-sorted, indexed BAM of premerged reads aligned to the reference.
+        pattern: "*.bam"
+    - bai:
+        type: file
+        description: BAM index (staged alongside the BAM).
+        pattern: "*.{bai,csi}"
+  - - fasta:
+        type: file
+        description: Reference FASTA containing the ORF.
+        pattern: "*.{fa,fasta}"
+  - - reading_frame:
+        type: string
+        description: ORF range as 1-based inclusive `start-stop` (GATK `--orf` convention), e.g. `352-1383`.
+  - - min_counts:
+        type: integer
+        description: Minimum number of observations for a variant to be reported (GATK `--min-variant-obs`).
+  - - base_qual:
+        type: integer
+        description: Minimum base quality for a mutation to be counted.
+  - - min_flank:
+        type: integer
+        description: Minimum distance (nt) of a mutation/covered base from a read edge; 0 disables edge trimming.
+
+output:
+  - variant_counts:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.variant_counts.tsv":
+          type: file
+          description: Tab-separated variant count table (no header), GATK-compatible 9 columns.
+          pattern: "*.variant_counts.tsv"
+  - columns:
+      - variant_counts_columns.tsv:
+          type: file
+          description: Concise explanation of each output column.
+          pattern: "variant_counts_columns.tsv"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
diff --git a/modules/local/variantcounting/templates/count_variants.py b/modules/local/variantcounting/templates/count_variants.py
new file mode 100644
index 0000000..73e460b
--- /dev/null
+++ b/modules/local/variantcounting/templates/count_variants.py
@@ -0,0 +1,522 @@
+#!/usr/bin/env python
+
+# Nextflow template: values below are interpolated by Nextflow. The whole file is compiled
+# as one Groovy GString, so every backslash must be doubled and no triple-quoted strings
+# are allowed anywhere in this file, including comments.
+
+#-- import modules --#
+import os
+import sys
+import re
+import gc
+import platform
+import pysam
+import numpy as np
+import pyarrow as pa
+import polars as pl
+import Bio
+from Bio import SeqIO
+from Bio.Seq import Seq
+from datetime import datetime
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from collections import defaultdict
+
+#-- functions --#
+def init_bam(bam_path):
+    global bam_file_cov
+    bam_file_cov = pysam.AlignmentFile(bam_path, "rb")
+
+def get_base_cov(chrom, start, end, qual, min_flank):
+    if min_flank and min_flank > 0:
+        cov = defaultdict(int)
+        for read in bam_file_cov.fetch(chrom, start, end):
+            if read.is_unmapped:
+                continue
+            quals = read.query_qualities
+            qlen = read.query_length
+            if quals is None or qlen is None:
+                continue
+            lo = min_flank
+            hi = qlen - min_flank
+            for qpos, refpos in read.get_aligned_pairs(matches_only=True):
+                if refpos is None or qpos is None:
+                    continue
+                if refpos < start or refpos >= end:
+                    continue
+                if qpos < lo or qpos >= hi:
+                    continue
+                if quals[qpos] < qual:
+                    continue
+                cov[refpos] += 1
+        return dict(cov)
+
+    A, C, G, T = bam_file_cov.count_coverage(chrom, start, end, quality_threshold = qual)
+    coverage_array = np.array(A, dtype=np.uint32) + \\
+                     np.array(C, dtype=np.uint32) + \\
+                     np.array(G, dtype=np.uint32) + \\
+                     np.array(T, dtype=np.uint32)
+
+    # key on 0-based ref position to match ref_pos lookups in gatk_formating
+    return {start + pos: int(cov) for pos, cov in enumerate(coverage_array)}
+
+def chunk_ranges(start, end, sub_region_size):
+    for s in range(start, end, sub_region_size):
+        yield s, min(s + sub_region_size, end)
+
+def get_base_cov_in_chunk(bam_path, chrom, start, end, qual, sub_region_size, threads, min_flank):
+    chunks = list(chunk_ranges(start, end, sub_region_size))
+    dicts = []
+
+    with ProcessPoolExecutor(max_workers = threads, initializer = init_bam, initargs = (bam_path,)) as executor:
+        futures = [
+            executor.submit(get_base_cov, chrom, s, e, qual, min_flank)
+            for s, e in chunks
+        ]
+
+        for f in as_completed(futures):
+            dicts.append(f.result())
+
+    merged_dict = {}
+    for d in dicts:
+        merged_dict.update(d)
+
+    return merged_dict
+
+def init_worker():
+    global global_dict_base_cov
+    global_dict_base_cov = dict_base_cov
+
+def extract_read_info(read):
+    read_seq = read.query_sequence
+    read_qual = read.query_qualities
+
+    return {
+        'ref':   read.reference_name,
+        'pos':   read.reference_start,
+        'seq':   read_seq,
+        'qual':  read_qual,
+        'md':    read.get_tag('MD'),
+        'cigar': read.cigartuples
+    }
+
+def parse_md(read):
+    read_pos = read.get('pos')
+    read_md = read.get('md')
+    md_pattern = re.finditer(r'(\\d+)|([A-Z]+)|(\\^[A-Z]+)', read_md)
+
+    base_ref_pos = read_pos
+
+    variants = []
+    for match in md_pattern:
+        if match.group(1):
+            num_matches = int(match.group(1))
+            base_ref_pos += num_matches
+        elif match.group(2):
+            for base in match.group(2):
+                variants.append(('X', base_ref_pos, base))
+                base_ref_pos += 1
+        elif match.group(3):
+            deleted_bases = match.group(3)[1:]
+            for base in deleted_bases:
+                variants.append(('D', base_ref_pos, base))
+                base_ref_pos += 1
+
+    return variants
+
+def gatk_formating(variants):
+    if not variants:
+        return 0, 0, "", 0, "", "", ""
+
+    varying_bases = 0
+    varying_codons = 0
+    base_mut = ""
+    codon_mut = ""
+    aa_mut = ""
+    pos_mut = ""
+
+    base_pos = [ v['ref_pos'] for v in variants if v['var_type'] == 'X' ]
+    base_cov = [ global_dict_base_cov.get(pos, 0) for pos in base_pos ]
+    base_cov_avg = int(np.mean(base_cov)) if base_cov else 0
+
+    base_variants = [ v for v in variants if v['var_type'] == 'X' ]
+    varying_bases = len(base_variants)
+
+    # base_mut positions are 1-based to match GATK AnalyzeSaturationMutagenesis
+    base_mut = ", ".join(
+        f"{v['ref_pos'] + 1}:{v['ref_base']}>{v['alt_base']}"
+        for v in base_variants
+    )
+
+    codon_changes = defaultdict(list)
+    for v in base_variants:
+        codon_idx = ((v['ref_pos'] - orf_start) // 3) + 1
+        codon_changes[codon_idx].append(v)
+
+    varying_codons = len(codon_changes)
+
+    codon_mut_list = []
+    aa_mut_list = []
+    pos_mut_list = []
+
+    for codon_idx in sorted(codon_changes):
+        vars_in_codon = codon_changes[codon_idx]
+        ref_codon = str(codon_dict[chrom][codon_idx])
+        codon_list = list(ref_codon)
+
+        for v in vars_in_codon:
+            pos_in_codon = (v['ref_pos'] - orf_start) % 3
+            codon_list[pos_in_codon] = v['alt_base']
+
+        alt_codon = "".join(codon_list)
+        codon_mut_list.append(f"{codon_idx}:{ref_codon}>{alt_codon}")
+
+        ref_aa = str(Seq(ref_codon).translate())
+        alt_aa = str(Seq(alt_codon).translate())
+
+        if alt_aa == ref_aa:
+            mut_type = "S"
+        elif alt_aa == "*":
+            mut_type = "N"
+        else:
+            mut_type = "M"
+
+        # GATK writes the stop codon as X, not *
+        ref_aa_s = "X" if ref_aa == "*" else ref_aa
+        alt_aa_s = "X" if alt_aa == "*" else alt_aa
+
+        aa_mut_list.append(f"{mut_type}:{ref_aa_s}>{alt_aa_s}")
+        pos_mut_list.append(f"{ref_aa_s}{codon_idx}{alt_aa_s}")
+
+    codon_mut = ", ".join(codon_mut_list)
+    aa_mut = ", ".join(aa_mut_list)
+    pos_mut = ";".join(pos_mut_list)
+
+    return base_cov_avg, varying_bases, base_mut, varying_codons, codon_mut, aa_mut, pos_mut
+
+def parse_read(read, orf_start, orf_end, base_qual, min_flank):
+    read_ref = read.get('ref')
+    read_pos = read.get('pos')
+    read_seq = read.get('seq')
+    read_qual = read.get('qual')
+    read_cigar = read.get('cigar')
+    read_md_variants = parse_md(read)
+
+    if not read_md_variants:
+        return []
+
+    qlen = len(read_seq)
+    base_ref_pos = read_pos
+    base_read_pos = 0
+    md_index = 0
+    variants = []
+    for op, op_len in read_cigar:
+        if op == 0: # M, match
+            for i in range(op_len):
+                if md_index < len(read_md_variants) and read_md_variants[md_index][1] == base_ref_pos:
+                    var_type, _, ref_base = read_md_variants[md_index]
+                    alt_base = read_seq[base_read_pos]
+                    alt_qual = read_qual[base_read_pos]
+                    alt_idx = (base_ref_pos - orf_start) % 3 + 1
+                    codon_idx = ((base_ref_pos - orf_start) // 3) + 1
+                    variants.append({ 'var_type':  var_type,
+                                      'ref_name':  read_ref,
+                                      'ref_pos':   base_ref_pos,
+                                      'ref_base':  ref_base,
+                                      'alt_base':  alt_base,
+                                      'alt_qual':  alt_qual,
+                                      'alt_idx' :  alt_idx,
+                                      'read_idx':  base_read_pos,
+                                      'codon_idx': codon_idx })
+                    md_index += 1
+                base_ref_pos += 1
+                base_read_pos += 1
+        elif op == 1: # I, insertion (indel-containing reads are dropped upstream in bam_filter)
+            base_read_pos += op_len
+        elif op == 2: # D, deletion
+            for i in range(op_len):
+                if md_index < len(read_md_variants) and read_md_variants[md_index][1] == base_ref_pos:
+                    var_type, _, ref_base = read_md_variants[md_index]
+                    alt_idx = (base_ref_pos - orf_start) % 3 + 1
+                    codon_idx = ((base_ref_pos - orf_start) // 3) + 1
+                    variants.append({ 'var_type':  'D',
+                                      'ref_name':  read_ref,
+                                      'ref_pos':   base_ref_pos,
+                                      'ref_base':  ref_base,
+                                      'alt_base':  '-',
+                                      'alt_qual':  99,
+                                      'alt_idx' :  alt_idx,
+                                      'read_idx':  base_read_pos,
+                                      'codon_idx': codon_idx })
+                    md_index += 1
+                base_ref_pos += 1
+        elif op == 3: # N, skip
+            base_ref_pos += op_len
+        elif op == 4: # S, softclip
+            base_read_pos += op_len
+        elif op == 5: # H, hardclip
+            continue
+
+    if not variants:
+        return []
+
+    variants_filtered = []
+    for var in variants:
+        if var['ref_pos'] >= orf_start and var['ref_pos'] <= orf_end:
+            if var['alt_qual'] >= base_qual:
+                # read-edge (min-flanking-length) filter
+                if min_flank > 0:
+                    read_idx = var['read_idx']
+                    if read_idx < min_flank or read_idx >= qlen - min_flank:
+                        continue
+                codons_tmp = codon_dict[var['ref_name']]
+                ref_codon = codons_tmp[var['codon_idx']]
+                variants_filtered.append({ 'var_type':  var['var_type'],
+                                           'ref_name':  var['ref_name'],
+                                           'ref_pos':   var['ref_pos'],
+                                           'ref_base':  var['ref_base'],
+                                           'alt_base':  var['alt_base'],
+                                           'alt_qual':  var['alt_qual'],
+                                           'alt_idx' :  var['alt_idx'],
+                                           'codon_idx': var['codon_idx'],
+                                           'ref_codon': ref_codon })
+
+    return gatk_formating(variants_filtered)
+
+def batch_parse_reads(batch_reads, orf_start, orf_end, base_qual, min_flank):
+    cols = {
+        "base_cov_avg": [],
+        "varying_bases": [],
+        "base_mut": [],
+        "varying_codons": [],
+        "codon_mut": [],
+        "aa_mut": [],
+        "pos_mut": []
+    }
+
+    for read in batch_reads:
+        result = parse_read(read, orf_start, orf_end, base_qual, min_flank)
+
+        if not result or result[2] == "":
+            continue
+
+        cols["base_cov_avg"].append(result[0])
+        cols["varying_bases"].append(result[1])
+        cols["base_mut"].append(result[2])
+        cols["varying_codons"].append(result[3])
+        cols["codon_mut"].append(result[4])
+        cols["aa_mut"].append(result[5])
+        cols["pos_mut"].append(result[6])
+
+    if not cols["base_mut"]:
+        return None
+
+    return pa.table(cols)
+
+def function_for_processpool(args_tuple):
+    return batch_parse_reads(*args_tuple)
+
+def empty_yield_df():
+    return pl.DataFrame([], schema={
+        "base_cov_avg": pl.Int64,
+        "varying_bases": pl.Int64,
+        "base_mut": pl.Utf8,
+        "varying_codons": pl.Int64,
+        "codon_mut": pl.Utf8,
+        "aa_mut": pl.Utf8,
+        "pos_mut": pl.Utf8,
+        "counts": pl.Int64
+    }, orient = "row")
+
+def build_yield_df(results):
+    if results:
+        df_yield = pl.from_arrow(pa.concat_tables(results))
+        df_yield = df_yield.filter(pl.col("base_mut") != "")
+        df_yield = df_yield.with_columns(pl.lit(1).alias("counts"))
+        return df_yield
+    return empty_yield_df()
+
+def read_bam_in_chunk(bam_path, orf_start, orf_end, base_qual, min_flank, chunk_size, threads):
+    with pysam.AlignmentFile(bam_path, "rb", threads = threads) as bam_file, \\
+        ProcessPoolExecutor(max_workers = threads, initializer = init_worker) as executor:
+
+        batch_size = min(chunk_size, 5000)
+
+        read_chunk = []
+        for read in bam_file.fetch(until_eof=True):
+            if read.is_unmapped or not read.has_tag('MD'):
+                continue
+
+            # perfectly-aligned (wildtype) reads carry no variants
+            if re.fullmatch(r'\\d+', read.get_tag('MD')):
+                continue
+
+            read_chunk.append(extract_read_info(read))
+
+            if len(read_chunk) >= chunk_size:
+                read_batches = [
+                    read_chunk[i:i + batch_size]
+                    for i in range(0, len(read_chunk), batch_size)
+                ]
+
+                futures = [
+                    executor.submit(function_for_processpool, (batch, orf_start, orf_end, base_qual, min_flank))
+                    for batch in read_batches
+                ]
+
+                results = []
+                for f in as_completed(futures):
+                    batch_result = f.result()
+                    if batch_result:
+                        results.append(batch_result)
+
+                df_yield = build_yield_df(results)
+                read_chunk = []
+                del read_batches, futures, results
+                gc.collect()
+
+                yield df_yield
+
+        if read_chunk:
+            read_batches = [
+                read_chunk[i:i + batch_size]
+                for i in range(0, len(read_chunk), batch_size)
+            ]
+
+            futures = [
+                executor.submit(function_for_processpool, (batch, orf_start, orf_end, base_qual, min_flank))
+                for batch in read_batches
+            ]
+
+            results = []
+            for f in as_completed(futures):
+                batch_result = f.result()
+                if batch_result:
+                    results.append(batch_result)
+
+            df_yield = build_yield_df(results)
+            del read_chunk, read_batches, futures, results
+            gc.collect()
+
+            yield df_yield
+
+def write_column_sheet(path):
+    rows = [
+        ("column", "description"),
+        ("counts", "Number of reads supporting this variant (observations)"),
+        ("cov", "Mean sequencing coverage across the variant's mutated base positions"),
+        ("mean_length_variant_reads", "Placeholder (0); mean read length of supporting reads is not computed"),
+        ("varying_bases", "Number of nucleotide substitutions in the variant"),
+        ("base_mut", "Nucleotide change(s), 1-based reference position(s), e.g. 352:G>A"),
+        ("varying_codons", "Number of codons altered by the variant"),
+        ("codon_mut", "Codon change(s), 1-based codon index, e.g. 1:GCT>ACT"),
+        ("aa_mut", "Amino-acid change per codon; type S=synonymous, M=missense, N=nonsense; stop=X, e.g. M:A>T"),
+        ("pos_mut", "Compact per-codon amino-acid change, e.g. A1T (stop written as X)"),
+    ]
+    with open(path, "w") as fh:
+        for name, desc in rows:
+            fh.write(name + "\\t" + desc + "\\n")
+
+#-- Nextflow-interpolated arguments --#
+class args:
+    input_bam  = "$bam"
+    reference  = "$fasta"
+    orf_range  = "$reading_frame"          # 1-based inclusive (GATK --orf convention)
+    min_counts = int("$min_counts")
+    base_qual  = int("$base_qual")
+    min_flank  = int("$min_flank")
+    threads    = max(1, int("$task.cpus"))
+    chunk_size = 1000
+    out_file   = "${meta.id}.variant_counts.tsv"
+
+#-- main execution --#
+if __name__ == "__main__":
+    write_column_sheet("variant_counts_columns.tsv")
+
+    # orf_range is 1-based inclusive (GATK --orf convention); convert to 0-based
+    orf_start_1, orf_end_1 = map(int, args.orf_range.split('-'))
+    orf_start = orf_start_1 - 1
+    orf_end   = orf_end_1 - 1
+
+    ref_dict = SeqIO.to_dict(SeqIO.parse(args.reference, "fasta"))
+
+    orf_dict = {}
+    codon_dict = {}
+
+    if os.path.exists(args.out_file):
+        os.remove(args.out_file)
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Get the reference codons, please wait...", flush = True)
+    for chrom, record in ref_dict.items():
+        orf_seq = record.seq[orf_start : orf_end + 1]
+        orf_dict[chrom] = orf_seq
+        ref_codons = {}
+        for i in range(0, len(orf_seq) - 2, 3):
+            codon_idx = i // 3 + 1 # 1-based codon index
+            ref_codon = orf_seq[i:i + 3]
+            if len(ref_codon) == 3:
+                ref_codons[codon_idx] = ref_codon
+        codon_dict[chrom] = ref_codons
+
+    del ref_dict
+    gc.collect()
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Get the base coverage, please wait...", flush = True)
+    sub_region_size = max(1, (orf_end - orf_start + 1) // args.threads)
+    dict_base_cov = get_base_cov_in_chunk(args.input_bam, chrom, orf_start, orf_end + 1, args.base_qual, sub_region_size, args.threads, args.min_flank)
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Extracting read information, please wait...", flush = True)
+    list_results = []
+    for i, chunk_result in enumerate(read_bam_in_chunk(args.input_bam, orf_start, orf_end, args.base_qual, args.min_flank, args.chunk_size, args.threads)):
+        print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} |--> Processed chunk {i+1}", flush = True)
+        list_results.append(chunk_result)
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} |--> Finished.", flush = True)
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Creating the variant matrix, please wait...", flush = True)
+    list_results_filtered = [df for df in list_results if df.height > 0]
+
+    if list_results_filtered:
+        df_variants = pl.concat(list_results_filtered, how = "vertical")
+        df_variants = df_variants.filter(pl.col("base_mut") != "")
+        df_variants_counts = ( df_variants.group_by("base_mut")
+                                          .agg([pl.col("counts").sum().alias("counts"),
+                                                pl.all().exclude(["base_mut", "counts"]).first()]) )
+        del list_results, list_results_filtered, df_variants
+        gc.collect()
+    else:
+        df_variants_counts = pl.DataFrame([], schema={
+            "base_mut": pl.Utf8,
+            "counts": pl.Int64,
+            "base_cov_avg": pl.Int64,
+            "varying_bases": pl.Int64,
+            "varying_codons": pl.Int64,
+            "codon_mut": pl.Utf8,
+            "aa_mut": pl.Utf8,
+            "pos_mut": pl.Utf8
+        }, orient = "row")
+
+    # matches GATK --min-variant-obs
+    df_variants_counts = df_variants_counts.filter(pl.col("counts") >= args.min_counts)
+
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Creating the output file, please wait...", flush = True)
+    df_variants_counts = df_variants_counts.select([pl.col("counts"),
+                                                    pl.col("base_cov_avg").alias("cov"),
+                                                    pl.lit(0).alias("mean_length_variant_reads"),
+                                                    pl.col("varying_bases"),
+                                                    pl.col("base_mut"),
+                                                    pl.col("varying_codons"),
+                                                    pl.col("codon_mut"),
+                                                    pl.col("aa_mut"),
+                                                    pl.col("pos_mut")])
+    # GATK variantCounts has no header line
+    df_variants_counts.write_csv(args.out_file, separator = "\\t", include_header = False)
+    print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Done.", flush = True)
+
+    #-- versions --#
+    with open("versions.yml", "w") as vf:
+        vf.write('"${task.process}":\\n')
+        vf.write("    python: " + platform.python_version() + "\\n")
+        vf.write("    pysam: " + pysam.__version__ + "\\n")
+        vf.write("    polars: " + pl.__version__ + "\\n")
+        vf.write("    pyarrow: " + pa.__version__ + "\\n")
+        vf.write("    numpy: " + np.__version__ + "\\n")
+        vf.write("    biopython: " + Bio.__version__ + "\\n")
diff --git a/modules/local/visualization/counts_heatmap/environment.yml b/modules/local/visualization/counts_heatmap/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/counts_heatmap/main.nf b/modules/local/visualization/counts_heatmap/main.nf
new file mode 100644
index 0000000..4ef05dd
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/main.nf
@@ -0,0 +1,31 @@
+process VISUALIZATION_COUNTS_HEATMAP {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(variantCounts_for_heatmaps)
+    val min_counts
+
+    output:
+    tuple val(meta), path("counts_heatmap.pdf"), emit: counts_heatmap
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'counts_heatmap.R'
+
+    stub:
+    """
+    touch counts_heatmap.pdf
+    echo "VISUALIZATION_COUNTS_HEATMAP:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/counts_heatmap/meta.yml b/modules/local/visualization/counts_heatmap/meta.yml
new file mode 100644
index 0000000..41ac5d7
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/meta.yml
@@ -0,0 +1,51 @@
+name: "visualization_counts_heatmap"
+description: Generates a comprehensive heatmap of variant counts, visualizing the mutation landscape across the entire reference sequence.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - heatmap
+  - mutation landscape
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_for_heatmaps:
+        type: file
+        description: CSV file containing processed variant counts formatted for heatmap generation
+        pattern: "*.{csv}"
+  - - min_counts:
+        type: integer
+        description: Minimum count threshold used to filter variants before plotting
+
+output:
+  - counts_heatmap:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - counts_heatmap.pdf:
+          type: file
+          description: PDF heatmap visualizing mutation frequencies across the sequence positions
+          pattern: "counts_heatmap.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/visualization/counts_heatmap/templates/counts_heatmap.R b/modules/local/visualization/counts_heatmap/templates/counts_heatmap.R
new file mode 100644
index 0000000..44ecb8d
--- /dev/null
+++ b/modules/local/visualization/counts_heatmap/templates/counts_heatmap.R
@@ -0,0 +1,187 @@
+#!/usr/bin/env Rscript
+
+# Input: prepared GATK data path, output_path, threshold (same as used for prepare_gatk_data_for_counts_per_cov_heatmap function)
+# Output: counts_per_cov_heatmap.pdf
+
+library(dplyr)
+library(ggplot2)
+
+counts_heatmap <- function(input_csv_path, threshold = 3, output_pdf_path, img_format = "pdf") {
+
+  # Inner function to add padding to the last row, adding 21 amino acids per position
+  pad_heatmap_data_long <- function(heatmap_data_long, min_non_na_value, num_positions_per_row = 75) {
+    all_amino_acids <- c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*")
+
+    max_position <- max(heatmap_data_long\$position)
+    num_missing_positions <- num_positions_per_row - (max_position %% num_positions_per_row)
+
+    if (num_missing_positions < num_positions_per_row) {
+      new_positions <- (max_position + 1):(max_position + num_missing_positions)
+
+      # Add all 21 amino acid variants for each new position
+      padding_data <- expand.grid(
+        mut_aa = all_amino_acids,  # All possible amino acids
+        position = new_positions  # New positions to be padded
+      )
+
+      # Set placeholder values for the added positions to the exact smallest non-NA value
+      padding_data\$total_counts <- min_non_na_value  # Set to the smallest non-NA value
+      padding_data\$wt_aa <- "Y"  # Set wild-type amino acid to 'Y'
+      padding_data\$wt_aa_pos <- paste0("Y", padding_data\$position)  # Create wt_aa_pos with correct positions
+      padding_data\$row_group <- max(heatmap_data_long\$row_group)  # Set row group to the current last group
+
+      # Add the new padding rows to heatmap_data_long
+      heatmap_data_long <- dplyr::bind_rows(heatmap_data_long, padding_data)
+    }
+
+    return(heatmap_data_long)
+  }
+
+  # Load the CSV data
+  heatmap_data <- read.csv(input_csv_path)
+
+  # Check if the necessary column exists in the data
+  if (!"total_counts" %in% colnames(heatmap_data)) {
+    stop("The column 'total_counts' is not found in the data.")
+  }
+
+  # Create heatmap_data_long by selecting necessary columns
+  heatmap_data_long <- heatmap_data %>%
+    select(mut_aa, position, total_counts, wt_aa)  # Use 'total_counts'
+
+  # Find the smallest non-NA value in total_counts
+  min_non_na_value <- min(heatmap_data_long\$total_counts, na.rm = TRUE)
+
+  # Group positions by rows (75 positions per row) and calculate row_group
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(row_group = ((position - 1) %/% 75) + 1)  # Grouping positions into rows
+
+  # Apply padding to add missing positions at the end of the last row, using the calculated min value
+  heatmap_data_long <- pad_heatmap_data_long(heatmap_data_long, min_non_na_value)
+
+  # Convert positions to numeric, sort them, and create wt_aa_pos for the plot
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(position = as.numeric(position)) %>%  # Ensure position is numeric
+    arrange(position) %>%  # Sort by position
+    mutate(wt_aa_pos = factor(paste0(wt_aa, position), levels = unique(paste0(wt_aa, position))))  # Create sorted factor levels for wt_aa_pos
+
+  # Add a column to identify synonymous mutations (where mut_aa == wt_aa)
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(synonymous = mut_aa == wt_aa)
+
+  # Definiere die korrekte Reihenfolge der Aminosäuren
+  amino_acid_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+
+  heatmap_data_long <- heatmap_data_long %>%
+  mutate(mut_aa = factor(mut_aa, levels = amino_acid_order))
+
+  # Bearbeite heatmap_data_long und erstelle syn_positions gleichzeitig
+  syn_positions <- heatmap_data_long %>%
+    mutate(mut_aa = factor(mut_aa, levels = amino_acid_order),
+           # Berechne die x-Koordinate, die pro Gruppe immer von 1 bis 75 verläuft
+           x = as.numeric(factor(wt_aa_pos, levels = unique(wt_aa_pos))) - ((row_group - 1) * 75),
+           y = as.numeric(factor(mut_aa, levels = amino_acid_order))) %>%
+    filter(synonymous == TRUE)
+
+  # Calculate the number of row groups and adjust plot height dynamically
+  num_row_groups <- max(heatmap_data_long\$row_group)
+  plot_height <- num_row_groups * 4
+
+  # Set the limits for the color scale, ignoring NA (negative values are now NA)
+  min_count <- min(heatmap_data_long\$total_counts, na.rm = TRUE)
+  max_count <- max(heatmap_data_long\$total_counts, na.rm = TRUE)
+  max_position <- max(heatmap_data\$position)
+
+  # Create the heatmap plot with explicit handling for positions > max_position
+  heatmap_plot <- ggplot(heatmap_data_long, aes(x = wt_aa_pos, y = mut_aa, fill = total_counts)) +
+    scale_fill_gradientn(colours = c(alpha("blue", 0), "blue"), na.value = "grey35", trans = "log",  # Apply log transformation to the scale
+                         limits = c(min_count, max_count),
+                         breaks = scales::trans_breaks("log10", function(x) 10^x),  # Logarithmic scale breaks
+                         labels = scales::trans_format("log10", scales::math_format(10^.x))) +
+    scale_x_discrete(labels = function(x) {
+      numeric_pos <- as.numeric(gsub("[^0-9]", "", x))
+      ifelse(numeric_pos > max_position, " ", x)
+    }) +
+    geom_tile() +
+
+    # Add diagonal lines for synonymous mutations using geom_segment
+    geom_segment(data = syn_positions[syn_positions\$position <= max_position, ],
+                 aes(x = x - 0.485, xend = x + 0.485,
+                     y = y - 0.485, yend = y + 0.485, color = synonymous),
+                 size = 0.2) +
+
+    # Manuelle Farbskala für die diagonalen Linien
+    scale_color_manual(values = c("TRUE" = "grey10"), labels = c("TRUE" = "")) +
+
+    theme_minimal() +
+    labs(title = "Heatmap of Counts per Variant", x = "Wild-type Amino Acid", y = "Mutant Amino Acid", fill = "Counts", color = "Synonymous Mutation") +
+    theme(plot.title = element_text(size = 16, face = "bold"),
+          axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
+          axis.text.y = element_text(size = 12),  # Larger y-axis labels
+          axis.title.x = element_text(size = 16),
+          axis.title.y = element_text(size = 16),
+          legend.title = element_text(size = 14),  # Larger legend title
+          legend.text = element_text(size = 12),  # Larger legend text
+          panel.spacing = unit(0.1, "lines"),  # Adjust panel spacing
+          strip.text = element_blank(),  # Remove row group labels (facet numbers)
+          strip.background = element_blank(),
+          panel.grid.major = element_blank(),  # Remove major grid lines
+          panel.grid.minor = element_blank()) +  # Remove minor grid lines
+    facet_wrap(~ row_group, scales = "free_x", ncol = 1) +  # Group by 75 positions per row
+    theme(panel.spacing = unit(0.2, "lines"))
+
+  heatmap_plot <- heatmap_plot +
+    geom_point(data = heatmap_data_long, aes(size = ""), colour = "black", alpha = 0)  # Invisible points for legend
+  heatmap_plot <- heatmap_plot +
+    guides(size = guide_legend(paste("Dropout (Counts <", threshold, ")"), override.aes = list(shape = 15, size = 8, colour = "grey35", alpha = 1)))  # Define Legend for Dropouts
+
+  # Save the heatmap plot
+  if (img_format == "pdf") {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150, device = cairo_pdf)
+  } else {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150)
+  }
+
+  if (file.exists(output_pdf_path)) {
+    print("Heatmap image successfully created!")
+  } else {
+    print("Error: Heatmap image was not created.")
+  }
+}
+
+#####
+# run function
+#####
+counts_heatmap(
+  input_csv_path = "$variantCounts_for_heatmaps",
+  threshold = $min_counts,
+  output_pdf_path = "counts_heatmap.pdf",
+  img_format = "pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/counts_per_cov/environment.yml b/modules/local/visualization/counts_per_cov/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/counts_per_cov/main.nf b/modules/local/visualization/counts_per_cov/main.nf
new file mode 100644
index 0000000..e19bd40
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/main.nf
@@ -0,0 +1,31 @@
+process VISUALIZATION_COUNTS_PER_COV {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(variantCounts_for_heatmaps)
+    val min_counts
+
+    output:
+    tuple val(meta), path("counts_per_cov_heatmap.pdf"), emit: counts_per_cov_heatmap
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'counts_per_cov_heatmap.R'
+
+    stub:
+    """
+    touch counts_per_cov_heatmap.pdf
+    echo "VISUALIZATION_COUNTS_PER_COV:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/counts_per_cov/meta.yml b/modules/local/visualization/counts_per_cov/meta.yml
new file mode 100644
index 0000000..354351c
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/meta.yml
@@ -0,0 +1,49 @@
+name: "visualization_counts_per_cov"
+description: "Generates a heatmap visualizing the distribution of variant counts per coverage depth in Deep Mutational Scanning data."
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - heatmap
+  - coverage
+tools:
+  - "r-base":
+      description: "R is a free software environment for statistical computing and graphics"
+      homepage: "https://www.r-project.org/"
+      documentation: "https://cran.r-project.org/manuals.html"
+      licence: ["GPL-2.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_for_heatmaps:
+        type: file
+        description: CSV file containing processed variant counts formatted for heatmap generation
+        pattern: "*.{csv}"
+  - - min_counts:
+        type: integer
+        description: Minimum count threshold used to filter variants for visualization
+
+output:
+  - counts_per_cov_heatmap:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - counts_per_cov_heatmap.pdf:
+          type: file
+          description: PDF heatmap showing variant counts distributed across coverage depths
+          pattern: "counts_per_cov_heatmap.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/visualization/counts_per_cov/templates/counts_per_cov_heatmap.R b/modules/local/visualization/counts_per_cov/templates/counts_per_cov_heatmap.R
new file mode 100644
index 0000000..77518b7
--- /dev/null
+++ b/modules/local/visualization/counts_per_cov/templates/counts_per_cov_heatmap.R
@@ -0,0 +1,187 @@
+#!/usr/bin/env Rscript
+
+# Input: prefiltered GATK data path, output_path, threshold (same as used for prepare_gatk_data_for_counts_per_cov_heatmap function)
+# Output: counts_per_cov_heatmap.pdf
+
+library(dplyr)
+library(ggplot2)
+
+counts_per_cov_heatmap <- function(input_csv_path, threshold = 3, output_pdf_path, img_format = "pdf") {
+
+  # Inner function to add padding to the last row, adding 21 amino acids per position
+  pad_heatmap_data_long <- function(heatmap_data_long, min_non_na_value, num_positions_per_row = 75) {
+    all_amino_acids <- c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*")
+
+    max_position <- max(heatmap_data_long\$position)
+    num_missing_positions <- num_positions_per_row - (max_position %% num_positions_per_row)
+
+    if (num_missing_positions < num_positions_per_row) {
+      new_positions <- (max_position + 1):(max_position + num_missing_positions)
+
+      # Add all 21 amino acid variants for each new position
+      padding_data <- expand.grid(
+        mut_aa = all_amino_acids,  # All possible amino acids
+        position = new_positions  # New positions to be padded
+      )
+
+      # Set placeholder values for the added positions to the exact smallest non-NA value
+      padding_data\$total_counts_per_cov <- min_non_na_value  # Set to the smallest non-NA value
+      padding_data\$wt_aa <- "Y"  # Set wild-type amino acid to 'Y'
+      padding_data\$wt_aa_pos <- paste0("Y", padding_data\$position)  # Create wt_aa_pos with correct positions
+      padding_data\$row_group <- max(heatmap_data_long\$row_group)  # Set row group to the current last group
+
+      # Add the new padding rows to heatmap_data_long
+      heatmap_data_long <- dplyr::bind_rows(heatmap_data_long, padding_data)
+    }
+
+    return(heatmap_data_long)
+  }
+
+  # Load the CSV data
+  heatmap_data <- read.csv(input_csv_path)
+
+  # Check if the necessary column exists in the data
+  if (!"total_counts_per_cov" %in% colnames(heatmap_data)) {
+    stop("The column 'total_counts_per_cov' is not found in the data.")
+  }
+
+  # Create heatmap_data_long by selecting necessary columns
+  heatmap_data_long <- heatmap_data %>%
+    select(mut_aa, position, total_counts_per_cov, wt_aa)  # Use 'total_counts_per_cov'
+
+  # Find the smallest non-NA value in total_counts_per_cov
+  min_non_na_value <- min(heatmap_data_long\$total_counts_per_cov, na.rm = TRUE)
+
+  # Group positions by rows (75 positions per row) and calculate row_group
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(row_group = ((position - 1) %/% 75) + 1)  # Grouping positions into rows
+
+  # Apply padding to add missing positions at the end of the last row, using the calculated min value
+  heatmap_data_long <- pad_heatmap_data_long(heatmap_data_long, min_non_na_value)
+
+  # Convert positions to numeric, sort them, and create wt_aa_pos for the plot
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(position = as.numeric(position)) %>%  # Ensure position is numeric
+    arrange(position) %>%  # Sort by position
+    mutate(wt_aa_pos = factor(paste0(wt_aa, position), levels = unique(paste0(wt_aa, position))))  # Create sorted factor levels for wt_aa_pos
+
+  # Add a column to identify synonymous mutations (where mut_aa == wt_aa)
+  heatmap_data_long <- heatmap_data_long %>%
+    mutate(synonymous = mut_aa == wt_aa)
+
+  # Definiere die korrekte Reihenfolge der Aminosäuren
+  amino_acid_order <- rev(c("G", "A", "V", "L", "M", "I", "F",
+                         "Y", "W", "K", "R", "H", "D", "E",
+                         "S", "T", "C", "N", "Q", "P", "*"))
+
+  heatmap_data_long <- heatmap_data_long %>%
+  mutate(mut_aa = factor(mut_aa, levels = amino_acid_order))
+
+  # Bearbeite heatmap_data_long und erstelle syn_positions gleichzeitig
+  syn_positions <- heatmap_data_long %>%
+    mutate(mut_aa = factor(mut_aa, levels = amino_acid_order),
+           # Berechne die x-Koordinate, die pro Gruppe immer von 1 bis 75 verläuft
+           x = as.numeric(factor(wt_aa_pos, levels = unique(wt_aa_pos))) - ((row_group - 1) * 75),
+           y = as.numeric(factor(mut_aa, levels = amino_acid_order))) %>%
+    filter(synonymous == TRUE)
+
+  # Calculate the number of row groups and adjust plot height dynamically
+  num_row_groups <- max(heatmap_data_long\$row_group)
+  plot_height <- num_row_groups * 4
+
+  # Set the limits for the color scale, ignoring NA (negative values are now NA)
+  min_count <- min(heatmap_data_long\$total_counts_per_cov, na.rm = TRUE)
+  max_count <- max(heatmap_data_long\$total_counts_per_cov, na.rm = TRUE)
+  max_position <- max(heatmap_data\$position)
+
+  # Create the heatmap plot with explicit handling for positions > max_position
+  heatmap_plot <- ggplot(heatmap_data_long, aes(x = wt_aa_pos, y = mut_aa, fill = total_counts_per_cov)) +
+    scale_fill_gradientn(colours = c(alpha("blue", 0), "blue"), na.value = "grey35", trans = "log",  # Apply log transformation to the scale
+                         limits = c(min_count, max_count),
+                         breaks = scales::trans_breaks("log10", function(x) 10^x),  # Logarithmic scale breaks
+                         labels = scales::trans_format("log10", scales::math_format(10^.x))) +
+    scale_x_discrete(labels = function(x) {
+      numeric_pos <- as.numeric(gsub("[^0-9]", "", x))
+      ifelse(numeric_pos > max_position, " ", x)
+    }) +
+    geom_tile() +
+
+    # Add diagonal lines for synonymous mutations using geom_segment
+    geom_segment(data = syn_positions[syn_positions\$position <= max_position, ],
+                 aes(x = x - 0.485, xend = x + 0.485,
+                     y = y - 0.485, yend = y + 0.485, color = synonymous),
+                 size = 0.2) +
+
+    # Manuelle Farbskala für die diagonalen Linien
+    scale_color_manual(values = c("TRUE" = "grey10"), labels = c("TRUE" = "")) +
+
+    theme_minimal() +
+    labs(title = "Heatmap of Counts per Coverage for Mutations", x = "Wild-type Amino Acid", y = "Mutant Amino Acid", fill = "Counts per \\n Coverage", color = "Synonymous Mutation") +
+    theme(plot.title = element_text(size = 16, face = "bold"),
+          axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, size = 12),
+          axis.text.y = element_text(size = 12),  # Larger y-axis labels
+          axis.title.x = element_text(size = 16),
+          axis.title.y = element_text(size = 16),
+          legend.title = element_text(size = 14),  # Larger legend title
+          legend.text = element_text(size = 12),  # Larger legend text
+          panel.spacing = unit(0.1, "lines"),  # Adjust panel spacing
+          strip.text = element_blank(),  # Remove row group labels (facet numbers)
+          strip.background = element_blank(),
+          panel.grid.major = element_blank(),  # Remove major grid lines
+          panel.grid.minor = element_blank()) +  # Remove minor grid lines
+    facet_wrap(~ row_group, scales = "free_x", ncol = 1) +  # Group by 75 positions per row
+    theme(panel.spacing = unit(0.2, "lines"))
+
+  heatmap_plot <- heatmap_plot +
+    geom_point(data = heatmap_data_long, aes(size = ""), colour = "black", alpha = 0)  # Invisible points for legend
+  heatmap_plot <- heatmap_plot +
+    guides(size = guide_legend(paste("Dropout (Counts <", threshold, ")"), override.aes = list(shape = 15, size = 8, colour = "grey35", alpha = 1)))  # Define Legend for Dropouts
+
+  # Save the heatmap plot
+  if (img_format == "pdf") {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150, device = cairo_pdf)
+  } else {
+    ggsave(output_pdf_path, plot = heatmap_plot, width = 16, height = plot_height, dpi = 150)
+  }
+
+  if (file.exists(output_pdf_path)) {
+    print("Heatmap image successfully created!")
+  } else {
+    print("Error: Heatmap image was not created.")
+  }
+}
+
+#####
+# run function
+#####
+counts_per_cov_heatmap(
+  input_csv_path = "$variantCounts_for_heatmaps",
+  threshold = $min_counts,
+  output_pdf_path = "counts_per_cov_heatmap.pdf",
+  img_format = "pdf"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/error_correction_report/environment.yml b/modules/local/visualization/error_correction_report/environment.yml
new file mode 100644
index 0000000..a72f7ec
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/environment.yml
@@ -0,0 +1,7 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - conda-forge::python=3.12.3
+  - conda-forge::pandas=2.2.1
+  - conda-forge::numpy=1.26.4
diff --git a/modules/local/visualization/error_correction_report/main.nf b/modules/local/visualization/error_correction_report/main.nf
new file mode 100644
index 0000000..6038ef7
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/main.nf
@@ -0,0 +1,43 @@
+// Build the self-contained error-correction HTML report for the whole run.
+//
+// One report, not one per file: every sequencing file's numbers travel in the page and each panel
+// recomputes in the browser from whichever files the reader selects, so a single-file view is still
+// one click away while cross-replicate spread (the error bars) becomes possible at all.
+process VISUALIZATION_ERROR_CORRECTION_REPORT {
+    tag "${sample}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://depot.galaxyproject.org/singularity/pandas:2.2.1'
+        : 'quay.io/biocontainers/pandas:2.2.1' }"
+
+    input:
+    val sample                               // run-level name, for the heading
+    val ids_b64                              // base64(JSON) [file id, ...] aligned with `corrected`
+    path corrected, stageAs: 'c*/*'          // per-file tables share a basename -> numbered dirs
+    val method
+    val false_doubles_method                 // 'mle' | 'eb' - names the estimator in the report heading
+    path template_html
+
+    output:
+    path "error_correction_report.html", emit: report
+    path "versions.yml"                , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'build_ec_report.py'
+
+    stub:
+    """
+    touch error_correction_report.html
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        python: "0.0.0"
+        pandas: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/visualization/error_correction_report/meta.yml b/modules/local/visualization/error_correction_report/meta.yml
new file mode 100644
index 0000000..9cb45dc
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/meta.yml
@@ -0,0 +1,53 @@
+name: "visualization_error_correction_report"
+description: Build a self-contained, per-sample HTML report visualising the effect of the sequencing-error correction (positional error bias, correction magnitude, and per-variant raw-vs-corrected counts).
+keywords:
+  - deep mutational scanning
+  - error correction
+  - report
+  - visualisation
+tools:
+  - "pandas":
+      description: "Fast, powerful data structures for data analysis in Python."
+      homepage: "https://pandas.pydata.org/"
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1' ]`
+    - corrected:
+        type: file
+        description: Error-corrected, library-filtered counts (with `counts` corrected and `counts_raw` preserved).
+        pattern: "*.csv"
+  - - method:
+        type: string
+        description: Error-correction method label (e.g. `false_doubles`, `wildtype`).
+  - - false_doubles_method:
+        type: string
+        description: False-doubles estimator actually used (`mle` or `eb`); named in the report heading.
+  - - template_html:
+        type: file
+        description: HTML template into which the report data is injected.
+        pattern: "*.html"
+
+output:
+  - report:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*_error_correction_report.html":
+          type: file
+          description: Self-contained error-correction report.
+          pattern: "*_error_correction_report.html"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
diff --git a/modules/local/visualization/error_correction_report/templates/build_ec_report.py b/modules/local/visualization/error_correction_report/templates/build_ec_report.py
new file mode 100644
index 0000000..e6719a9
--- /dev/null
+++ b/modules/local/visualization/error_correction_report/templates/build_ec_report.py
@@ -0,0 +1,158 @@
+#!/usr/bin/env python
+
+# Build the self-contained error-correction HTML report for a whole run.
+#
+# One report covers every sequencing file: the page carries each file's numbers separately and
+# recomputes every panel in the browser from whichever files are selected, so deselecting down to a
+# single file reproduces exactly what a per-file report used to show.
+#
+# Nextflow template: values below are interpolated; backslashes meant for Python are doubled.
+
+import base64
+import json
+import platform
+import re
+
+import numpy as np
+import pandas as pd
+
+sample = "$sample"
+method = "$method"
+# The false-doubles estimator actually used (mle|eb), so the report can name it. Empty/other for the
+# wildtype method, where it does not apply.
+estimator = "$false_doubles_method"
+# Each entry is {id, type, replicate} - type/replicate come straight from the samplesheet (general
+# for any protein, not hard-coded here) and drive the input-vs-output split in the report.
+files = json.loads(base64.b64decode("$ids_b64").decode("utf-8"))
+paths = "$corrected".split()
+
+if len(files) != len(paths):
+    raise SystemExit(f"{len(files)} ids but {len(paths)} files staged")
+
+MUT = re.compile(r"^([A-Za-z*]+)(\\d+)([A-Za-z*]+)")
+
+# `base_mut` holds the nucleotide change(s) of a variant as "<pos>:<ref>><alt>", comma-separated when
+# the codon carries 2-3 changes (the NNK case). Positions are reference coordinates.
+SNV = re.compile(r"^(\\d+):([ACGTacgt])>([ACGTacgt])\$")
+
+COMPLEMENT = {"A": "T", "C": "G", "G": "C", "T": "A"}
+
+# The six strand-symmetric substitution classes, pyrimidine-centric: a purine reference is
+# reverse-complemented, so C>A and G>T are one and the same event. Order is canonical (as used for
+# mutational signatures) and fixed - the colour of a class never depends on the data.
+SNV_CLASSES = ["C>A", "C>G", "C>T", "T>A", "T>C", "T>G"]
+SNV_LABELS = {
+    "C>A": "C>A / G>T", "C>G": "C>G / G>C", "C>T": "C>T / G>A",
+    "T>A": "T>A / A>T", "T>C": "T>C / A>G", "T>G": "T>G / A>C",
+}
+
+
+def parse_pos(s):
+    m = MUT.match(str(s))
+    return (m.group(1), int(m.group(2)), m.group(3)) if m else (None, None, None)
+
+
+def parse_snvs(base_mut):
+    """"1220:T>G, 1221:T>G" -> [(1220,'T','G'), (1221,'T','G')]"""
+    out = []
+    for part in str(base_mut).split(","):
+        m = SNV.match(part.strip())
+        if m:
+            out.append((int(m.group(1)), m.group(2).upper(), m.group(3).upper()))
+    return out
+
+
+def classify_snv(ref, alt):
+    """Collapse a substitution onto its pyrimidine-centric class key."""
+    if ref not in COMPLEMENT or alt not in COMPLEMENT or ref == alt:
+        return None
+    if ref in ("A", "G"):
+        ref, alt = COMPLEMENT[ref], COMPLEMENT[alt]
+    key = ref + ">" + alt
+    return key if key in SNV_LABELS else None
+
+
+def numcol(df, name):
+    return (pd.to_numeric(df[name], errors="coerce") if name in df.columns
+            else pd.Series([np.nan] * len(df)))
+
+
+def f(x, nd):
+    return None if x is None or x != x else round(float(x), nd)
+
+
+# Variants are keyed on `codon_mut`, not `pos_mut`. `pos_mut` is NOT unique - one amino-acid
+# substitution is reachable through up to three codons (6898 unique pos_mut across 10254 rows on the
+# GID1A run), so keying on it would silently merge distinct nucleotide variants. `codon_mut` is
+# unique (10254/10254).
+#
+# The files do not share a variant set either (union 10771 vs intersection 6886 on GID1A), so this is
+# a union: a variant absent from a file gets None there, never 0. Absent means "not observed above
+# threshold in that file", which is missing data, not a measured zero - averaging it in as 0 would
+# drag every frequency down. The browser skips Nones and reports how many files contributed.
+variants = {}
+n_files = len(files)
+
+for fi, (fid, path) in enumerate(zip(files, paths)):
+    df = pd.read_csv(path)
+    raw = numcol(df, "counts_raw")
+    cor = numcol(df, "counts")
+    cpc_raw = numcol(df, "counts_per_cov_raw")
+    cpc_cor = numcol(df, "counts_per_cov")
+    posmut = df["pos_mut"].astype(str)
+    basemut = df["base_mut"].astype(str) if "base_mut" in df.columns else pd.Series([""] * len(df))
+    codonmut = df["codon_mut"].astype(str) if "codon_mut" in df.columns else posmut
+
+    for i in range(len(df)):
+        r = raw.iloc[i]
+        if pd.isna(r):
+            continue
+        key = codonmut.iloc[i].strip()
+        v = variants.get(key)
+        if v is None:
+            wt, pos, _mut = parse_pos(posmut.iloc[i])
+            nt = basemut.iloc[i].strip()
+            snvs = parse_snvs(nt)
+            # A class is only meaningful when a single nucleotide changed; NNK codon variants
+            # (2-3 changes) mix classes and stay unclassified by design.
+            kls = classify_snv(snvs[0][1], snvs[0][2]) if len(snvs) == 1 else None
+            v = variants[key] = {
+                "key": key, "pos_mut": posmut.iloc[i], "pos": pos, "wt_aa": wt,
+                "nt": nt, "cls": kls,
+                "ntpos": (snvs[0][0] if len(snvs) == 1 else None),
+                "raw": [None] * n_files, "cor": [None] * n_files,
+                "f_raw": [None] * n_files, "f_cor": [None] * n_files,
+            }
+        c = cor.iloc[i]
+        v["raw"][fi] = f(r, 3)
+        v["cor"][fi] = 0.0 if pd.isna(c) else f(c, 3)
+        v["f_raw"][fi] = f(cpc_raw.iloc[i], 10)
+        v["f_cor"][fi] = 0.0 if pd.isna(cpc_cor.iloc[i]) else f(cpc_cor.iloc[i], 10)
+
+data = {
+    "sample": sample,
+    "method": method,
+    "estimator": estimator,
+    "files": files,
+    "classes": [{"cls": k, "label": SNV_LABELS[k]} for k in SNV_CLASSES],
+    "variants": sorted(variants.values(), key=lambda v: (v["pos"] if v["pos"] is not None else 0, v["key"])),
+}
+
+data_json = json.dumps(data, separators=(",", ":")).replace("</", "<\\\\/")
+
+html = open("$template_html", encoding="utf-8").read()
+html = html.replace("__EC_DATA__", data_json).replace("__SAMPLE__", sample)
+with open("error_correction_report.html", "w", encoding="utf-8") as fh:
+    fh.write(html)
+
+with open("versions.yml", "w") as vf:
+    vf.write('"${task.process}":\\n')
+    vf.write("    python: " + platform.python_version() + "\\n")
+    vf.write("    pandas: " + pd.__version__ + "\\n")
+    vf.write("    numpy: " + np.__version__ + "\\n")
+
+import os
+mb = os.path.getsize("error_correction_report.html") / 1e6
+n_snv = sum(1 for v in data["variants"] if v["cls"])
+print(f"Wrote error_correction_report.html ({mb:.1f} MB) - {len(files)} files, "
+      f"{len(data['variants'])} variants ({n_snv} single-nucleotide)")
diff --git a/modules/local/visualization/global_pos_biases_counts/environment.yml b/modules/local/visualization/global_pos_biases_counts/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/global_pos_biases_counts/main.nf b/modules/local/visualization/global_pos_biases_counts/main.nf
new file mode 100644
index 0000000..f28fb1d
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/main.nf
@@ -0,0 +1,34 @@
+process VISUALIZATION_GLOBAL_POS_BIASES_COUNTS {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path aa_seq
+    val sliding_window_size
+
+    output:
+    tuple val(meta), path("rolling_counts.pdf"), emit: rolling_counts
+    tuple val(meta), path("rolling_counts_per_cov.pdf"), emit: rolling_counts_per_cov
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'global_position_biases_counts_and_counts_per_cov.R'
+
+    stub:
+    """
+    touch rolling_counts.pdf
+    touch rolling_counts_per_cov.pdf
+    echo "VISUALIZATION_COUNTS_HEATMAP:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/global_pos_biases_counts/meta.yml b/modules/local/visualization/global_pos_biases_counts/meta.yml
new file mode 100644
index 0000000..8a02b08
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/meta.yml
@@ -0,0 +1,61 @@
+name: "visualization_global_pos_biases_counts"
+description: Calculates and visualizes the rolling mean of variant counts and counts normalized by coverage across the reference sequence to identify regional biases or mutation hotspots.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - rolling mean
+  - sliding window
+  - bias
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts filtered against the target library
+        pattern: "*.{csv}"
+  - - aa_seq:
+        type: file
+        description: Text or FASTA file containing the reference amino acid sequence
+  - - sliding_window_size:
+        type: integer
+        description: The size of the window (number of amino acids) used for calculating the rolling mean
+
+output:
+  - rolling_counts:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - rolling_counts.pdf:
+          type: file
+          description: PDF plot showing the rolling mean of raw variant counts across positions
+          pattern: "rolling_counts.pdf"
+  - rolling_counts_per_cov:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - rolling_counts_per_cov.pdf:
+          type: file
+          description: PDF plot showing the rolling mean of counts normalized by local coverage
+          pattern: "rolling_counts_per_cov.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/visualization/global_pos_biases_counts/templates/global_position_biases_counts_and_counts_per_cov.R b/modules/local/visualization/global_pos_biases_counts/templates/global_position_biases_counts_and_counts_per_cov.R
new file mode 100644
index 0000000..2ec9d91
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_counts/templates/global_position_biases_counts_and_counts_per_cov.R
@@ -0,0 +1,186 @@
+#!/usr/bin/env Rscript
+
+# input: prefiltered gatk path, aa seq path, window_size (sliding window), output_path_folder
+# output: two lineplots showing counts & counts per coverage divided in types of variants (single-/double-/triple-base exchange)
+
+library(zoo) # sliding window
+library(dplyr)
+library(ggplot2)
+library(scales)
+
+position_biases <- function(prefiltered_gatk_path, aa_seq_path, window_size = 10) {
+
+  # Load and process the data
+  prefiltered_gatk <- read.table(prefiltered_gatk_path, sep = ",", fill = NA, header = TRUE)
+  prefiltered_gatk\$pos <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", prefiltered_gatk\$pos_mut))
+  unique_pos <- unique(as.numeric(prefiltered_gatk\$pos))
+  aa_seq <- readLines(aa_seq_path, warn = FALSE)
+  aa_seq_length <- nchar(aa_seq)
+  aa_positions <- seq(nchar(aa_seq))
+
+  means_counts_single <- rep(NA, nchar(aa_seq))
+  means_counts_double <- rep(NA, nchar(aa_seq))
+  means_counts_triple <- rep(NA, nchar(aa_seq))
+  means_counts_per_cov_single <- rep(NA, nchar(aa_seq))
+  means_counts_per_cov_double <- rep(NA, nchar(aa_seq))
+  means_counts_per_cov_triple <- rep(NA, nchar(aa_seq))
+
+
+  # Loop through each position in the amino acid sequence
+  for (i in 1:(nchar(aa_seq))) {
+
+    # Filter the data for the current position in aa_positions
+    window_data <- prefiltered_gatk %>% filter(prefiltered_gatk\$pos %in% aa_positions[i])
+
+    # Calculate mean for Single mutations (where varying_bases == 1)
+    window_data_single <- window_data %>% filter(varying_bases == 1)
+    means_counts_single[i] <- mean(window_data_single\$counts, na.rm = FALSE)
+    means_counts_per_cov_single[i] <- mean(window_data_single\$counts_per_cov, na.rm = FALSE)
+
+    # Calculate mean for Double mutations (where varying_bases == 2)
+    window_data_double <- window_data %>% filter(varying_bases == 2)
+    means_counts_double[i] <- mean(window_data_double\$counts, na.rm = FALSE)
+    means_counts_per_cov_double[i] <- mean(window_data_double\$counts_per_cov, na.rm = FALSE)
+
+    # Calculate mean for Triple mutations (where varying_bases == 3)
+    window_data_triple <- window_data %>% filter(varying_bases == 3)
+    means_counts_triple[i] <- mean(window_data_triple\$counts, na.rm = FALSE)
+    means_counts_per_cov_triple[i] <- mean(window_data_triple\$counts_per_cov, na.rm = FALSE)
+  }
+
+  pos_bias_df <- data.frame(pos = seq(nchar(aa_seq)))
+
+
+  pos_bias_df\$rolling_mean_counts_single <- rollapply(means_counts_single, width = window_size,
+                                               FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_double <- rollapply(means_counts_double, width = window_size,
+                                                      FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_triple <- rollapply(means_counts_triple, width = window_size,
+                                                      FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+
+  pos_bias_df\$rolling_mean_counts_per_cov_single <- rollapply(means_counts_per_cov_single, width = window_size,
+                                                       FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_per_cov_double <- rollapply(means_counts_per_cov_double, width = window_size,
+                                                              FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+  pos_bias_df\$rolling_mean_counts_per_cov_triple <- rollapply(means_counts_per_cov_triple, width = window_size,
+                                                              FUN = function(x) (mean(x, na.rm = TRUE) + 0.00001), fill = "extend")
+
+
+  # Replace NAs with 0 for rolling means and SE
+  pos_bias_df\$rolling_mean_counts_single[is.na(pos_bias_df\$rolling_mean_counts_single)] <- 0.00001
+  pos_bias_df\$rolling_mean_counts_double[is.na(pos_bias_df\$rolling_mean_counts_double)] <- 0.00001
+  pos_bias_df\$rolling_mean_counts_triple[is.na(pos_bias_df\$rolling_mean_counts_triple)] <- 0.00001
+  pos_bias_df\$rolling_mean_counts_per_cov_single[is.na(pos_bias_df\$rolling_mean_counts_per_cov_single)] <- 0.000001
+  pos_bias_df\$rolling_mean_counts_per_cov_double[is.na(pos_bias_df\$rolling_mean_counts_per_cov_double)] <- 0.000001
+  pos_bias_df\$rolling_mean_counts_per_cov_triple[is.na(pos_bias_df\$rolling_mean_counts_per_cov_triple)] <- 0.000001
+
+
+
+  plots_theme <- list(
+
+    # Add the minimal theme to the list
+    theme_minimal(),
+
+    # Customize legend title and appearance
+    theme(legend.title = element_text(size = 10, face = "bold"),
+          legend.position = "right"),
+
+    # Customize guides for the legend elements
+    guides(
+      color = guide_legend(title = "Mutation Type", order = 1),  # Title for line color
+      fill = guide_legend(title = "Standard Deviation", order = 2),  # Title for ribbon fill
+      linetype = guide_legend(title = "Required Coverage", order = 3)  # Title for line type
+    )
+  )
+
+
+   # Placeholder name for the linetype
+  linetype_placeholder <- "Required coverage"
+
+  rolling_counts_plot <- ggplot(pos_bias_df, aes(x = pos)) +
+
+    # Add line for rolling mean coverage
+    geom_line(aes(y = rolling_mean_counts_single, color = "One Varying Base")) +
+    geom_line(aes(y = rolling_mean_counts_double, color = "Two Varying Bases")) +
+    geom_line(aes(y = rolling_mean_counts_triple, color = "Three Varying Bases")) +
+
+    # Individual axis labels for this plot
+    xlab("Amino Acid Position") +
+    ylab("Counts") +
+
+    # Manually set color and fill labels
+    scale_color_manual(name = "Mutation Type",
+                       values = c("One Varying Base" = "chocolate", "Two Varying Bases" = "darkolivegreen3", "Three Varying Bases" = "deepskyblue1"),
+                       limits = c("One Varying Base", "Two Varying Bases", "Three Varying Bases")) + # Color for line
+
+    # Apply the saved theme and design elements at the end
+    plots_theme +
+
+    scale_y_continuous(trans = 'log10', labels = scales::comma)
+
+
+
+
+  rolling_counts_per_cov_plot <- ggplot(pos_bias_df, aes(x = pos)) +
+
+    # Add line for rolling mean coverage
+    geom_line(aes(y = rolling_mean_counts_per_cov_single, color = "One Varying Base")) +
+    geom_line(aes(y = rolling_mean_counts_per_cov_double, color = "Two Varying Bases")) +
+    geom_line(aes(y = rolling_mean_counts_per_cov_triple, color = "Three Varying Bases")) +
+
+    # Individual axis labels for this plot
+    xlab("Amino Acid Position") +
+    ylab("Counts") +
+
+    # Manually set color and fill labels
+    scale_color_manual(name = "Mutation Type",
+                       values = c("One Varying Base" = "chocolate", "Two Varying Bases" = "darkolivegreen3", "Three Varying Bases" = "deepskyblue1"),
+                       limits = c("One Varying Base", "Two Varying Bases", "Three Varying Bases")) + # Color and legend order for lines
+
+    # Apply the saved theme and design elements at the end
+    plots_theme +
+
+    scale_y_continuous(trans = 'log10', labels = scales::comma)
+
+
+  ggsave(filename = "rolling_counts.pdf", plot = rolling_counts_plot, device = "pdf", width = 10, height = 6)
+  ggsave(filename = "rolling_counts_per_cov.pdf", plot = rolling_counts_per_cov_plot, device = "pdf", width = 10, height = 6)
+}
+
+#####
+# run pipeline
+#####
+position_biases(
+  prefiltered_gatk_path = "$variantCounts_filtered_by_library",
+  aa_seq_path = "$aa_seq",
+  window_size = $sliding_window_size
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+scales_version <- as.character(packageVersion("scales"))
+zoo_version <- as.character(packageVersion("zoo"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+if (length(zoo_version) == 0) zoo_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-scales:', scales_version),
+    paste('    r-zoo:', zoo_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/global_pos_biases_cov/environment.yml b/modules/local/visualization/global_pos_biases_cov/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/global_pos_biases_cov/main.nf b/modules/local/visualization/global_pos_biases_cov/main.nf
new file mode 100644
index 0000000..b6b18e3
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/main.nf
@@ -0,0 +1,33 @@
+process VISUALIZATION_GLOBAL_POS_BIASES_COV {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path aa_seq
+    val sliding_window_size
+    val aimed_cov
+
+    output:
+    tuple val(meta), path("rolling_coverage.pdf"), emit: rolling_coverage
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'global_position_biases_cov.R'
+
+    stub:
+    """
+    touch rolling_coverage.pdf
+    echo "VISUALIZATION_COUNTS_HEATMAP:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/global_pos_biases_cov/meta.yml b/modules/local/visualization/global_pos_biases_cov/meta.yml
new file mode 100644
index 0000000..c0b6216
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/meta.yml
@@ -0,0 +1,56 @@
+name: "visualization_global_pos_biases_cov"
+description: Calculates and plots the rolling mean coverage across the protein sequence to identify positional biases, regions of low coverage, or potential experimental dropouts.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - coverage
+  - rolling window
+  - bias
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts filtered against the target library
+        pattern: "*.{csv}"
+  - - aa_seq:
+        type: file
+        description: Text file containing the reference amino acid sequence
+  - - sliding_window_size:
+        type: integer
+        description: The size of the window (in amino acids) used for calculating the rolling mean coverage
+  - - aimed_cov:
+        type: integer
+        description: The target coverage threshold to visualize as a horizontal reference line in the plot
+
+output:
+  - rolling_coverage:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - rolling_coverage.pdf:
+          type: file
+          description: PDF plot showing the rolling mean coverage across sequence positions
+          pattern: "rolling_coverage.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/visualization/global_pos_biases_cov/templates/global_position_biases_cov.R b/modules/local/visualization/global_pos_biases_cov/templates/global_position_biases_cov.R
new file mode 100644
index 0000000..0a2fda1
--- /dev/null
+++ b/modules/local/visualization/global_pos_biases_cov/templates/global_position_biases_cov.R
@@ -0,0 +1,121 @@
+#!/usr/bin/env Rscript
+
+# input: prefiltered gatk path, aa seq path, window_size (sliding window), output_path_folder, aimed counts per aa variant
+# output: lineplot showing coverage over positions and dotted line: Assuming all potential 21 variants are equally distributed, this is the coverage one need to get the aimed counts per variant.
+
+library(zoo) # sliding window
+library(dplyr)
+library(ggplot2)
+library(scales)
+position_biases <- function(prefiltered_gatk_path, aa_seq_path, window_size = 10, output_file_path, targeted_counts_per_aa_variant = 100) {
+
+  # Load and process the data
+  prefiltered_gatk <- read.table(prefiltered_gatk_path, sep = ",", fill = NA, header = TRUE)
+  prefiltered_gatk\$pos <- as.numeric(sub("(\\\\D)(\\\\d+)(\\\\D)", "\\\\2", prefiltered_gatk\$pos_mut))
+  unique_pos <- unique(as.numeric(prefiltered_gatk\$pos))
+  aa_seq <- readLines(aa_seq_path, warn = FALSE)
+  aa_seq_length <- nchar(aa_seq)
+  aa_positions <- seq(nchar(aa_seq))
+
+  means_cov <- rep(NA, nchar(aa_seq))
+
+  # Calculate means for cov (should be the same over all variants in one position)
+  for (i in 1:(nchar(aa_seq))) {
+    window_data <- prefiltered_gatk %>% filter(prefiltered_gatk\$pos %in% aa_positions[i])
+    means_cov[i] <- mean(window_data\$cov, na.rm = FALSE)
+  }
+
+  pos_bias_df <- data.frame(pos = seq(nchar(aa_seq)))
+
+  # Log-transform the rolling means to avoid issues with zeros (log(y + 0.001))
+  pos_bias_df\$rolling_mean_cov <- rollapply(means_cov, width = window_size,
+                                            FUN = function(x) (mean(x, na.rm = TRUE)), fill = "extend")
+
+  # Generate the plot
+  plots_theme <- list(
+
+    # Customize legend appearance (leave titles blank)
+    guides(
+      color = guide_legend(title = NULL, order = 1),  # Title for line color
+      fill = guide_legend(title = NULL, order = 2),  # Title for ribbon fill
+      linetype = guide_legend(title = NULL, order = 3)  # Title for line type
+    ),
+
+    # Apply minimal theme
+    theme_minimal()
+  )
+
+
+  linetype_placeholder <- "Required coverage"
+
+  rolling_cov_plot <- ggplot(pos_bias_df, aes(x = pos, y = rolling_mean_cov)) +
+
+    # Add line for rolling mean coverage
+    geom_line(aes(color = "Coverage")) +
+
+    # Add a horizontal line with a mapped linetype so it appears in the legend
+    geom_hline(aes(yintercept = ((21 * nchar(aa_seq) * targeted_counts_per_aa_variant)),
+                   linetype = linetype_placeholder),
+               color = "black", linewidth = 0.3) +
+
+    # Individual axis labels for this plot
+    xlab("Amino Acid Position") +
+    ylab("Coverage") +
+
+    # Manually set color and fill labels
+    scale_color_manual(values = c("Coverage" = "black")) +  # Color for line
+
+    # Set the linetype with a label that uses the targeted threshold dynamically
+    scale_linetype_manual(values = c("Required coverage" = "dotted"),
+                          labels = paste("Required coverage \\n for", as.character(targeted_counts_per_aa_variant), "counts per variant \\n if equally present")) +
+
+    # Apply the saved theme and design elements at the end
+    plots_theme +
+
+    # Set y-axis to log scale and apply comma formatting
+    scale_y_continuous(trans = 'log10', labels = scales::comma)
+
+
+  # Return the plot
+  ggsave(filename = output_file_path, plot = rolling_cov_plot, device = "pdf", width = 10, height = 6)
+}
+
+#####
+# run function
+#####
+position_biases(
+  prefiltered_gatk_path = "$variantCounts_filtered_by_library",
+  aa_seq_path = "$aa_seq",
+  window_size = $sliding_window_size,
+  output_file_path = "rolling_coverage.pdf",
+  targeted_counts_per_aa_variant = $aimed_cov
+)
+
+#####
+# create versions.yml
+#####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+scales_version <- as.character(packageVersion("scales"))
+zoo_version <- as.character(packageVersion("zoo"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+if (length(zoo_version) == 0) zoo_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-scales:', scales_version),
+    paste('    r-zoo:', zoo_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/logdiff/environment.yml b/modules/local/visualization/logdiff/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/visualization/logdiff/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/logdiff/main.nf b/modules/local/visualization/logdiff/main.nf
new file mode 100644
index 0000000..3b733c5
--- /dev/null
+++ b/modules/local/visualization/logdiff/main.nf
@@ -0,0 +1,32 @@
+process VISUALIZATION_LOGDIFF {
+    tag "$meta.id"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(library_completed_variantCounts)
+
+    output:
+    tuple val(meta), path("logdiff_plot.pdf"), emit: logdiff_plot
+    tuple val(meta), path("logdiff_varying_bases.pdf"), emit: logdiff_varying_bases
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'logdiff.R'
+
+    stub:
+    """
+    touch logdiff_plot.pdf
+    touch logdiff_varying_bases.pdf
+    echo "VISUALIZATION_COUNTS_HEATMAP:" > versions.yml
+    echo "  stub-version: 0.0.0" >> versions.yml
+    """
+}
diff --git a/modules/local/visualization/logdiff/meta.yml b/modules/local/visualization/logdiff/meta.yml
new file mode 100644
index 0000000..dd4e343
--- /dev/null
+++ b/modules/local/visualization/logdiff/meta.yml
@@ -0,0 +1,54 @@
+name: "visualization_logdiff"
+description: Generates diagnostic plots to visualize the logarithmic differences in variant counts, specifically focusing on the distribution of variants across different nucleotide compositions.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - diagnostics
+  - logdiff
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - library_completed_variantCounts:
+        type: file
+        description: CSV file containing processed variant counts, including zero-count entries from the library
+        pattern: "*.{csv}"
+
+output:
+  - logdiff_plot:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - logdiff_plot.pdf:
+          type: file
+          description: PDF plot showing the overall logarithmic differences in variant counts
+          pattern: "logdiff_plot.pdf"
+  - logdiff_varying_bases:
+      - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - logdiff_varying_bases.pdf:
+          type: file
+          description: PDF plot showing logarithmic differences categorized by varying nucleotide bases
+          pattern: "logdiff_varying_bases.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/visualization/logdiff/templates/logdiff.R b/modules/local/visualization/logdiff/templates/logdiff.R
new file mode 100644
index 0000000..752e574
--- /dev/null
+++ b/modules/local/visualization/logdiff/templates/logdiff.R
@@ -0,0 +1,115 @@
+#!/usr/bin/env Rscript
+
+# Maybe develop additional ideas to characterise variants below 10 percentile -> Do we find patterns that help the user to understand why certain variants are hard to count?
+
+# input: completed_prefiltered_gatk path, output folder path
+# output: two logdiff-plots (counts_per_cov): 1st lineplot 2nd dotplot showing type of variant (varying bases - 1/2/3)
+
+library(dplyr)
+library(ggplot2)
+library(scales)
+
+ logdiff_plot <- function(prefiltered_gatk_path) {
+
+   # Load the data
+   prefiltered_gatk <- read.table(prefiltered_gatk_path, sep = ",", fill = NA, header = TRUE)
+
+   # Sort by counts_per_cov while keeping corresponding varying_bases
+   sorted_counts <- prefiltered_gatk %>%
+     arrange(counts_per_cov)  # Sort by counts_per_cov
+
+   # Create a new column for the sorted index (1, 2, 3, ...)
+   sorted_counts\$ids <- 1:nrow(sorted_counts)
+
+   # Calculate Q1 (10% quantile) and Q3 (90% quantile)
+   Q1 <- quantile(sorted_counts\$counts_per_cov, 0.10, na.rm = TRUE)
+   Q3 <- quantile(sorted_counts\$counts_per_cov, 0.90, na.rm = TRUE)
+
+   # Calculate the LogDiff
+   LogDiff <- log10(Q3) - log10(Q1)
+
+   # Create the first plot with a line
+   line_plot <- ggplot(sorted_counts, aes(x = ids, y = counts_per_cov)) +
+     geom_line(color = "black") +
+
+     # Set axis labels
+     xlab("Variants") +
+     ylab("Counts per Coverage") +
+
+     # Apply logarithmic scale to the y-axis
+     scale_y_continuous(trans = 'log10') +
+
+     # Add horizontal dotted lines at Q1 and Q3
+     geom_hline(yintercept = Q1, linetype = "dotted", color = "black") +
+     geom_hline(yintercept = Q3, linetype = "dotted", color = "black") +
+
+     # Add the LogDiff value to the top left corner
+     annotate("text", x = 0, y = max(sorted_counts\$counts_per_cov), label = paste("LogDiff =", round(LogDiff, 2)), hjust = 0, vjust = 1, size = 5, color = "black") +
+
+     # Apply the minimal theme
+     theme_minimal()
+
+   # Save the line plot as a PDF
+   ggsave(filename = "logdiff_plot.pdf", plot = line_plot, device = "pdf", width = 10, height = 6)
+
+   # Create the second plot with colored dots based on varying_bases
+   colored_plot <- ggplot(sorted_counts, aes(x = ids, y = counts_per_cov, color = as.factor(varying_bases))) +
+
+     # Add horizontal dotted lines at Q1 and Q3
+     geom_hline(yintercept = Q1, linetype = "dotted", color = "black") +
+     geom_hline(yintercept = Q3, linetype = "dotted", color = "black") +
+
+     geom_point(size = 0.9) +  # Use points instead of lines
+
+     # Set axis labels
+     xlab("Variants") +
+     ylab("Counts per Coverage") +
+
+     # Apply logarithmic scale to the y-axis
+     scale_y_continuous(trans = 'log10') +
+
+     # Add the LogDiff value to the top left corner
+     annotate("text", x = 1, y = max(sorted_counts\$counts_per_cov), label = paste("LogDiff =", round(LogDiff, 2)), hjust = 0, vjust = 1, size = 5, color = "black") +
+
+     # Add color scale for varying_bases
+     scale_color_manual(values = c("1" = "chocolate", "2" = "darkolivegreen3", "3" = "deepskyblue1"), name = "Varying \\n Bases") +
+
+     # Apply the minimal theme
+     theme_minimal()
+
+   # Save the colored plot as a PDF
+   ggsave(filename = "logdiff_varying_bases.pdf", plot = colored_plot, device = "pdf", width = 10, height = 6)
+ }
+
+#####
+# run function
+#####
+logdiff_plot(
+  prefiltered_gatk_path = "$library_completed_variantCounts"
+)
+
+####
+# create versions.yml
+####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+dplyr_version <- as.character(packageVersion("dplyr"))
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+scales_version <- as.character(packageVersion("scales"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(dplyr_version) == 0) dplyr_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+if (length(scales_version) == 0) scales_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-dplyr:', dplyr_version),
+    paste('    r-ggplot2:', ggplot2_version),
+    paste('    r-scales:', scales_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/seqdepth/environment.yml b/modules/local/visualization/seqdepth/environment.yml
new file mode 100644
index 0000000..197283a
--- /dev/null
+++ b/modules/local/visualization/seqdepth/environment.yml
@@ -0,0 +1,15 @@
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - bioconda::bioconductor-biostrings=2.78.0
+  - conda-forge::r-base=4.5.1
+  - conda-forge::r-biocmanager=1.30.27
+  - conda-forge::r-dplyr=1.1.4
+  - conda-forge::r-ggplot2=4.0.2
+  - conda-forge::r-reshape2=1.4.4
+  - conda-forge::r-scales=1.4.0
+  - conda-forge::r-stringr=1.6.0
+  - conda-forge::r-tidyr=1.3.1
+  - conda-forge::r-tidyverse=2.0.0
+  - conda-forge::r-zoo=1.8_15
diff --git a/modules/local/visualization/seqdepth/main.nf b/modules/local/visualization/seqdepth/main.nf
new file mode 100644
index 0000000..9d338c4
--- /dev/null
+++ b/modules/local/visualization/seqdepth/main.nf
@@ -0,0 +1,38 @@
+process VISUALIZATION_SEQDEPTH {
+    tag "$meta.id"
+    // Closed-form hypergeometric rarefaction - fast, so no longer a heavy process.
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity'
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/73/73a72ec77725aeb67678a74228938fdd6827b669d01a8c96951b1a8ef96eeb0f/data'
+        : 'community.wave.seqera.io/library/bioconductor-biostrings_bioconductor-mutscan_r-base_r-biocmanager_pruned:c65036d76406f342' }"
+
+    input:
+    tuple val(meta), path(variantCounts_filtered_by_library)
+    path possible_mutations
+    val min_counts
+
+    output:
+    tuple val(meta), path("SeqDepth.pdf"), emit: SeqDepth
+    tuple val(meta), path("seqdepth_curve.csv"), emit: curve
+    path "versions.yml", emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'SeqDepth_simulation.R'
+
+    stub:
+    """
+    touch SeqDepth.pdf
+    touch seqdepth_curve.csv
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        r-base: "0.0.0"
+        r-ggplot2: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/visualization/seqdepth/meta.yml b/modules/local/visualization/seqdepth/meta.yml
new file mode 100644
index 0000000..e3a35c6
--- /dev/null
+++ b/modules/local/visualization/seqdepth/meta.yml
@@ -0,0 +1,55 @@
+name: "visualization_seqdepth"
+description: Performs a sequencing depth simulation to assess library coverage and saturation, helping to determine if the sequencing effort was sufficient to capture all intended variants.
+keywords:
+  - deep mutational scanning
+  - dms
+  - visualization
+  - sequencing depth
+  - saturation
+tools:
+  - "mutscan":
+      description: "R package for analysis of deep mutational scanning data"
+      homepage: "https://bioconductor.org/packages/release/bioc/html/mutscan.html"
+      documentation: "https://bioconductor.org/packages/release/bioc/manuals/mutscan/man/mutscan.pdf"
+      tool_dev_url: "https://github.com/csoneson/mutscan"
+      doi: "10.1186/s12859-023-05187-y"
+      licence: ["GPL-3.0-or-later"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', single_end:false ]`
+    - variantCounts_filtered_by_library:
+        type: file
+        description: CSV file containing variant counts filtered against the target library
+        pattern: "*.{csv}"
+  - - possible_mutations:
+        type: file
+        description: CSV file defining all theoretically possible mutations for the library design
+        pattern: "*.{csv}"
+  - - min_counts:
+        type: integer
+        description: Minimum count threshold used in the simulation logic
+
+output:
+  - SeqDepth:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', single_end:false ]`
+      - SeqDepth.pdf:
+          type: file
+          description: PDF plot showing the simulation of variant detection as a function of sequencing depth
+          pattern: "SeqDepth.pdf"
+  - versions:
+      - versions.yml:
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/modules/local/visualization/seqdepth/templates/SeqDepth_simulation.R b/modules/local/visualization/seqdepth/templates/SeqDepth_simulation.R
new file mode 100644
index 0000000..9a55b1b
--- /dev/null
+++ b/modules/local/visualization/seqdepth/templates/SeqDepth_simulation.R
@@ -0,0 +1,106 @@
+#!/usr/bin/env Rscript
+
+# Sequencing-depth rarefaction: what fraction of the possible library variants stay detectable
+# (>= `threshold` reads) as the sequencing depth is subsampled from full down to zero.
+#
+# This is a closed-form rarefaction, not a stochastic simulation. Subsampling reads without
+# replacement makes each variant's surviving read count hypergeometric, so the expected number of
+# variants still at or above the threshold at depth n is a sum of hypergeometric tail probabilities:
+#
+#   E[detected | n reads] = sum_i  P(Hypergeom(N, c_i, n) >= threshold)
+#                         = sum_i  phyper(threshold - 1, c_i, N - c_i, n, lower.tail = FALSE)
+#
+# where N is the total read count and c_i the reads on variant i. It is exact (no Monte-Carlo noise),
+# needs no random sampling, and is orders of magnitude faster than draw-one-read-at-a-time loops - so
+# the step count can be fine without the runtime blowing up.
+
+library(ggplot2)
+
+SeqDepth_simulation_plot <- function(prefiltered_gatk_path, possible_mutations_path,
+                                     output_file_path, curve_csv_path,
+                                     n_steps = 40, threshold = 3) {
+
+  data <- read.csv(prefiltered_gatk_path)
+  counts <- as.numeric(data\$counts)
+  counts <- counts[!is.na(counts) & counts > 0]           # only observed variants carry reads
+  possible_mutations <- read.csv(possible_mutations_path)
+  total_possible <- nrow(possible_mutations)               # denominator: the whole designed library
+
+  N <- sum(counts)
+  if (N <= 0 || total_possible <= 0) {
+    stop("SeqDepth: no counts or no possible mutations to rarefy")
+  }
+
+  # Evenly spaced depths from 0 to the full library, as integer read totals.
+  depths <- unique(round(seq(0, N, length.out = n_steps + 1)))
+
+  # Expected detected variants at each depth. phyper is vectorised over the per-variant counts, so each
+  # depth is one vectorised call; `lower.tail = FALSE` gives P(X > threshold-1) = P(X >= threshold).
+  expected_detected <- vapply(depths, function(n) {
+    if (n <= 0) return(0)
+    sum(phyper(threshold - 1, counts, N - counts, n, lower.tail = FALSE))
+  }, numeric(1))
+
+  curve <- data.frame(
+    depth_fold   = round(depths / N, 4),                       # x: fraction of full sequencing depth
+    variants_pct = round(expected_detected / total_possible * 100, 4)  # y: % of the possible library
+  )
+  write.csv(curve, curve_csv_path, row.names = FALSE)
+
+  plot <- ggplot(curve, aes(x = depth_fold, y = variants_pct)) +
+    geom_line(color = "black", linewidth = 0.4) +
+    geom_hline(yintercept = 100, linetype = "dotted", color = "black") +
+    scale_y_continuous(
+      labels = scales::percent_format(scale = 1),
+      limits = c(0, 100),
+      breaks = seq(0, 100, by = 5)
+    ) +
+    scale_x_continuous(
+      limits = c(0, 1),
+      breaks = seq(0, 1, length.out = 20),
+      labels = scales::number_format(accuracy = 0.01)
+    ) +
+    labs(x = "Fraction of sequencing depth", y = "Variants detected (% of possible library)") +
+    theme_minimal() +
+    theme(
+      panel.border = element_rect(color = "black", fill = NA),
+      panel.grid.major = element_line(linewidth = 0.2, linetype = "solid", color = "grey80"),
+      panel.grid.minor = element_blank(),
+      axis.text.x = element_text(angle = 45, hjust = 1)
+    )
+
+  ggsave(output_file_path, plot = plot, device = "pdf", width = 8, height = 6)
+}
+
+
+#####
+# run function
+#####
+SeqDepth_simulation_plot(
+  prefiltered_gatk_path   = "$variantCounts_filtered_by_library",
+  possible_mutations_path = "$possible_mutations",
+  output_file_path        = "SeqDepth.pdf",
+  curve_csv_path          = "seqdepth_curve.csv",
+  n_steps                 = 40,
+  threshold               = $min_counts
+  )
+
+#####
+# create versions.yml
+#####
+r_version <- strsplit(version[['version.string']], ' ')[[1]][3]
+ggplot2_version <- as.character(packageVersion("ggplot2"))
+
+if (is.null(r_version)) r_version <- "unknown"
+if (length(ggplot2_version) == 0) ggplot2_version <- "unknown"
+
+f <- file("versions.yml", "w")
+writeLines(
+  c(
+    '"${task.process}":',
+    paste('    r-base:', r_version),
+    paste('    r-ggplot2:', ggplot2_version)
+  ),
+  f
+)
+close(f)
diff --git a/modules/local/visualization/summary_report/environment.yml b/modules/local/visualization/summary_report/environment.yml
new file mode 100644
index 0000000..b4d8e52
--- /dev/null
+++ b/modules/local/visualization/summary_report/environment.yml
@@ -0,0 +1,8 @@
+name: visualization_summary_report
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - python=3.12.3
+  - pandas=2.2.1
+  - numpy=1.26.4
diff --git a/modules/local/visualization/summary_report/main.nf b/modules/local/visualization/summary_report/main.nf
new file mode 100644
index 0000000..25acef6
--- /dev/null
+++ b/modules/local/visualization/summary_report/main.nf
@@ -0,0 +1,43 @@
+// Build the single, self-contained all-in-one HTML report for a run.
+//
+// Every artefact the report shows is passed through one generic pair of inputs: a base64-encoded
+// JSON manifest and the matching staged files, in the same order. That keeps the process signature
+// stable no matter which optional parts of the pipeline ran - a section simply disappears when its
+// entries are absent from the manifest, so no channel juggling is needed for --fitness/--pdb/EC.
+process VISUALIZATION_SUMMARY_REPORT {
+    tag "${meta.sample ?: meta.id}"
+    label 'process_single'
+
+    conda "${moduleDir}/environment.yml"
+
+    container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container
+        ? 'https://depot.galaxyproject.org/singularity/pandas:2.2.1'
+        : 'quay.io/biocontainers/pandas:2.2.1' }"
+
+    input:
+    tuple val(meta), val(run_b64)            // base64(JSON) run metadata (params, samples, versions)
+    val manifest_b64                         // base64(JSON) [{kind,group,name}, ...] aligned with `assets`
+    path assets, stageAs: 'a*/*'             // pdf/csv/tsv/html artefacts; numbered dirs avoid basename clashes
+    path template_html
+    path threedmol_js                        // 3Dmol.js library, inlined only when the run has a structure
+
+    output:
+    tuple val(meta), path("deepmutscan_report.html"), emit: report
+    path "versions.yml"                            , emit: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    template 'build_summary_report.py'
+
+    stub:
+    """
+    touch deepmutscan_report.html
+    cat <<-END_VERSIONS > versions.yml
+    "${task.process}":
+        python: "0.0.0"
+        pandas: "0.0.0"
+    END_VERSIONS
+    """
+}
diff --git a/modules/local/visualization/summary_report/meta.yml b/modules/local/visualization/summary_report/meta.yml
new file mode 100644
index 0000000..0b23fca
--- /dev/null
+++ b/modules/local/visualization/summary_report/meta.yml
@@ -0,0 +1,60 @@
+name: "visualization_summary_report"
+description: Build the single self-contained all-in-one HTML report for a run (QC to fitness)
+keywords:
+  - report
+  - html
+  - quality control
+  - deep mutational scanning
+tools:
+  - "pandas":
+      description: "Powerful data structures for data analysis, time series, and statistics"
+      homepage: "https://pandas.pydata.org/"
+      licence: ["BSD-3-Clause"]
+
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. `[ id:'test', sample:'ORF1' ]`
+    - run_b64:
+        type: string
+        description: Base64-encoded JSON with run-level metadata (parameters, samples, versions).
+  - - manifest_b64:
+        type: string
+        description: |
+          Base64-encoded JSON array describing every staged asset as `{kind, group, name}`,
+          in the same order as `assets`. Base64 keeps the JSON free of quoting problems when it is
+          interpolated into the template.
+  - - assets:
+        type: file
+        description: |
+          Every artefact embedded in the report (plot PDFs, count tables, the fitness table and the
+          detail HTML reports). Staged into numbered directories because basenames repeat across
+          samples.
+  - - template_html:
+        type: file
+        description: The report's HTML shell; data, plots and assets are injected into it.
+        pattern: "*.html"
+
+output:
+  - report:
+      - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. `[ id:'test', sample:'ORF1' ]`
+      - "deepmutscan_report.html":
+          type: file
+          description: Self-contained all-in-one report; no sidecar files are needed to view or share it.
+          pattern: "deepmutscan_report.html"
+  - versions:
+      - "versions.yml":
+          type: file
+          description: File containing software versions
+          pattern: "versions.yml"
+
+authors:
+  - "@BenjaminWehnert1008"
+maintainers:
+  - "@BenjaminWehnert1008"
diff --git a/modules/local/visualization/summary_report/templates/build_summary_report.py b/modules/local/visualization/summary_report/templates/build_summary_report.py
new file mode 100644
index 0000000..ea17fc5
--- /dev/null
+++ b/modules/local/visualization/summary_report/templates/build_summary_report.py
@@ -0,0 +1,766 @@
+#!/usr/bin/env python3
+"""Build the self-contained all-in-one HTML report for nf-core/deepmutscan.
+
+Nextflow ``template`` script for the VISUALIZATION_SUMMARY_REPORT module. Everything the report
+shows arrives through one generic mechanism: a base64 JSON manifest describing each staged asset
+(`kind|group|name`) plus the assets themselves, in the same order. Sections that have no entries
+are simply omitted, so the same code serves runs with or without fitness / error correction / a PDB.
+
+Two kinds of payload end up in the HTML:
+  * data  - count tables and the fitness table, aggregated down to what the plots actually need,
+            then rendered natively as interactive SVG in the browser.
+  * blobs - the original R-generated plot PDFs and the standalone detail reports, embedded as
+            base64 data URIs so the single file needs no sidecars to view, download or share.
+
+Note: this is a Nextflow template, so every backslash meant for Python is doubled and literal
+dollar signs are escaped.
+"""
+import base64
+import json
+import platform
+import re
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+MUT = re.compile(r"^([A-Za-z*]+)(\\d+)([A-Za-z*]+)")
+REP_RE = re.compile(r"^rescaled_fitness_rep(\\d+)\$", re.I)
+IN_RE = re.compile(r"^input(\\d+)\$", re.I)
+OUT_RE = re.compile(r"^output(\\d+)\$", re.I)
+
+# Reference data for the citation page. Every entry is transcribed from CITATIONS.md, which is the
+# pipeline's own maintained list - the point is that the report and the repo can never drift apart,
+# and that nothing here is invented. `pkgs` are the names these tools use in the run's versions.yml,
+# which is what decides whether a tool actually ran and therefore appears.
+CITATIONS = [
+    {"id": "deepmutscan", "always": True, "pkgs": [],
+     "authors": "Wehnert B, et al.", "title": "nf-core/deepmutscan",
+     "journal": "", "year": "", "key": "deepmutscan",
+     "note": "bioRxiv preprint, coming soon."},
+    {"id": "nfcore", "always": True, "pkgs": [],
+     "authors": "Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S",
+     "title": "The nf-core framework for community-curated bioinformatics pipelines",
+     "journal": "Nature Biotechnology", "year": "2020", "volume": "38", "number": "3", "pages": "276--278",
+     "doi": "10.1038/s41587-020-0439-x", "key": "Ewels2020"},
+    {"id": "nextflow", "always": True, "pkgs": ["nextflow"],
+     "authors": "Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C",
+     "title": "Nextflow enables reproducible computational workflows",
+     "journal": "Nature Biotechnology", "year": "2017", "volume": "35", "number": "4", "pages": "316--319",
+     "doi": "10.1038/nbt.3820", "key": "DiTommaso2017"},
+    {"id": "fastqc", "pkgs": ["fastqc"],
+     "authors": "Andrews S", "title": "FastQC: A Quality Control Tool for High Throughput Sequence Data",
+     "year": "2010", "url": "https://www.bioinformatics.babraham.ac.uk/projects/fastqc/", "key": "Andrews2010"},
+    {"id": "multiqc", "pkgs": ["multiqc"],
+     "authors": "Ewels P, Magnusson M, Lundin S, K\\u00e4ller M",
+     "title": "MultiQC: summarize analysis results for multiple tools and samples in a single report",
+     "journal": "Bioinformatics", "year": "2016", "volume": "32", "number": "19", "pages": "3047--3048",
+     "doi": "10.1093/bioinformatics/btw354", "key": "Ewels2016"},
+    {"id": "samtools", "pkgs": ["samtools"],
+     "authors": "Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H",
+     "title": "Twelve years of SAMtools and BCFtools",
+     "journal": "GigaScience", "year": "2021", "volume": "10", "number": "2", "pages": "giab008",
+     "doi": "10.1093/gigascience/giab008", "key": "Danecek2021"},
+    # BWA-MEM, not BWA-backtrack: the pipeline runs `bwa mem`, whose citation is the 2013 arXiv
+    # preprint - the same one nf-core's own bwa/mem module points at. Li & Durbin 2009 describes
+    # `bwa aln`, a different algorithm this pipeline never calls.
+    {"id": "bwa", "pkgs": ["bwa"],
+     "authors": "Li H", "title": "Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM",
+     "journal": "arXiv", "year": "2013", "eprint": "1303.3997", "primaryclass": "q-bio.GN",
+     "url": "https://arxiv.org/abs/1303.3997", "key": "Li2013"},
+    {"id": "pysam", "pkgs": ["pysam"],
+     "authors": "Heger A, et al.", "title": "pysam: a Python module for reading, manipulating and writing genomic data sets",
+     "year": "", "url": "https://github.com/pysam-developers/pysam", "key": "pysam"},
+    {"id": "polars", "pkgs": ["polars"],
+     "authors": "Vink R, et al.", "title": "Polars: Lightning-fast DataFrame library for Rust and Python",
+     "year": "", "url": "https://github.com/pola-rs/polars", "key": "polars"},
+    {"id": "biopython", "pkgs": ["biopython"],
+     "authors": "Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJL",
+     "title": "Biopython: freely available Python tools for computational molecular biology and bioinformatics",
+     "journal": "Bioinformatics", "year": "2009", "volume": "25", "number": "11", "pages": "1422--1423",
+     "doi": "10.1093/bioinformatics/btp163", "key": "Cock2009"},
+    {"id": "threedmol", "pkgs": ["3dmol"],
+     "authors": "Rego N, Koes D", "title": "3Dmol.js: molecular visualization with WebGL",
+     "journal": "Bioinformatics", "year": "2015", "volume": "31", "number": "8", "pages": "1322--1324",
+     "doi": "10.1093/bioinformatics/btu829", "key": "Rego2015",
+     "note": "Bundled in the interactive variant effect inspection tool (BSD-3-Clause)."},
+    {"id": "dimsum", "pkgs": ["r-dimsum", "dimsum"],
+     "authors": "Faure AJ, Schmiedel JM, Baeza-Centurion P, Lehner B",
+     "title": "DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies",
+     "journal": "Genome Biology", "year": "2020", "volume": "21", "number": "1", "pages": "207",
+     "doi": "10.1186/s13059-020-02091-3", "key": "Faure2020"},
+    {"id": "mutscan", "pkgs": ["r-mutscan", "mutscan"],
+     "authors": "Soneson C, Bendel AM, Diss G, Stadler MB",
+     "title": "mutscan - a flexible R package for efficient end-to-end analysis of multiplexed assays of variant effect data",
+     "journal": "Genome Biology", "year": "2023", "volume": "24", "number": "1", "pages": "132",
+     "doi": "10.1186/s13059-023-02967-0", "key": "Soneson2023"},
+]
+
+
+class args:
+    run = "$run_b64"
+    manifest = "$manifest_b64"
+    assets = "$assets".split()
+    template = "$template_html"
+    threedmol = "$threedmol_js"          # 3Dmol.js, inlined only when a structure is shown
+    out_html = "deepmutscan_report.html"
+
+
+def b64json(s):
+    return json.loads(base64.b64decode(s).decode("utf-8"))
+
+
+def data_uri(path, mime):
+    return "data:" + mime + ";base64," + base64.b64encode(Path(path).read_bytes()).decode("ascii")
+
+
+# Embedded reports run in a frame whose origin is opaque, and in an opaque origin `localStorage` and
+# `document.cookie` do not return empty - they *throw* SecurityError. MultiQC v1.35 reads both while
+# bootstrapping without a try/catch, so the exception unwinds its ready handler and the report is left
+# frozen on its "Loading report.." placeholder with no plots. Handing it a memory-backed store that
+# never throws is enough; the only thing lost is persistence of the reader's MultiQC preferences.
+# This prepends to the file we embed and does not touch the published multiqc_report.html.
+STORAGE_SHIM = """<script>
+(function () {
+  try { window.localStorage.getItem('__probe__'); } catch (e) {
+    var m = Object.create(null);
+    var s = {
+      getItem: function (k) { return k in m ? m[k] : null; },
+      setItem: function (k, v) { m[k] = String(v); },
+      removeItem: function (k) { delete m[k]; },
+      clear: function () { m = Object.create(null); },
+      key: function (i) { return Object.keys(m)[i] || null; }
+    };
+    Object.defineProperty(s, 'length', { get: function () { return Object.keys(m).length; } });
+    try { Object.defineProperty(window, 'localStorage', { value: s, configurable: true }); } catch (e2) {}
+  }
+  try { void document.cookie; } catch (e) {
+    var jar = '';
+    try {
+      Object.defineProperty(document, 'cookie', {
+        get: function () { return jar; },
+        set: function (v) { jar = String(v).split(';')[0]; },
+        configurable: true
+      });
+    } catch (e2) {}
+  }
+})();
+</script>"""
+
+HEAD_RE = re.compile(r"<head[^>]*>", re.I)
+
+
+def html_for_frame(path):
+    """Read an embedded report and make it survive an opaque origin."""
+    text = Path(path).read_text(encoding="utf-8", errors="surrogateescape")
+    m = HEAD_RE.search(text)
+    if m:
+        text = text[: m.end()] + STORAGE_SHIM + text[m.end():]
+    return text
+
+
+def r6(x):
+    return round(float(x), 6) if x is not None and x == x else None
+
+
+def to_float(x):
+    try:
+        v = float(x)
+        return round(v, 6) if v == v else None
+    except (TypeError, ValueError):
+        return None
+
+
+def wt_orf(fasta_path, reading_frame):
+    """Wildtype ORF nucleotides plus the reference offset they start at.
+
+    --reading_frame is the 1-based inclusive range the pipeline uses everywhere else (e.g.
+    "352-1383"), converted to a 0-based half-open slice here. `start` is kept so nucleotide positions
+    can be reported in reference coordinates, which is what base_mut uses.
+
+    Returns None rather than guessing whenever anything fails to line up: a wrong offset would
+    mislabel every nucleotide change while still looking entirely plausible.
+    """
+    if not fasta_path or not reading_frame:
+        return None
+    try:
+        lo_s, hi_s = str(reading_frame).split("-")
+        lo, hi = int(lo_s), int(hi_s)
+    except (TypeError, ValueError):
+        return None
+    seq = "".join(l.strip() for l in Path(fasta_path).read_text().splitlines()
+                  if l.strip() and not l.startswith(">")).upper()
+    if lo < 1 or hi > len(seq) or hi < lo:
+        return None
+    orf = seq[lo - 1:hi]
+    if len(orf) % 3:
+        return None
+    return {"seq": orf, "start": lo}
+
+
+def parse_pos(s):
+    m = MUT.match(str(s))
+    return (m.group(1), int(m.group(2)), m.group(3)) if m else (None, None, None)
+
+
+def num(df, name):
+    return (pd.to_numeric(df[name], errors="coerce") if name in df.columns
+            else pd.Series([np.nan] * len(df)))
+
+
+def per_position(path, orf_len):
+    """Aggregate a filtered-by-library table to per-ORF-position series.
+
+    Deliberately mirrors `global_position_biases_{counts,cov}.R` so the interactive curves and the
+    pipeline's published PDFs describe the same quantity:
+
+      * coverage  - a property of the position, so it is averaged over the substitutions measured
+                    there (the R comment claims it is constant; on real data it varies by ~0.25%
+                    because reads must span every varying base plus --min_flank, which is small
+                    enough that the mean is the right summary).
+      * counts / counts_per_cov
+                  - averaged *within* a varying-bases class and never pooled across classes. The
+                    classes differ several-fold in both counts and frequency, and their mix changes
+                    along the ORF, so a pooled curve would track composition rather than the library.
+      * R uses na.rm=FALSE for these per-position means, hence skipna=False here.
+
+    Series run over every ORF position 1..orf_len, with None where a class has no variants, so that
+    the rolling mean in the browser sees the same gaps rollapply() does.
+
+    `counts_raw` is only present once error correction has run, and drives the per-sample summary.
+    """
+    df = pd.read_csv(path)
+    pos = df["pos_mut"].astype(str).map(lambda s: parse_pos(s)[1])
+    cov, cnt = num(df, "cov"), num(df, "counts")
+    cpc, raw = num(df, "counts_per_cov"), num(df, "counts_raw")
+    vb = num(df, "varying_bases")
+    d = pd.DataFrame({"pos": pos, "cov": cov, "counts": cnt, "cpc": cpc, "raw": raw, "vb": vb})
+    d = d.dropna(subset=["pos"])
+    d["pos"] = d["pos"].astype(int)
+
+    n = int(orf_len) if orf_len else (int(d["pos"].max()) if len(d) else 0)
+    axis = list(range(1, n + 1))
+
+    cov_by = d.groupby("pos")["cov"].mean(numeric_only=False)
+    positions = [{"pos": p, "cov": r6(cov_by.get(p))} for p in axis]
+
+    classes = {}
+    for k in (1, 2, 3):
+        sub = d[d["vb"] == k]
+        cm = sub.groupby("pos")["counts"].apply(lambda s: s.mean(skipna=False))
+        fm = sub.groupby("pos")["cpc"].apply(lambda s: s.mean(skipna=False))
+        classes[str(k)] = [{"pos": p, "counts": r6(cm.get(p)), "cpc": r6(fm.get(p))} for p in axis]
+
+    ec = None
+    if raw.notna().any():
+        tr, tc = float(np.nansum(raw.values)), float(np.nansum(cnt.values))
+        ec = {
+            "n_variants": int(len(d)),
+            "n_corrected": int((raw.fillna(0) != cnt.fillna(0)).sum()),
+            "total_raw": round(tr, 1),
+            "total_corrected": round(tc, 1),
+            "pct_removed": round((tr - tc) / tr * 100.0, 2) if tr > 0 else 0.0,
+        }
+    return positions, classes, ec
+
+
+def heatmap(path):
+    """Counts-per-coverage grid (position x substituted amino acid) for one sample."""
+    df = pd.read_csv(path)
+    if "position" not in df.columns or "mut_aa" not in df.columns:
+        return None
+    pos = pd.to_numeric(df["position"], errors="coerce")
+    val = num(df, "total_counts_per_cov")
+    cells = [{"p": int(p), "a": str(a), "v": r6(v)}
+             for p, a, v in zip(pos, df["mut_aa"].astype(str), val)
+             if p == p and v == v]
+    wt = {}
+    if "wt_aa" in df.columns:
+        for p, w in zip(pos, df["wt_aa"].astype(str)):
+            if p == p and w and w != "nan":
+                wt[int(p)] = w
+    return {"cells": cells, "wt": wt}
+
+
+def codon_changes(nt_seq_field, pos, wt_nt):
+    """Nucleotide changes behind one amino-acid substitution.
+
+    `nt_seq` holds the full ORF for every distinct coding variant that produces this amino-acid
+    change, comma-separated - an NNK library typically reaches one residue through several codons.
+    Each is diffed against the wildtype ORF over this residue's codon only: changes elsewhere belong
+    to other residues and would be double counted. Returns one entry per distinct codon.
+
+    Reported positions are in REFERENCE coordinates, not ORF-relative ones, because that is the
+    convention `base_mut` uses everywhere else in the pipeline - checked against variantCounts, where
+    4000/4000 variants agree with the reference convention and 0/4000 with the ORF-relative one.
+    Emitting ORF-relative numbers here would be off by the reading frame's start and would silently
+    disagree with the error-correction report.
+    """
+    if not wt_nt or not wt_nt.get("seq") or not nt_seq_field:
+        return []
+    seq_wt, rf_lo = wt_nt["seq"], wt_nt["start"]
+    lo = (int(pos) - 1) * 3
+    if lo + 3 > len(seq_wt):
+        return []
+    wt_codon = seq_wt[lo:lo + 3]
+    out, seen = [], set()
+    for seq in (s.strip() for s in str(nt_seq_field).split(",")):
+        if len(seq) < lo + 3:
+            continue
+        mut_codon = seq[lo:lo + 3]
+        if mut_codon == wt_codon or mut_codon in seen:
+            continue
+        seen.add(mut_codon)
+        changes, positions = [], []
+        for k in range(3):
+            if wt_codon[k] != mut_codon[k]:
+                changes.append(f"{wt_codon[k]}>{mut_codon[k]}")
+                positions.append(rf_lo + lo + k)   # reference coords, as base_mut reports them
+        out.append({"wt_codon": wt_codon, "mut_codon": mut_codon,
+                    "changes": changes, "positions": positions})
+    return out
+
+
+def load_fitness(path, wt_nt=None):
+    """Per-substitution fitness plus its per-position mean, from the default fitness table."""
+    df = pd.read_csv(path, sep="\\t", dtype=str, keep_default_na=False)
+
+    def col(n):
+        return next((c for c in df.columns if c.strip().lower() == n), None)
+
+    fc, sc = col("mean fitness"), col("fitness sd")
+    if not fc:
+        return None
+    # Replicate count is a per-run property, so discover the columns instead of assuming rep1/rep2.
+    rep_cols = sorted((c for c in df.columns if REP_RE.match(c.strip())),
+                      key=lambda c: int(REP_RE.match(c.strip()).group(1)))
+    in_cols = [c for c in df.columns if IN_RE.match(c.strip())]
+    out_cols = [c for c in df.columns if OUT_RE.match(c.strip())]
+    ntc = col("nt_seq")
+    rows, per_pos = [], {}
+    for _, r in df.iterrows():
+        try:
+            p = int(float(r.get("pos", "") or "nan"))
+        except (TypeError, ValueError):
+            continue
+        wt, mut = (r.get("wt_aa", "") or "").strip(), (r.get("mut_aa", "") or "").strip()
+        if not wt or not mut:
+            continue
+        try:
+            f = float(r.get(fc, ""))
+        except (TypeError, ValueError):
+            continue
+        if f != f:
+            continue
+        sd = None
+        try:
+            sd = float(r.get(sc, "")) if sc else None
+            if sd != sd:
+                sd = None
+        except (TypeError, ValueError):
+            sd = None
+        stop = mut == "*" or (r.get("stop", "").strip() not in ("", "0"))
+
+        def tot(cols):
+            s = 0.0
+            got = False
+            for c in cols:
+                try:
+                    s += float(r.get(c, "") or 0)
+                    got = True
+                except (TypeError, ValueError):
+                    pass
+            return s if got else None
+
+        cell = {"p": p, "wt": wt, "a": mut, "f": r6(f), "sd": r6(sd),
+                "syn": mut == wt, "stop": bool(stop)}
+        if rep_cols:
+            cell["reps"] = [to_float(r.get(c, "")) for c in rep_cols]
+        cell["n_in"], cell["n_out"] = tot(in_cols), tot(out_cols)
+        if ntc and wt_nt:
+            nt = codon_changes(r.get(ntc, ""), p, wt_nt)
+            if nt:
+                cell["nt"] = nt
+        rows.append(cell)
+        if mut != wt and not stop:
+            per_pos.setdefault(p, []).append(f)
+    return {"cells": rows}
+
+
+THREE_TO_ONE = {
+    "ALA": "A", "ARG": "R", "ASN": "N", "ASP": "D", "CYS": "C", "GLN": "Q", "GLU": "E",
+    "GLY": "G", "HIS": "H", "ILE": "I", "LEU": "L", "LYS": "K", "MET": "M", "PHE": "F",
+    "PRO": "P", "SER": "S", "THR": "T", "TRP": "W", "TYR": "Y", "VAL": "V",
+}
+
+
+def parse_pdb(pdb_path):
+    """(pdb_text, residues) with residues = [{resnum, aa}] from the CA atoms, ordered by residue number."""
+    text = Path(pdb_path).read_text()
+    residues, seen = [], set()
+    for line in text.splitlines():
+        if not line.startswith("ATOM") or line[12:16].strip() != "CA":
+            continue
+        try:
+            resnum = int(line[22:26])
+        except ValueError:
+            continue
+        if resnum in seen:
+            continue
+        seen.add(resnum)
+        residues.append({"resnum": resnum, "aa": THREE_TO_ONE.get(line[17:20].strip().upper(), "X")})
+    residues.sort(key=lambda r: r["resnum"])
+    return text, residues
+
+
+def best_alignment(wt_seq, struct_residues):
+    """Offset mapping wt position j (0-based) to struct_residues[off0 + j], maximising identity.
+
+    The PDB's residue numbering need not start at the ORF's first residue (AFDB models often carry a
+    signal peptide or a different frame), so the mapping is found by sliding the sequences, exactly as
+    the inspection tool does - the two must agree or the structure would be coloured off by a frame.
+    """
+    struct_seq = "".join(r["aa"] for r in struct_residues)
+    n, m = len(wt_seq), len(struct_seq)
+    best_off, best_matches = 0, -1
+    for off in range(-n + 1, m):
+        matches = sum(1 for j in range(n) if 0 <= off + j < m and struct_seq[off + j] == wt_seq[j])
+        if matches > best_matches:
+            best_matches, best_off = matches, off
+    return best_off, best_matches / max(1, n)
+
+
+def structure_data(pdb_path, wt_seq, fitness):
+    """PDB text plus per-residue mean fitness, keyed by PDB residue number, for the spinning viewer.
+
+    The colour at a residue is the mean fitness over the missense substitutions measured there - the
+    same position summary the fitness heatmap collapses to - so the structure and the heatmap tell one
+    story. Returns None (viewer simply hidden) whenever a structure cannot be trusted: no PDB, no
+    fitness, or an alignment that never lines up.
+    """
+    if not pdb_path or not wt_seq or not fitness or not fitness.get("cells"):
+        return None
+    pdb_text, residues = parse_pdb(pdb_path)
+    if not residues:
+        return None
+    off0, identity = best_alignment(wt_seq, residues)
+    by_pos = {}
+    for c in fitness["cells"]:
+        if c.get("syn") or c.get("stop") or c.get("f") is None:
+            continue
+        by_pos.setdefault(c["p"], []).append(c["f"])
+    pos_mean = {p: sum(v) / len(v) for p, v in by_pos.items() if v}
+    resi_fit = {}
+    for j in range(len(wt_seq)):
+        k = off0 + j
+        if 0 <= k < len(residues) and (j + 1) in pos_mean:
+            resi_fit[residues[k]["resnum"]] = round(pos_mean[j + 1], 4)
+    if not resi_fit:
+        return None
+    fmax = max(abs(v) for v in resi_fit.values()) or 1.0
+    return {"resi_fit": resi_fit, "fmax": round(fmax, 4),
+            "identity": round(identity, 4), "n_residues": len(residues), "pdb": pdb_text}
+
+
+def _dur_s(s):
+    """Nextflow-formatted duration ('7m 37s', '746ms', '1h 2m') to seconds."""
+    s = str(s).strip()
+    if not s or s == "-":
+        return 0.0
+    tot = 0.0
+    for num, unit in re.findall(r"([\\d.]+)\\s*(ms|s|m|h|d)", s):
+        tot += float(num) * {"ms": 0.001, "s": 1, "m": 60, "h": 3600, "d": 86400}[unit]
+    return tot
+
+
+def _size_b(s):
+    """Nextflow-formatted size ('3 GB', '4.9 MB') to bytes."""
+    m = re.match(r"([\\d.]+)\\s*(B|KB|MB|GB|TB)", str(s).strip(), re.I)
+    if not m:
+        return 0.0
+    return float(m.group(1)) * {"B": 1, "KB": 1e3, "MB": 1e6, "GB": 1e9, "TB": 1e12}[m.group(2).upper()]
+
+
+def run_stats(path):
+    """Per-process compute stats from Nextflow's execution trace.
+
+    The trace is written continuously, so at report time it holds every task finished so far - i.e.
+    everything but the report itself and the version collation that follows it. That is the whole
+    compute-heavy body of the run, which is what the page is about; it is labelled 'as of report
+    generation' rather than pretended to be the final total. CACHED tasks are counted separately
+    because they consumed no compute this run.
+    """
+    df = pd.read_csv(path, sep="\\t", dtype=str, keep_default_na=False)
+    if "name" not in df.columns:
+        return None
+
+    def proc(n):
+        return re.sub(r"\\s*\\(.*\\)\\s*\$", "", n).split(":")[-1]  # drop "(sample)" and the module path
+
+    rows = {}
+    for _, r in df.iterrows():
+        p = proc(r.get("name", ""))
+        d = rows.setdefault(p, {"n": 0, "cached": 0, "cpu_time": 0.0, "rss": 0.0, "cpu": [], "submit": ""})
+        d["n"] += 1
+        if r.get("status", "") == "CACHED":
+            d["cached"] += 1
+        d["cpu_time"] += _dur_s(r.get("realtime", ""))
+        d["rss"] = max(d["rss"], _size_b(r.get("peak_rss", "")))
+        # earliest submission time, so the table can read top-to-bottom in run order. The trace timestamp
+        # is "YYYY-MM-DD HH:MM:SS.mmm", which sorts lexically = chronologically, so no parsing is needed.
+        sub = r.get("submit", "")
+        if sub and (not d["submit"] or sub < d["submit"]):
+            d["submit"] = sub
+        c = r.get("%cpu", "").replace("%", "").strip()
+        try:
+            d["cpu"].append(float(c))
+        except ValueError:
+            pass
+    procs = [{
+        "process": p, "tasks": d["n"], "cached": d["cached"],
+        "cpu_time": round(d["cpu_time"], 1), "peak_rss": round(d["rss"]),
+        "cpu_pct": round(sum(d["cpu"]) / len(d["cpu"])) if d["cpu"] else None,
+        "submit": d["submit"],
+    } for p, d in rows.items()]
+    # chronological: first-submitted process first (falls back to name for any row with no timestamp)
+    procs.sort(key=lambda x: (x["submit"] or "~", x["process"]))
+    status = df["status"] if "status" in df.columns else pd.Series([], dtype=str)
+    total = {
+        "tasks": int(df.shape[0]),
+        "cached": int((status == "CACHED").sum()),
+        "cpu_time": round(sum(p["cpu_time"] for p in procs), 1),
+        "peak_rss": round(max((p["peak_rss"] for p in procs), default=0)),
+    }
+    return {"processes": procs, "total": total}
+
+
+def software_versions(path):
+    """Parse pipeline_info's software versions YAML into {process: {package: version}}.
+
+    Hand-parsed rather than via a YAML library: the file is two levels of trivial `key: value` and
+    pandas' container carries no yaml. Duplicate process keys are merged rather than overwritten -
+    they occur in practice when a module fails to interpolate its own name.
+    """
+    out = {}
+    cur = None
+    for line in Path(path).read_text().splitlines():
+        if not line.strip() or line.lstrip().startswith("#"):
+            continue
+        if not line.startswith((" ", "\\t")):
+            cur = line.strip().rstrip(":").strip('"')
+            out.setdefault(cur, {})
+        elif cur:
+            k, _, v = line.strip().partition(":")
+            if k:
+                out[cur][k.strip()] = v.strip()
+    return out
+
+
+def citations(versions, extra=()):
+    """The subset of CITATIONS whose tools actually ran, plus the pipeline and frameworks always.
+
+    Package names in versions.yml are the primary signal, but they are not always sufficient: DiMSum,
+    for instance, reports only `r-base`, so it is invisible at the package level. `extra` carries the
+    tokens that a run's own flags settle unambiguously (--dimsum, --mutscan, a --pdb structure). A tool
+    outside both signals is never cited - the page is about this run, not the pipeline's full
+    repertoire.
+    """
+    seen = {p.strip().lower() for pkgs in versions.values() for p in pkgs}
+    seen |= {e.lower() for e in extra}
+    out = []
+    for c in CITATIONS:
+        if c.get("always") or any(p.lower() in seen for p in c["pkgs"]):
+            out.append({k: v for k, v in c.items() if k != "pkgs"})
+    return out
+
+
+def biblatex(entries):
+    """A biblatex source block for the given entries."""
+    # The braces are assembled from these two names instead of being written literally: nf-core's
+    # `template_strings` lint reads any doubled-brace pair in the source as a leftover Jinja
+    # placeholder, and biblatex needs exactly such a pair around the title to protect its
+    # capitalisation.
+    ob, cb = "{", "}"
+    lines = []
+    for c in entries:
+        kind = "@article"
+        if c.get("eprint"):
+            kind = "@misc"
+        elif not c.get("journal"):
+            kind = "@misc"
+        lines.append(kind + ob + c["key"] + ",")
+        lines.append("  author       = " + ob + c["authors"].replace(", ", " and ") + cb + ",")
+        lines.append("  title        = " + ob + ob + c["title"] + cb + cb + ",")
+        for fld, key in (("journaltitle", "journal"), ("year", "year"), ("volume", "volume"),
+                         ("number", "number"), ("pages", "pages"), ("doi", "doi"),
+                         ("eprint", "eprint"), ("eprintclass", "primaryclass"), ("url", "url")):
+            if c.get(key):
+                lines.append(f"  {fld:<12} = " + ob + str(c[key]) + cb + ",")
+        if c.get("note"):
+            lines.append("  note         = " + ob + c["note"] + cb + ",")
+        lines.append("}")
+        lines.append("")
+    return "\\n".join(lines)
+
+
+def main():
+    run = b64json(args.run)
+    manifest = b64json(args.manifest)
+    if len(manifest) != len(args.assets):
+        raise SystemExit(f"manifest has {len(manifest)} entries but {len(args.assets)} files were staged")
+
+    samples, plots, reports, fitness, dl, versions = {}, {}, [], None, [], {}
+
+    def sample(sid):
+        # type/replicate drive the input-vs-output colour palette and the seqdepth default selection.
+        # Pipeline ids are "<sample>_<type>_<rep>_<pe|se>", so the type is a delimited token - matching it
+        # that way is robust for any protein name (which is a separate, earlier segment).
+        m = re.search(r"_(input|output|wildtype|quality)_(\\d+)", sid)
+        stype = m.group(1) if m else None
+        srep = int(m.group(2)) if m else None
+        return samples.setdefault(sid, {"id": sid, "type": stype, "replicate": srep,
+                                        "positions": [], "classes": {}, "heatmap": None, "ec": None, "seqdepth": None})
+
+    # The ORF length has to come from the wildtype protein sequence, not from the highest position that
+    # happens to carry a variant, or trailing residues with no library coverage would silently shorten
+    # the axis and shift every rolling window.
+    orf_len, wt_nt, wt_aa_seq, pdb_path = 0, None, None, None
+    for entry, path in zip(manifest, args.assets):
+        if entry["kind"] == "aa_seq":
+            wt_aa_seq = Path(path).read_text().strip().split("\\n")[0].strip()
+            orf_len = len(wt_aa_seq)
+        elif entry["kind"] == "fasta":
+            wt_nt = wt_orf(path, run.get("reading_frame"))
+        elif entry["kind"] == "pdb":
+            pdb_path = path
+    # A frame that disagrees with the protein length means the codon diffs would be off by a whole
+    # residue, so drop the nucleotide detail rather than print something plausible and wrong.
+    if wt_nt and orf_len and len(wt_nt["seq"]) // 3 not in (orf_len, orf_len + 1):
+        print(f"WARNING: ORF is {len(wt_nt['seq'])} nt ({len(wt_nt['seq'])//3} codons) but the protein is "
+              f"{orf_len} aa; skipping nucleotide-level detail")
+        wt_nt = None
+
+    for entry, path in zip(manifest, args.assets):
+        kind, group, name = entry["kind"], entry["group"], entry["name"]
+
+        if kind == "qc":                       # per-sample library-QC plot (original R PDF)
+            # Deliberately NOT embedded. The report re-plots these natively and offers the live chart
+            # as SVG/PNG, so carrying ~25 MB of base64 PDF bought nothing but weight - and the weight
+            # is not free: it is what stops the embedded MultiQC report from finishing its render.
+            # Only the location is kept, for the "open the results folder" links.
+            plots.setdefault(group, {})[Path(name).stem] = None
+
+        elif kind == "filtered":               # per-sample counts -> per-position curves + EC summary
+            positions, classes, ec = per_position(path, orf_len)
+            s = sample(group)
+            s["positions"], s["classes"], s["ec"] = positions, classes, ec
+
+        elif kind == "heatmap":                # per-sample counts-per-cov grid
+            sample(group)["heatmap"] = heatmap(path)
+
+        elif kind == "seqdepth":               # per-sample rarefaction curve (depth fraction -> % library)
+            sd = pd.read_csv(path)
+            sample(group)["seqdepth"] = [
+                {"x": r6(x), "y": r6(y)}
+                for x, y in zip(num(sd, "depth_fold"), num(sd, "variants_pct"))
+                if x == x and y == y
+            ]
+
+        elif kind == "fitness_table":
+            fitness = load_fitness(path, wt_nt)
+
+        elif kind in ("fitness_pdf", "mutscan_pdf"):
+            # Same reasoning as the QC PDFs: recorded by name so the page can link to them in the
+            # results folder, not inlined.
+            dl.append({"section": "fitness", "group": group, "name": name})
+
+        elif kind == "report":                 # standalone detail report, embedded for a lazy iframe
+            # Carried as srcdoc text rather than a data: URI. srcdoc inherits this document's origin
+            # instead of minting an opaque one, and it is ~25% smaller than the base64 equivalent.
+            reports.append({"group": group, "name": name, "html": html_for_frame(path)})
+
+        elif kind == "versions":               # pipeline_info software-versions YAML
+            versions = software_versions(path)
+
+        elif kind == "trace":                  # Nextflow execution trace -> per-process compute stats
+            try:
+                run["stats"] = run_stats(path)
+            except Exception as exc:  # a malformed/partial trace must never sink the whole report
+                print(f"WARNING: could not parse execution trace: {exc}")
+
+    # Coverage needed for every residue outcome to reach --aimed_cov counts, assuming all 21 outcomes
+    # are equally represented. Same expression as the reference line in global_position_biases_cov.R;
+    # it is a per-position number because each read spans the whole ORF in this assay.
+    try:
+        run["required_cov"] = 21 * orf_len * float(run.get("aimed_cov") or 0) or None
+    except (TypeError, ValueError):
+        run["required_cov"] = None
+    run["orf_length"] = orf_len
+
+    # Flags settle the tools that the version strings alone cannot: DiMSum reports only r-base, and
+    # 3Dmol.js has no package entry at all - the structure viewer's presence is the tell.
+    def truthy(x):
+        return str(x).strip().lower() in ("true", "1", "yes")
+
+    extra = set()
+    if truthy(run.get("dimsum")):
+        extra.add("dimsum")
+    if truthy(run.get("mutscan")):
+        extra.add("mutscan")
+    if any(r["group"] == "viewer" for r in reports):
+        extra.add("3dmol")
+    if pdb_path:
+        extra.add("3dmol")
+    cites = citations(versions, extra)
+    # The spinning structure needs a PDB, fitness, and the wildtype protein sequence to align them; any
+    # missing and the viewer is simply left out (structure = None) so the page still renders.
+    structure = structure_data(pdb_path, wt_aa_seq, fitness)
+    data = {
+        "run": run,
+        "samples": [samples[k] for k in sorted(samples)],
+        "plots": plots,
+        "reports": reports,
+        "fitness": fitness,
+        "structure": structure,
+        "downloads": dl,
+        # Two citation groups for the page: the pipeline itself (always) and the tools that actually
+        # ran. Each carries its own biblatex block so the "get biblatex" button has nothing to compute.
+        # Section 1 is the pipeline and the frameworks it is built on; section 2 is the analysis tools
+        # this particular run actually invoked.
+        "citations": (lambda pipe: {
+            "pipeline": [c for c in cites if c["id"] in pipe],
+            "tools": [c for c in cites if c["id"] not in pipe],
+            "biblatex_pipeline": biblatex([c for c in cites if c["id"] in pipe]),
+            "biblatex_tools": biblatex([c for c in cites if c["id"] not in pipe]),
+        })({"deepmutscan", "nfcore", "nextflow"}),
+        "software": versions,
+    }
+
+    # 3Dmol.js is ~0.5 MB, so inline it only when a structure will actually be shown; otherwise the
+    # placeholder collapses to nothing and a no-PDB run carries no dead weight.
+    threedmol_js = ""
+    if structure and args.threedmol:
+        try:
+            threedmol_js = Path(args.threedmol).read_text(encoding="utf-8", errors="surrogateescape")
+        except OSError:
+            threedmol_js = ""
+
+    html = (Path(args.template).read_text(encoding="utf-8")
+            .replace("__REPORT_DATA__", json.dumps(data, separators=(",", ":")).replace("</", "<\\\\/"))
+            .replace("/*__THREEDMOL_JS__*/", threedmol_js)
+            .replace("__SAMPLE__", str(run.get("sample", "run"))))
+    Path(args.out_html).write_text(html, encoding="utf-8")
+
+    Path("versions.yml").write_text(
+        '"${task.process}":\\n'
+        + "    python: " + platform.python_version() + "\\n"
+        + "    pandas: " + pd.__version__ + "\\n"
+        + "    numpy: " + np.__version__ + "\\n"
+    )
+    mb = Path(args.out_html).stat().st_size / 1e6
+    print(f"Wrote {args.out_html} ({mb:.1f} MB) - {len(samples)} samples, "
+          f"{sum(len(v) for v in plots.values())} plot PDFs, {len(reports)} embedded reports")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/modules/nf-core/bwa/index/environment.yml b/modules/nf-core/bwa/index/environment.yml
new file mode 100644
index 0000000..54e6794
--- /dev/null
+++ b/modules/nf-core/bwa/index/environment.yml
@@ -0,0 +1,13 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - conda-forge
+  - bioconda
+
+dependencies:
+  # renovate: datasource=conda depName=bioconda/bwa
+  - bioconda::bwa=0.7.19
+  # renovate: datasource=conda depName=bioconda/htslib
+  - bioconda::htslib=1.22.1
+  # renovate: datasource=conda depName=bioconda/samtools
+  - bioconda::samtools=1.22.1
diff --git a/modules/nf-core/bwa/index/main.nf b/modules/nf-core/bwa/index/main.nf
new file mode 100644
index 0000000..a1c98ac
--- /dev/null
+++ b/modules/nf-core/bwa/index/main.nf
@@ -0,0 +1,44 @@
+process BWA_INDEX {
+    tag "$fasta"
+    // NOTE requires 5.37N memory where N is the size of the database
+    // source: https://bio-bwa.sourceforge.net/bwa.shtml#8
+    memory { 7.B * fasta.size() }
+
+    conda "${moduleDir}/environment.yml"
+    container "${ workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container ?
+        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/d7/d7e24dc1e4d93ca4d3a76a78d4c834a7be3985b0e1e56fddd61662e047863a8a/data' :
+        'community.wave.seqera.io/library/bwa_htslib_samtools:83b50ff84ead50d0' }"
+
+    input:
+    tuple val(meta), path(fasta)
+
+    output:
+    tuple val(meta), path("bwa"), emit: index
+    tuple val("${task.process}"), val('bwa'), eval('bwa 2>&1 | sed -n "s/^Version: //p"'), topic: versions, emit: versions_bwa
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def prefix = task.ext.prefix ?: "${fasta.baseName}"
+    def args   = task.ext.args ?: ''
+    """
+    mkdir bwa
+    bwa \\
+        index \\
+        $args \\
+        -p bwa/${prefix} \\
+        $fasta
+    """
+
+    stub:
+    def prefix = task.ext.prefix ?: "${fasta.baseName}"
+    """
+    mkdir bwa
+    touch bwa/${prefix}.amb
+    touch bwa/${prefix}.ann
+    touch bwa/${prefix}.bwt
+    touch bwa/${prefix}.pac
+    touch bwa/${prefix}.sa
+    """
+}
diff --git a/modules/nf-core/bwa/index/meta.yml b/modules/nf-core/bwa/index/meta.yml
new file mode 100644
index 0000000..f5bf7f5
--- /dev/null
+++ b/modules/nf-core/bwa/index/meta.yml
@@ -0,0 +1,71 @@
+name: bwa_index
+description: Create BWA index for reference genome
+keywords:
+  - index
+  - fasta
+  - genome
+  - reference
+tools:
+  - bwa:
+      description: |
+        BWA is a software package for mapping DNA sequences against
+        a large reference genome, such as the human genome.
+      homepage: http://bio-bwa.sourceforge.net/
+      documentation: https://bio-bwa.sourceforge.net/bwa.shtml
+      arxiv: arXiv:1303.3997
+      licence: ["GPL-3.0-or-later"]
+      identifier: "biotools:bwa"
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing reference information.
+          e.g. [ id:'test', single_end:false ]
+    - fasta:
+        type: file
+        description: Input genome fasta file
+        ontologies:
+          - edam: "http://edamontology.org/data_2044" # Sequence
+          - edam: "http://edamontology.org/format_1929" # FASTA
+output:
+  index:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing reference information.
+            e.g. [ id:'test', single_end:false ]
+      - bwa:
+          type: map
+          description: |
+            Groovy Map containing reference information.
+            e.g. [ id:'test', single_end:false ]
+          pattern: "*.{amb,ann,bwt,pac,sa}"
+          ontologies:
+            - edam: "http://edamontology.org/data_3210" # Genome index
+  versions_bwa:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - bwa:
+          type: string
+          description: The tool name
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: string
+          description: The command used to generate the version of the tool
+topics:
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - bwa:
+          type: string
+          description: The tool name
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: string
+          description: The command used to generate the version of the tool
+authors:
+  - "@drpatelh"
+  - "@maxulysse"
+maintainers:
+  - "@maxulysse"
+  - "@gallvp"
diff --git a/modules/nf-core/bwa/index/tests/main.nf.test b/modules/nf-core/bwa/index/tests/main.nf.test
new file mode 100644
index 0000000..f0fba82
--- /dev/null
+++ b/modules/nf-core/bwa/index/tests/main.nf.test
@@ -0,0 +1,57 @@
+nextflow_process {
+
+    name "Test Process BWA_INDEX"
+    tag "modules_nfcore"
+    tag "modules"
+    tag "bwa"
+    tag "bwa/index"
+    script "../main.nf"
+    process "BWA_INDEX"
+
+    test("BWA index") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [id: 'test'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(process.out).match() }
+            )
+        }
+
+    }
+
+    test("BWA index - stub") {
+
+        options "-stub"
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [id: 'test'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                """
+            }
+        }
+
+        then {
+            assert process.success
+            assertAll(
+                { assert snapshot(process.out).match() }
+            )
+        }
+
+    }
+
+}
diff --git a/modules/nf-core/bwa/index/tests/main.nf.test.snap b/modules/nf-core/bwa/index/tests/main.nf.test.snap
new file mode 100644
index 0000000..21a6f73
--- /dev/null
+++ b/modules/nf-core/bwa/index/tests/main.nf.test.snap
@@ -0,0 +1,108 @@
+{
+    "BWA index - stub": {
+        "content": [
+            {
+                "0": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.ann:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.bwt:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.pac:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.sa:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "1": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "index": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.ann:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.bwt:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.pac:md5,d41d8cd98f00b204e9800998ecf8427e",
+                            "genome.sa:md5,d41d8cd98f00b204e9800998ecf8427e"
+                        ]
+                    ]
+                ],
+                "versions_bwa": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.2"
+        },
+        "timestamp": "2026-01-23T16:58:59.966558606"
+    },
+    "BWA index": {
+        "content": [
+            {
+                "0": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,3a68b8b2287e07dd3f5f95f4344ba76e",
+                            "genome.ann:md5,c32e11f6c859f166c7525a9c1d583567",
+                            "genome.bwt:md5,0469c30a1e239dd08f68afe66fde99da",
+                            "genome.pac:md5,983e3d2cd6f36e2546e6d25a0da78d66",
+                            "genome.sa:md5,ab3952cabf026b48cd3eb5bccbb636d1"
+                        ]
+                    ]
+                ],
+                "1": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "index": [
+                    [
+                        {
+                            "id": "test"
+                        },
+                        [
+                            "genome.amb:md5,3a68b8b2287e07dd3f5f95f4344ba76e",
+                            "genome.ann:md5,c32e11f6c859f166c7525a9c1d583567",
+                            "genome.bwt:md5,0469c30a1e239dd08f68afe66fde99da",
+                            "genome.pac:md5,983e3d2cd6f36e2546e6d25a0da78d66",
+                            "genome.sa:md5,ab3952cabf026b48cd3eb5bccbb636d1"
+                        ]
+                    ]
+                ],
+                "versions_bwa": [
+                    [
+                        "BWA_INDEX",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.2"
+        },
+        "timestamp": "2026-01-23T16:58:53.330725134"
+    }
+}
\ No newline at end of file
diff --git a/modules/nf-core/bwa/mem/environment.yml b/modules/nf-core/bwa/mem/environment.yml
new file mode 100644
index 0000000..54e6794
--- /dev/null
+++ b/modules/nf-core/bwa/mem/environment.yml
@@ -0,0 +1,13 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - conda-forge
+  - bioconda
+
+dependencies:
+  # renovate: datasource=conda depName=bioconda/bwa
+  - bioconda::bwa=0.7.19
+  # renovate: datasource=conda depName=bioconda/htslib
+  - bioconda::htslib=1.22.1
+  # renovate: datasource=conda depName=bioconda/samtools
+  - bioconda::samtools=1.22.1
diff --git a/modules/nf-core/bwa/mem/main.nf b/modules/nf-core/bwa/mem/main.nf
new file mode 100644
index 0000000..bde6a9a
--- /dev/null
+++ b/modules/nf-core/bwa/mem/main.nf
@@ -0,0 +1,73 @@
+process BWA_MEM {
+    tag "$meta.id"
+    label 'process_high'
+
+    conda "${moduleDir}/environment.yml"
+    container "${ workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container ?
+        'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/d7/d7e24dc1e4d93ca4d3a76a78d4c834a7be3985b0e1e56fddd61662e047863a8a/data' :
+        'community.wave.seqera.io/library/bwa_htslib_samtools:83b50ff84ead50d0' }"
+
+    input:
+    tuple val(meta) , path(reads)
+    tuple val(meta2), path(index)
+    tuple val(meta3), path(fasta)
+    val   sort_bam
+
+    output:
+    tuple val(meta), path("*.bam")  , emit: bam,    optional: true
+    tuple val(meta), path("*.cram") , emit: cram,   optional: true
+    tuple val(meta), path("*.sam")  , emit: sam,    optional: true
+    tuple val(meta), path("*.csi")  , emit: csi,    optional: true
+    tuple val(meta), path("*.crai") , emit: crai,   optional: true
+    tuple val("${task.process}"), val('bwa'), eval('bwa 2>&1 | sed -n "s/^Version: //p"'), topic: versions, emit: versions_bwa
+    tuple val("${task.process}"), val('samtools'), eval("samtools version | sed '1!d;s/.* //'"), topic: versions, emit: versions_samtools
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args = task.ext.args ?: ''
+    def args2 = task.ext.args2 ?: ''
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    def samtools_command = sort_bam ? 'sort' : 'view'
+    def extension = args2.contains("--output-fmt sam")   ? "sam" :
+                    args2.contains("--output-fmt cram")  ? "cram":
+                    sort_bam && args2.contains("-O cram")? "cram":
+                    !sort_bam && args2.contains("-C")    ? "cram":
+                    "bam"
+    def reference = fasta && extension=="cram"  ? "--reference ${fasta}" : ""
+    if (!fasta && extension=="cram") error "Fasta reference is required for CRAM output"
+    //
+    // For SAM output we can skip samtools view
+    //
+    def pipe_command = ""
+    if (extension == "sam") {
+        pipe_command = "> ${prefix}.${extension}"
+    } else {
+        pipe_command = "| samtools $samtools_command $args2 ${reference} --threads $task.cpus -o ${prefix}.${extension} -"
+    }
+    """
+    INDEX=`find -L ./ -name "*.amb" | sed 's/\\.amb\$//'`
+
+    bwa mem \\
+        $args \\
+        -t $task.cpus \\
+        \$INDEX \\
+        $reads \\
+        $pipe_command
+    """
+
+    stub:
+    def args2 = task.ext.args2 ?: ''
+    def prefix = task.ext.prefix ?: "${meta.id}"
+    def extension = args2.contains("--output-fmt sam")   ? "sam" :
+                    args2.contains("--output-fmt cram")  ? "cram":
+                    sort_bam && args2.contains("-O cram")? "cram":
+                    !sort_bam && args2.contains("-C")    ? "cram":
+                    "bam"
+    """
+    touch ${prefix}.${extension}
+    touch ${prefix}.csi
+    touch ${prefix}.crai
+    """
+}
diff --git a/modules/nf-core/bwa/mem/meta.yml b/modules/nf-core/bwa/mem/meta.yml
new file mode 100644
index 0000000..1c4ee88
--- /dev/null
+++ b/modules/nf-core/bwa/mem/meta.yml
@@ -0,0 +1,159 @@
+name: bwa_mem
+description: Performs fastq alignment to a fasta reference using BWA
+keywords:
+  - mem
+  - bwa
+  - alignment
+  - map
+  - fastq
+  - bam
+  - sam
+tools:
+  - bwa:
+      description: |
+        BWA is a software package for mapping DNA sequences against
+        a large reference genome, such as the human genome.
+      homepage: http://bio-bwa.sourceforge.net/
+      documentation: https://bio-bwa.sourceforge.net/bwa.shtml
+      arxiv: arXiv:1303.3997
+      licence:
+        - "GPL-3.0-or-later"
+      identifier: "biotools:bwa"
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'test', single_end:false ]
+    - reads:
+        type: file
+        description: |
+          List of input FastQ files of size 1 and 2 for single-end and paired-end data,
+          respectively.
+        ontologies:
+          - edam: "http://edamontology.org/data_2044"
+          - edam: "http://edamontology.org/format_1930"
+  - - meta2:
+        type: map
+        description: |
+          Groovy Map containing reference information.
+          e.g. [ id:'test', single_end:false ]
+    - index:
+        type: file
+        description: BWA genome index files
+        pattern: "Directory containing BWA index *.{amb,ann,bwt,pac,sa}"
+        ontologies:
+          - edam: "http://edamontology.org/data_3210"
+  - - meta3:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'test', single_end:false ]
+    - fasta:
+        type: file
+        description: Reference genome in FASTA format
+        pattern: "*.{fasta,fa}"
+        ontologies:
+          - edam: "http://edamontology.org/data_2044"
+          - edam: "http://edamontology.org/format_1929"
+  - sort_bam:
+      type: boolean
+      description: use samtools sort (true) or samtools view (false)
+      pattern: "true or false"
+output:
+  bam:
+    - - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.bam":
+          type: file
+          description: Output BAM file containing read alignments
+          pattern: "*.{bam}"
+          ontologies:
+            - edam: "http://edamontology.org/format_2572"
+  cram:
+    - - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.cram":
+          type: file
+          description: Output CRAM file containing read alignments
+          pattern: "*.{cram}"
+          ontologies:
+            - edam: "http://edamontology.org/format_3462"
+  sam:
+    - - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.sam":
+          type: file
+          description: Output SAM file containing read alignments
+          pattern: "*.{sam}"
+          ontologies:
+            - edam: "http://edamontology.org/format_2573"
+  csi:
+    - - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.csi":
+          type: file
+          description: Optional index file for BAM file
+          pattern: "*.{csi}"
+          ontologies: []
+  crai:
+    - - meta:
+          type: map
+          description: Groovy Map containing sample information
+      - "*.crai":
+          type: file
+          description: Optional index file for CRAM file
+          pattern: "*.{crai}"
+          ontologies: []
+  versions_bwa:
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - bwa:
+          type: string
+          description: The name of the tool
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: eval
+          description: The expression to obtain the version of the tool
+  versions_samtools:
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - samtools version | sed '1!d;s/.* //':
+          type: eval
+          description: The expression to obtain the version of the tool
+topics:
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - bwa:
+          type: string
+          description: The name of the tool
+      - 'bwa 2>&1 | sed -n "s/^Version: //p"':
+          type: eval
+          description: The expression to obtain the version of the tool
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - samtools version | sed '1!d;s/.* //':
+          type: eval
+          description: The expression to obtain the version of the tool
+authors:
+  - "@drpatelh"
+  - "@jeremy1805"
+  - "@matthdsm"
+maintainers:
+  - "@drpatelh"
+  - "@jeremy1805"
+  - "@matthdsm"
diff --git a/modules/nf-core/bwa/mem/tests/main.nf.test b/modules/nf-core/bwa/mem/tests/main.nf.test
new file mode 100644
index 0000000..e284e2e
--- /dev/null
+++ b/modules/nf-core/bwa/mem/tests/main.nf.test
@@ -0,0 +1,343 @@
+nextflow_process {
+
+    name "Test Process BWA_MEM"
+    tag "modules_nfcore"
+    tag "modules"
+    tag "bwa"
+    tag "bwa/mem"
+    tag "bwa/index"
+    script "../main.nf"
+    process "BWA_MEM"
+
+    setup {
+        run("BWA_INDEX") {
+            script "../../index/main.nf"
+            process {
+                """
+                input[0] = [
+                    [id: 'test'],
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)
+                ]
+                """
+            }
+        }
+    }
+
+    test("Single-End") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:true ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.cram,
+                    process.out.sam,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.bam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test("Single-End Sort") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:true ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = true
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.cram,
+                    process.out.sam,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.bam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test("Single-End - SAM output") {
+
+        config "./nextflow_sam.config"
+
+        when {
+            params {
+                module_args2 = "--output-fmt sam"
+            }
+
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:true ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.bam,
+                    process.out.cram,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.sam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test("Paired-End") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.cram,
+                    process.out.sam,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.bam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test("Paired-End Sort") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = true
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.cram,
+                    process.out.sam,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.bam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test("Paired-End - no fasta") {
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[:],[]]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.cram,
+                    process.out.sam,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.bam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test ("Paired-end - SAM output") {
+
+        config "./nextflow_sam.config"
+
+        when {
+            params {
+                module_args2 = "--output-fmt sam"
+            }
+
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[:],[]]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(
+                    process.out.bam,
+                    process.out.cram,
+                    process.out.csi,
+                    process.out.crai,
+                    process.out.findAll { key, val -> key.startsWith("versions") },
+                    bam(process.out.sam[0][1]).getReadsMD5()
+                    ).match()
+                }
+            )
+        }
+
+    }
+
+    test("Single-end - stub") {
+
+        options "-stub"
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:true ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(process.out).match() }
+            )
+        }
+    }
+
+    test("Paired-end - stub") {
+
+        options "-stub"
+
+        when {
+            process {
+                """
+                input[0] = [
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_2.fastq.gz', checkIfExists: true)
+                    ]
+                ]
+                input[1] = BWA_INDEX.out.index
+                input[2] = [[id: 'test'],file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true)]
+                input[3] = false
+                """
+            }
+        }
+
+        then {
+            assertAll(
+                { assert process.success },
+                { assert snapshot(process.out).match() }
+            )
+        }
+    }
+}
diff --git a/modules/nf-core/bwa/mem/tests/main.nf.test.snap b/modules/nf-core/bwa/mem/tests/main.nf.test.snap
new file mode 100644
index 0000000..6f9031d
--- /dev/null
+++ b/modules/nf-core/bwa/mem/tests/main.nf.test.snap
@@ -0,0 +1,478 @@
+{
+    "Single-End - SAM output": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "798439cbd7fd81cbcc5078022dc5479d"
+        ],
+        "timestamp": "2026-05-11T12:09:32.334359515",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Single-End": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "798439cbd7fd81cbcc5078022dc5479d"
+        ],
+        "timestamp": "2026-05-11T12:07:21.233636979",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Single-End Sort": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "94fcf617f5b994584c4e8d4044e16b4f"
+        ],
+        "timestamp": "2026-05-11T12:07:28.74614221",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Paired-End": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "57aeef88ed701a8ebc8e2f0a381b2a6"
+        ],
+        "timestamp": "2026-05-11T12:07:42.612131595",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Paired-End Sort": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "af8628d9df18b2d3d4f6fd47ef2bb872"
+        ],
+        "timestamp": "2026-05-11T12:09:45.938323098",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Single-end - stub": {
+        "content": [
+            {
+                "0": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": true
+                        },
+                        "test.bam:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "1": [
+                    
+                ],
+                "2": [
+                    
+                ],
+                "3": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": true
+                        },
+                        "test.csi:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "4": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": true
+                        },
+                        "test.crai:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "5": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "6": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ],
+                "bam": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": true
+                        },
+                        "test.bam:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "crai": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": true
+                        },
+                        "test.crai:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "cram": [
+                    
+                ],
+                "csi": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": true
+                        },
+                        "test.csi:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "sam": [
+                    
+                ],
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-05-11T12:10:09.92486753",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Paired-End - no fasta": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "57aeef88ed701a8ebc8e2f0a381b2a6"
+        ],
+        "timestamp": "2026-05-11T12:09:52.820539909",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Paired-end - SAM output": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            },
+            "57aeef88ed701a8ebc8e2f0a381b2a6"
+        ],
+        "timestamp": "2026-05-11T12:10:00.199968933",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    },
+    "Paired-end - stub": {
+        "content": [
+            {
+                "0": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.bam:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "1": [
+                    
+                ],
+                "2": [
+                    
+                ],
+                "3": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.csi:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "4": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.crai:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "5": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "6": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ],
+                "bam": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.bam:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "crai": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.crai:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "cram": [
+                    
+                ],
+                "csi": [
+                    [
+                        {
+                            "id": "test",
+                            "single_end": false
+                        },
+                        "test.csi:md5,d41d8cd98f00b204e9800998ecf8427e"
+                    ]
+                ],
+                "sam": [
+                    
+                ],
+                "versions_bwa": [
+                    [
+                        "BWA_MEM",
+                        "bwa",
+                        "0.7.19-r1273"
+                    ]
+                ],
+                "versions_samtools": [
+                    [
+                        "BWA_MEM",
+                        "samtools",
+                        "1.22.1"
+                    ]
+                ]
+            }
+        ],
+        "timestamp": "2026-05-11T12:10:16.940291647",
+        "meta": {
+            "nf-test": "0.9.5",
+            "nextflow": "26.04.0"
+        }
+    }
+}
\ No newline at end of file
diff --git a/modules/nf-core/bwa/mem/tests/nextflow_sam.config b/modules/nf-core/bwa/mem/tests/nextflow_sam.config
new file mode 100644
index 0000000..831332e
--- /dev/null
+++ b/modules/nf-core/bwa/mem/tests/nextflow_sam.config
@@ -0,0 +1,5 @@
+process {
+    withName: BWA_MEM {
+        ext.args2 = params.module_args2
+    }
+}
diff --git a/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c1f4a7982b743963_1.txt b/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
similarity index 75%
rename from modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c1f4a7982b743963_1.txt
rename to modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
index 7619030..2a91c22 100644
--- a/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c1f4a7982b743963_1.txt
+++ b/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
@@ -14,120 +14,118 @@ linux-64:
 - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.5.0-py314h680f03e_0.conda
 - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
 - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.2.25-hbd8a1cb_0.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.2.25-pyhd8ed1ab_0.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.6-pyhd8ed1ab_0.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh8f84b5b_1.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/click-8.4.0-pyhc90fa1f_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/coloredlogs-15.0.1-pyhd8ed1ab_4.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/colormath-3.0.0-pyhd8ed1ab_4.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.3-py314hd8ed1ab_101.conda
-- conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.4-hecca717_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.14.5-py314hd8ed1ab_100.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.8.1-hecca717_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
 - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
 - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
 - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda
-- conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.17.1-h27c8c51_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.0-h27c8c51_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/humanfriendly-10.0-pyh707e725_8.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/humanize-4.15.0-pyhd8ed1ab_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
-- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
-- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.8.0-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.15-pyhcf101f3_0.conda
+- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda
 - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda
 - conda: https://conda.anaconda.org/conda-forge/linux-64/kaleido-core-0.2.1-h3644ca4_0.tar.bz2
-- conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.18-h0c24ade_0.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h0c24ade_0.conda
 - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda
 - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.1.0-hdb68285_0.conda
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-5_h4a7cf45_openblas.conda
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-5_h0358290_openblas.conda
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 sha256: 58d7f40d2940dd0a8aa28651239adbf5613254df0f75789919c4e6762054403b
 md5: 0c96522c6bdaed4b1566d11387caaf45
@@ -314,21 +312,21 @@ license: LicenseRef-Ubuntu-Font-Licence-Version-1.0
 license_family: Other
 size: 1620504
 timestamp: 1727511233259
-- conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.17.1-h27c8c51_0.conda
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-- libuuid >=2.41.3,<3.0a0
-- libzlib >=1.3.1,<2.0a0
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+- libzlib >=1.3.2,<2.0a0
 license: MIT
 license_family: MIT
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 sha256: 54eea8469786bc2291cc40bca5f46438d3e062a399e8f53f013b6a9f50e98333
 md5: a7970cd949a077b7cb9696379d338681
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 license_family: MIT
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 timestamp: 1737618427549
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 license: BSD-3-Clause
 license_family: BSD
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 license: Apache-2.0
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 md5: 04558c96691bed63104678757beb4f8d
@@ -474,18 +461,18 @@ license: MIT
 license_family: MIT
 size: 62099926
 timestamp: 1615199463039
-- conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.18-h0c24ade_0.conda
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 depends:
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 - libgcc >=14
-- libjpeg-turbo >=3.1.2,<4.0a0
+- libjpeg-turbo >=3.1.4.1,<4.0a0
 - libtiff >=4.7.1,<4.8.0a0
 license: MIT
 license_family: MIT
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 md5: 18335a698559cdbcd86150a48bf54ba6
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 license_family: Apache
 size: 261513
 timestamp: 1773113328888
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-5_h4a7cf45_openblas.conda
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-- libopenblas >=0.3.30,<1.0a0
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+- libopenblas >=0.3.33,<1.0a0
 constrains:
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-- liblapack  3.11.0   5*_openblas
-- libcblas   3.11.0   5*_openblas
-- blas 2.305   openblas
-- liblapacke 3.11.0   5*_openblas
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+- liblapack  3.11.0   7*_openblas
+- liblapacke 3.11.0   7*_openblas
+- mkl <2027
 license: BSD-3-Clause
 license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-5_h0358290_openblas.conda
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 constrains:
-- liblapacke 3.11.0   5*_openblas
-- blas 2.305   openblas
-- liblapack  3.11.0   5*_openblas
+- liblapacke 3.11.0   7*_openblas
+- blas 2.307   openblas
+- liblapack  3.11.0   7*_openblas
 license: BSD-3-Clause
 license_family: BSD
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 md5: 6c77a605a7a689d17d4819c0f8ac9a00
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-md5: e7f7ce06ec24cfcfb9e36d28cf82ba57
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 depends:
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 - libgcc >=14
 constrains:
-- expat 2.7.4.*
+- expat 2.8.1.*
 license: MIT
 license_family: MIT
-size: 76798
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 md5: a360c33a5abe61c07959e449fa1453eb
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 license: GPL-2.0-only OR FTL
 size: 384575
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_18.conda
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 depends:
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 constrains:
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-- libgomp 15.2.0 he0feb66_18
+- libgcc-ng ==15.2.0=*_19
+- libgomp 15.2.0 he0feb66_19
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 license_family: GPL
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 constrains:
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+- libgfortran-ng ==15.2.0=*_19
 license: GPL-3.0-only WITH GCC-exception-3.1
 license_family: GPL
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 depends:
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 license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_18.conda
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 depends:
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 constrains:
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 license: IJG AND BSD-3-Clause AND Zlib
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 constrains:
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-- liblapacke 3.11.0   5*_openblas
-- libcblas   3.11.0   5*_openblas
+- liblapacke 3.11.0   7*_openblas
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 license: BSD-3-Clause
 license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.2-hb03c661_0.conda
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 depends:
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 - libgcc >=14
 constrains:
-- xz 5.8.2.*
+- xz 5.8.3.*
 license: 0BSD
-size: 113207
-timestamp: 1768752626120
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 sha256: fe171ed5cf5959993d43ff72de7596e8ac2853e9021dec0344e583734f1e0843
 md5: 2c21e66f50753a083cbe6b80f38268fa
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 license_family: BSD
 size: 92400
 timestamp: 1769482286018
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.30-pthreads_h94d23a6_4.conda
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-md5: be43915efc66345cccb3c310b6ed0374
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 depends:
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 constrains:
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+- openblas >=0.3.33,<0.3.34.0a0
 license: BSD-3-Clause
 license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.55-h421ea60_0.conda
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 depends:
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 license: zlib-acknowledgement
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+- libzlib >=1.3.2,<2.0a0
 license: blessing
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 constrains:
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 license_family: GPL
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 depends:
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 constrains:
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-- polars-runtime-64 ==1.39.3
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 license: MIT
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 constrains:
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 license: MIT
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-- libzlib >=1.3.1,<2.0a0
-- ncurses >=6.5,<7.0a0
-- openssl >=3.5.5,<4.0a0
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 license: Python-2.0
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 python_site_packages_path: lib/python3.14/site-packages
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 sha256: 74e417a768f59f02a242c25e7db0aa796627b5bc8c818863b57786072aeb85e5
@@ -1228,15 +1202,15 @@ license: BSD-3-Clause
 license_family: BSD
 size: 27848
 timestamp: 1772388605021
-- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.14.3-h4df99d1_101.conda
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-md5: 235765e4ea0d0301c75965985163b5a1
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 depends:
-- cpython 3.14.3.*
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 license: Python-2.0
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 license: Apache-2.0 AND CNRI-Python
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 license: Apache-2.0
 license_family: APACHE
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diff --git a/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-db7c73dae76bc9e6_1.txt b/modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-db7c73dae76bc9e6_1.txt
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diff --git a/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-40bf3b435e89dc22_1.txt b/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt
similarity index 74%
rename from modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-40bf3b435e89dc22_1.txt
rename to modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt
index a58231a..3d5b93d 100644
--- a/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-40bf3b435e89dc22_1.txt
+++ b/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt
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-- libstdcxx >=14
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 depends:
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 license: BSD-3-Clause
 license_family: BSD
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-timestamp: 1760286526795
-- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.8.0-pyhcf101f3_0.conda
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-md5: 080594bf4493e6bae2607e65390c520a
+size: 62642
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 depends:
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 - zipp >=3.20
 - python
 license: Apache-2.0
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 - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda
 sha256: fc9ca7348a4f25fed2079f2153ecdcf5f9cf2a0bc36c4172420ca09e1849df7b
 md5: 04558c96691bed63104678757beb4f8d
@@ -469,17 +457,17 @@ license: MIT
 license_family: MIT
 size: 65750397
 timestamp: 1615199465742
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.18-h9d5b58d_0.conda
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-md5: bb960f01525b5e001608afef9d47b79c
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.19.1-h9d5b58d_0.conda
+sha256: 1e5f68e4b36a0e1a278c6dc026bc3d7775518a15832cbc9d7fc1c0e4c47784b1
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 depends:
 - libgcc >=14
-- libjpeg-turbo >=3.1.2,<4.0a0
+- libjpeg-turbo >=3.1.4.1,<4.0a0
 - libtiff >=4.7.1,<4.8.0a0
 license: MIT
 license_family: MIT
-size: 293039
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45.1-default_h1979696_102.conda
 sha256: 7abd913d81a9bf00abb699e8987966baa2065f5132e37e815f92d90fc6bba530
 md5: a21644fc4a83da26452a718dc9468d5f
@@ -501,37 +489,37 @@ license: Apache-2.0
 license_family: Apache
 size: 240444
 timestamp: 1773114901155
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-5_haddc8a3_openblas.conda
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-md5: 5afcea37a46f76ec1322943b3c4dfdc0
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-7_haddc8a3_openblas.conda
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 depends:
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-- libopenblas >=0.3.30,<1.0a0
+- libopenblas >=0.3.33,<0.3.34.0a0
+- libopenblas >=0.3.33,<1.0a0
 constrains:
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-- libcblas   3.11.0   5*_openblas
-- liblapack  3.11.0   5*_openblas
-- liblapacke 3.11.0   5*_openblas
-- blas 2.305   openblas
+- liblapack  3.11.0   7*_openblas
+- libcblas   3.11.0   7*_openblas
+- mkl <2027
+- blas 2.307   openblas
+- liblapacke 3.11.0   7*_openblas
 license: BSD-3-Clause
 license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-5_hd72aa62_openblas.conda
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-- libblas 3.11.0 5_haddc8a3_openblas
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+md5: 90ac57b82c055faa9be25031864b7d8f
+depends:
+- libblas 3.11.0 7_haddc8a3_openblas
 constrains:
-- liblapack  3.11.0   5*_openblas
-- liblapacke 3.11.0   5*_openblas
-- blas 2.305   openblas
+- liblapack  3.11.0   7*_openblas
+- blas 2.307   openblas
+- liblapacke 3.11.0   7*_openblas
 license: BSD-3-Clause
 license_family: BSD
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.25-h1af38f5_0.conda
 sha256: 48814b73bd462da6eed2e697e30c060ae16af21e9fbed30d64feaf0aad9da392
 md5: a9138815598fe6b91a1d6782ca657b0c
@@ -541,17 +529,17 @@ license: MIT
 license_family: MIT
 size: 71117
 timestamp: 1761979776756
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.7.4-hfae3067_0.conda
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-md5: 57f3b3da02a50a1be2a6fe847515417d
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.8.1-hfae3067_0.conda
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 depends:
 - libgcc >=14
 constrains:
-- expat 2.7.4.*
+- expat 2.8.1.*
 license: MIT
 license_family: MIT
-size: 76564
-timestamp: 1771259530958
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 sha256: 3df4c539449aabc3443bbe8c492c01d401eea894603087fca2917aa4e1c2dea9
 md5: 2f364feefb6a7c00423e80dcb12db62a
@@ -581,90 +569,90 @@ constrains:
 license: GPL-2.0-only OR FTL
 size: 422941
 timestamp: 1774301093473
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-15.2.0-h8acb6b2_18.conda
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 depends:
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 constrains:
-- libgcc-ng ==15.2.0=*_18
-- libgomp 15.2.0 h8acb6b2_18
+- libgomp 15.2.0 h8acb6b2_19
+- libgcc-ng ==15.2.0=*_19
 license: GPL-3.0-only WITH GCC-exception-3.1
 license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-15.2.0-he9431aa_18.conda
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 depends:
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 license: GPL-3.0-only WITH GCC-exception-3.1
 license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-15.2.0-he9431aa_18.conda
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 depends:
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+- libgfortran5 15.2.0 h1b7bec0_19
 constrains:
-- libgfortran-ng ==15.2.0=*_18
+- libgfortran-ng ==15.2.0=*_19
 license: GPL-3.0-only WITH GCC-exception-3.1
 license_family: GPL
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 depends:
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 constrains:
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 license_family: GPL
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 depends:
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 constrains:
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 license: IJG AND BSD-3-Clause AND Zlib
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.11.0-5_h88aeb00_openblas.conda
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 constrains:
-- liblapacke 3.11.0   5*_openblas
-- blas 2.305   openblas
-- libcblas   3.11.0   5*_openblas
+- libcblas   3.11.0   7*_openblas
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+- liblapacke 3.11.0   7*_openblas
 license: BSD-3-Clause
 license_family: BSD
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 depends:
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 constrains:
-- xz 5.8.2.*
+- xz 5.8.3.*
 license: 0BSD
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libmpdec-4.0.0-he30d5cf_1.conda
 sha256: 57c0dd12d506e84541c4e877898bd2a59cca141df493d34036f18b2751e0a453
 md5: 7b9813e885482e3ccb1fa212b86d7fd0
@@ -674,49 +662,48 @@ license: BSD-2-Clause
 license_family: BSD
 size: 114056
 timestamp: 1769482343003
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.30-pthreads_h9d3fd7e_4.conda
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 depends:
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 constrains:
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+- openblas >=0.3.33,<0.3.34.0a0
 license: BSD-3-Clause
 license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.55-h1abf092_0.conda
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 depends:
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-- libzlib >=1.3.1,<2.0a0
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 license: zlib-acknowledgement
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.52.0-h10b116e_0.conda
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-- libzlib >=1.3.1,<2.0a0
+- libzlib >=1.3.2,<2.0a0
 license: blessing
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-15.2.0-hef695bb_18.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-15.2.0-hef695bb_19.conda
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 depends:
-- libgcc 15.2.0 h8acb6b2_18
+- libgcc 15.2.0 h8acb6b2_19
 constrains:
-- libstdcxx-ng ==15.2.0=*_18
+- libstdcxx-ng ==15.2.0=*_19
 license: GPL-3.0-only WITH GCC-exception-3.1
 license_family: GPL
-size: 5541411
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.7.1-hdb009f0_1.conda
 sha256: 7ff79470db39e803e21b8185bc8f19c460666d5557b1378d1b1e857d929c6b39
 md5: 8c6fd84f9c87ac00636007c6131e457d
@@ -733,15 +720,15 @@ depends:
 license: HPND
 size: 488407
 timestamp: 1762022048105
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.41.3-h1022ec0_0.conda
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-md5: cf2861212053d05f27ec49c3784ff8bb
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.42.1-h1022ec0_0.conda
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 depends:
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 license: BSD-3-Clause
 license_family: BSD
 size: 43453
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 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.6.0-ha2e29f5_0.conda
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+sha256: 05ef55f09f31eabd0a205f6b065e13fc746675f41924620977692ef0ffe5aad8
+md5: 34ed7bc9febeca70f55b757ca09c354d
 depends:
 - libgcc >=14
 - python >=3.14,<3.15.0a0
@@ -1263,27 +1237,27 @@ depends:
 - python_abi 3.14.* *_cp314
 license: Apache-2.0 AND CNRI-Python
 license_family: PSF
-size: 408097
-timestamp: 1772255205521
-- conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.5-pyhcf101f3_1.conda
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-md5: c65df89a0b2e321045a9e01d1337b182
+size: 409780
+timestamp: 1778374195988
+- conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda
+sha256: 1715246b19c9f85ee022933b4845f2fc14ac9184981b7b7d9b728bec8e9588da
+md5: 4a85203c1d80c1059086ae860836ffb9
 depends:
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-- certifi >=2017.4.17
+- certifi >=2023.5.7
 - charset-normalizer >=2,<4
 - idna >=2.5,<4
-- urllib3 >=1.21.1,<3
+- urllib3 >=1.26,<3
 - python
 constrains:
-- chardet >=3.0.2,<6
+- chardet >=3.0.2,<8
 license: Apache-2.0
 license_family: APACHE
-size: 63602
-timestamp: 1766926974520
-- conda: https://conda.anaconda.org/conda-forge/noarch/rich-14.3.3-pyhcf101f3_0.conda
-sha256: b06ce84d6a10c266811a7d3adbfa1c11f13393b91cc6f8a5b468277d90be9590
-md5: 7a6289c50631d620652f5045a63eb573
+size: 68709
+timestamp: 1778851103479
+- conda: https://conda.anaconda.org/conda-forge/noarch/rich-15.0.0-pyhcf101f3_0.conda
+sha256: 3d6ba2c0fcdac3196ba2f0615b4104e532525ffa1335b50a2878be5ff488814a
+md5: 0242025a3c804966bf71aa04eee82f66
 depends:
 - markdown-it-py >=2.2.0
 - pygments >=2.13.0,<3.0.0
@@ -1292,8 +1266,8 @@ depends:
 - python
 license: MIT
 license_family: MIT
-size: 208472
-timestamp: 1771572730357
+size: 208577
+timestamp: 1775991661559
 - conda: https://conda.anaconda.org/conda-forge/noarch/rich-click-1.9.7-pyh8f84b5b_0.conda
 sha256: aa3fcb167321bae51998de2e94d199109c9024f25a5a063cb1c28d8f1af33436
 md5: 0c20a8ebcddb24a45da89d5e917e6cb9
@@ -1331,19 +1305,18 @@ license: MIT
 license_family: MIT
 size: 22284
 timestamp: 1735770589188
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/sqlite-3.52.0-hf1c7be2_0.conda
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-md5: d9634079df93a65ee045b3c75f35cae1
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/sqlite-3.53.1-he8854b5_0.conda
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 depends:
-- icu >=78.2,<79.0a0
 - libgcc >=14
-- libsqlite 3.52.0 h10b116e_0
-- libzlib >=1.3.1,<2.0a0
-- ncurses >=6.5,<7.0a0
+- libsqlite 3.53.1 h022381a_0
+- libzlib >=1.3.2,<2.0a0
+- ncurses >=6.6,<7.0a0
 - readline >=8.3,<9.0a0
 license: blessing
-size: 209416
-timestamp: 1772818891689
+size: 209964
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 sha256: c1da41c79262b27efa168407cfecc47b20270e5fc071a8307f95a2c85fb94170
 md5: 55bf7b559202236157b14323b40f19e6
@@ -1382,20 +1355,20 @@ depends:
 license: MPL-2.0 and MIT
 size: 94132
 timestamp: 1770153424136
-- conda: https://conda.anaconda.org/conda-forge/noarch/typeguard-4.5.1-pyhd8ed1ab_0.conda
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-md5: 260af1b0a94f719de76b4e14094e9a3b
+- conda: https://conda.anaconda.org/conda-forge/noarch/typeguard-4.5.2-pyhcf101f3_0.conda
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 depends:
-- importlib-metadata >=3.6
-- python >=3.10
-- typing-extensions >=4.10.0
 - typing_extensions >=4.14.0
+- python >=3.10
+- importlib-metadata >=3.6
+- python
 constrains:
 - pytest >=7
 license: MIT
 license_family: MIT
-size: 36838
-timestamp: 1771532971545
+size: 38297
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 - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda
 sha256: 7c2df5721c742c2a47b2c8f960e718c930031663ac1174da67c1ed5999f7938c
 md5: edd329d7d3a4ab45dcf905899a7a6115
@@ -1405,16 +1378,17 @@ license: PSF-2.0
 license_family: PSF
 size: 91383
 timestamp: 1756220668932
-- conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda
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-md5: a0a4a3035667fc34f29bfbd5c190baa6
+- conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhcf101f3_2.conda
+sha256: 8b90d2f19f9458b8c58a55e1fcdc1d90c1603a847a47654d8a454549413ba60a
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 depends:
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+- python
 license: MIT
 license_family: MIT
-size: 18923
-timestamp: 1764158430324
+size: 20935
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 - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda
 sha256: 032271135bca55aeb156cee361c81350c6f3fb203f57d024d7e5a1fc9ef18731
 md5: 0caa1af407ecff61170c9437a808404d
@@ -1431,9 +1405,9 @@ md5: ad659d0a2b3e47e38d829aa8cad2d610
 license: LicenseRef-Public-Domain
 size: 119135
 timestamp: 1767016325805
-- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.6.3-pyhd8ed1ab_0.conda
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-md5: 9272daa869e03efe68833e3dc7a02130
+- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.7.0-pyhd8ed1ab_0.conda
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 depends:
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 - brotli-python >=1.2.0
@@ -1442,8 +1416,8 @@ depends:
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 license: MIT
 license_family: MIT
-size: 103172
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+size: 103560
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 md5: 1c246e1105000c3660558459e2fd6d43
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 license_family: MIT
 size: 88088
 timestamp: 1753484092643
-- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
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 license: MIT
 license_family: MIT
-size: 24194
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 sha256: 638a3a41a4fbfed52d3c60c8ef5a3693b3f12a5b1a3f58fa29f5698d0a0702e2
 md5: f731af71c723065d91b4c01bb822641b
diff --git a/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-d167b8012595a136_1.txt b/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-d167b8012595a136_1.txt
deleted file mode 100644
index f787dbe..0000000
--- a/modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-d167b8012595a136_1.txt
+++ /dev/null
@@ -1,125 +0,0 @@
-
-# This file may be used to create an environment using:
-# $ conda create --name <env> --file <this file>
-# platform: linux-aarch64
-@EXPLICIT
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-https://conda.anaconda.org/conda-forge/linux-aarch64/rpds-py-0.30.0-py314h02b7a91_0.conda#e7f6ed9e60043bb5cbcc527764897f0d
-https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda#870293df500ca7e18bedefa5838a22ab
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda#439cd0f567d697b20a8f45cb70a1005a
-https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda#ada41c863af263cc4c5fcbaff7c3e4dc
-https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-15.2.0-he9431aa_18.conda#4feebd0fbf61075a1a9c2e9b3936c257
-https://conda.anaconda.org/conda-forge/linux-aarch64/mathjax-2.7.7-h8af1aa0_3.tar.bz2#7b08314a6867a9d5648a1c3265e9eb8e
-https://conda.anaconda.org/conda-forge/linux-aarch64/nspr-4.38-h3ad9384_0.conda#6dd4f07147774bf720075a210f8026b9
-https://conda.anaconda.org/conda-forge/linux-aarch64/nss-3.118-h544fa81_0.conda#4540f9570d12db2150f42ba036154552
-https://conda.anaconda.org/conda-forge/linux-aarch64/sqlite-3.53.0-he8854b5_0.conda#ad8164bdeece883b825c50639c0c4725
-https://conda.anaconda.org/conda-forge/linux-aarch64/kaleido-core-0.2.1-he5a581e_0.tar.bz2#4f0d284f5d11e04277b552eb1c172c7f
-https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.1.4.1-he30d5cf_0.conda#a85ba48648f6868016f2741fd9170250
-https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.1.0-h52b7260_0.conda#d13423b06447113a90b5b1366d4da171
-https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.25-h1af38f5_0.conda#a9138815598fe6b91a1d6782ca657b0c
-https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.6.0-ha2e29f5_0.conda#24e92d0942c799db387f5c9d7b81f1af
-https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.7.1-hdb009f0_1.conda#8c6fd84f9c87ac00636007c6131e457d
-https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.18-h9d5b58d_0.conda#bb960f01525b5e001608afef9d47b79c
-https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-h86ecc28_1002.conda#bb5a90c93e3bac3d5690acf76b4a6386
-https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-he30d5cf_1.conda#1c246e1105000c3660558459e2fd6d43
-https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.5-he30d5cf_1.conda#bff06dcde4a707339d66d45d96ceb2e2
-https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.17.0-h262b8f6_0.conda#cd14ee5cca2464a425b1dbfc24d90db2
-https://conda.anaconda.org/conda-forge/noarch/markdown-3.10.2-pyhcf101f3_0.conda#ba0a9221ce1063f31692c07370d062f3
-https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda#592132998493b3ff25fd7479396e8351
-https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-4.0.0-pyhd8ed1ab_0.conda#5b5203189eb668f042ac2b0826244964
-https://conda.anaconda.org/conda-forge/noarch/natsort-8.4.0-pyhcf101f3_2.conda#e941e85e273121222580723010bd4fa2
-https://conda.anaconda.org/conda-forge/noarch/packaging-26.1-pyhc364b38_0.conda#b8ae38639d323d808da535fb71e31be8
-https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.4-h5da879a_0.conda#cea962410e327262346d48d01f05936c
-https://conda.anaconda.org/conda-forge/linux-aarch64/zlib-ng-2.3.3-ha7cb516_1.conda#f731af71c723065d91b4c01bb822641b
-https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-12.2.0-py314hac3e5ec_0.conda#87d58d103b47c4a8567b3d7666647684
-https://conda.anaconda.org/conda-forge/noarch/narwhals-2.20.0-pyhcf101f3_0.conda#6cac1a50359219d786453c6fef819f98
-https://conda.anaconda.org/conda-forge/noarch/plotly-6.6.0-pyhd8ed1ab_0.conda#3e9427ee186846052e81fadde8ebe96a
-https://conda.anaconda.org/conda-forge/linux-aarch64/polars-runtime-32-1.40.0-py310hff09b76_0.conda#d5628a33ce7652511e38fc98643dc910
-https://conda.anaconda.org/conda-forge/noarch/polars-1.40.0-pyh58ad624_0.conda#fd16be490f5403adfbf27dd4901bbe34
-https://conda.anaconda.org/conda-forge/linux-aarch64/polars-runtime-compat-1.40.0-py310hf00a4a2_0.conda#a82af0fcbb72db253dc89a7a45279372
-https://conda.anaconda.org/conda-forge/noarch/polars-lts-cpu-1.34.0.deprecated-hc364b38_0.conda#ef0340e75068ac8ff96462749b5c98e7
-https://conda.anaconda.org/conda-forge/linux-aarch64/yaml-0.2.5-h80f16a2_3.conda#032d8030e4a24fe1f72c74423a46fb88
-https://conda.anaconda.org/conda-forge/linux-aarch64/pyyaml-6.0.3-py314h807365f_1.conda#9ae2c92975118058bd720e9ba2bb7c58
-https://conda.anaconda.org/conda-forge/noarch/pyaml-env-1.2.2-pyhd8ed1ab_0.conda#e17be1016bcc3516827b836cd3e4d9dc
-https://conda.anaconda.org/conda-forge/linux-aarch64/pydantic-core-2.46.3-py314h451b6cc_0.conda#1a2cb55be9a153ad6203bff6b787c240
-https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda#a0a4a3035667fc34f29bfbd5c190baa6
-https://conda.anaconda.org/conda-forge/noarch/pydantic-2.13.3-pyhcf101f3_0.conda#f690e6f204efd2e5c06b57518a383d98
-https://conda.anaconda.org/conda-forge/noarch/python-dotenv-1.2.2-pyhcf101f3_0.conda#130584ad9f3a513cdd71b1fdc1244e9c
-https://conda.anaconda.org/conda-forge/noarch/python-kaleido-0.2.1-pyhd8ed1ab_0.tar.bz2#310259a5b03ff02289d7705f39e2b1d2
-https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac
-https://conda.anaconda.org/conda-forge/noarch/urllib3-2.6.3-pyhd8ed1ab_0.conda#9272daa869e03efe68833e3dc7a02130
-https://conda.anaconda.org/conda-forge/noarch/requests-2.33.1-pyhcf101f3_0.conda#10afbb4dbf06ff959ad25a92ccee6e59
-https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda#16c18772b340887160c79a6acc022db0
-https://conda.anaconda.org/conda-forge/noarch/rich-15.0.0-pyhcf101f3_0.conda#0242025a3c804966bf71aa04eee82f66
-https://conda.anaconda.org/conda-forge/noarch/rich-click-1.9.7-pyh8f84b5b_0.conda#0c20a8ebcddb24a45da89d5e917e6cb9
-https://conda.anaconda.org/conda-forge/noarch/spectra-0.0.11-pyhd8ed1ab_2.conda#472239e4eb7b5a84bb96b3ed7e3a596a
-https://conda.anaconda.org/conda-forge/linux-aarch64/regex-2026.4.4-py314h51f160d_0.conda#88a3dbd279e6b1faf0cddb8397866864
-https://conda.anaconda.org/conda-forge/linux-aarch64/tiktoken-0.12.0-py314h6a36e60_3.conda#55bf7b559202236157b14323b40f19e6
-https://conda.anaconda.org/conda-forge/noarch/tqdm-4.67.3-pyh8f84b5b_0.conda#e5ce43272193b38c2e9037446c1d9206
-https://conda.anaconda.org/conda-forge/noarch/typeguard-4.5.1-pyhd8ed1ab_0.conda#260af1b0a94f719de76b4e14094e9a3b
-https://conda.anaconda.org/bioconda/noarch/multiqc-1.34-pyhdfd78af_0.conda#a7111ab9a6a6146b40cbce16655ac873
-https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda#09a970fbf75e8ed1aa633827ded6aa4f
-https://conda.anaconda.org/conda-forge/linux-aarch64/procps-ng-4.0.6-h1779866_0.conda#ab7288cc39545556d1bc5e71ab2df9a9
diff --git a/modules/nf-core/multiqc/environment.yml b/modules/nf-core/multiqc/environment.yml
index 37e7612..7a970e2 100644
--- a/modules/nf-core/multiqc/environment.yml
+++ b/modules/nf-core/multiqc/environment.yml
@@ -4,4 +4,4 @@ channels:
   - conda-forge
   - bioconda
 dependencies:
-  - bioconda::multiqc=1.34
+  - bioconda::multiqc=1.35
diff --git a/modules/nf-core/multiqc/main.nf b/modules/nf-core/multiqc/main.nf
index e80e8cd..c4bc715 100644
--- a/modules/nf-core/multiqc/main.nf
+++ b/modules/nf-core/multiqc/main.nf
@@ -4,8 +4,8 @@ process MULTIQC {
 
     conda "${moduleDir}/environment.yml"
     container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
-        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1b/1bef8af6be88c5733461959c46ac8ef73d18f65277f62a1695d0e1633054f9c2/data'
-        : 'community.wave.seqera.io/library/multiqc:1.34--db7c73dae76bc9e6'}"
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/c8/c8e346f4f6080eadf1253505e6ff09ef004454fc18e8d672006fd7b222cc412e/data'
+        : 'community.wave.seqera.io/library/multiqc:1.35--c17fb751507e9dfc'}"
 
     input:
     tuple val(meta), path(multiqc_files, stageAs: "?/*"), path(multiqc_config, stageAs: "?/*"), path(multiqc_logo), path(replace_names), path(sample_names)
diff --git a/modules/nf-core/multiqc/meta.yml b/modules/nf-core/multiqc/meta.yml
index 2facc62..27ce18d 100644
--- a/modules/nf-core/multiqc/meta.yml
+++ b/modules/nf-core/multiqc/meta.yml
@@ -110,24 +110,24 @@ maintainers:
 containers:
   conda:
     linux/amd64:
-      lock_file: modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-db7c73dae76bc9e6_1.txt
+      lock_file: modules/nf-core/multiqc/.conda-lock/linux_amd64-bd-c17fb751507e9dfc_1.txt
     linux/arm64:
-      lock_file: modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-d167b8012595a136_1.txt
+      lock_file: modules/nf-core/multiqc/.conda-lock/linux_arm64-bd-5c84a5000a226ab5_1.txt
   docker:
     linux/amd64:
-      name: community.wave.seqera.io/library/multiqc:1.34--db7c73dae76bc9e6
-      build_id: bd-db7c73dae76bc9e6_1
-      scan_id: sc-66fc7138dbf1cf48_1
+      name: community.wave.seqera.io/library/multiqc:1.35--c17fb751507e9dfc
+      build_id: bd-c17fb751507e9dfc_1
+      scan_id: sc-3b1b3932f9846892_1
     linux/arm64:
-      name: community.wave.seqera.io/library/multiqc:1.34--d167b8012595a136
-      build_id: bd-d167b8012595a136_1
-      scan_id: sc-ac701dfa631a2af9_1
+      name: community.wave.seqera.io/library/multiqc:1.35--5c84a5000a226ab5
+      build_id: bd-5c84a5000a226ab5_1
+      scan_id: sc-0d39df41e9737bbd_1
   singularity:
     linux/amd64:
-      name: oras://community.wave.seqera.io/library/multiqc:1.34--4fc8657c816047c0
-      build_id: bd-4fc8657c816047c0_1
-      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/1b/1bef8af6be88c5733461959c46ac8ef73d18f65277f62a1695d0e1633054f9c2/data
+      name: oras://community.wave.seqera.io/library/multiqc:1.35--c680f2aea25ccec2
+      build_id: bd-c680f2aea25ccec2_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/c8/c8e346f4f6080eadf1253505e6ff09ef004454fc18e8d672006fd7b222cc412e/data
     linux/arm64:
-      name: oras://community.wave.seqera.io/library/multiqc:1.34--7fbd82d945c06726
-      build_id: bd-7fbd82d945c06726_1
-      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/9a/9a1fec9662a152683e6fcae440d0ce20920b3b89dc62d1e3a52e73f92eba0969/data
+      name: oras://community.wave.seqera.io/library/multiqc:1.35--c0468833d65b2f81
+      build_id: bd-c0468833d65b2f81_1
+      https: https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/e4/e48aa28aebc881254a499b24c3e1ce77b8df1b85a5432699ed6f72eb17ac7fb5/data
diff --git a/modules/nf-core/multiqc/tests/main.nf.test.snap b/modules/nf-core/multiqc/tests/main.nf.test.snap
index 7c2f370..4489921 100644
--- a/modules/nf-core/multiqc/tests/main.nf.test.snap
+++ b/modules/nf-core/multiqc/tests/main.nf.test.snap
@@ -81,7 +81,7 @@
                     [
                         "MULTIQC",
                         "multiqc",
-                        "1.34"
+                        "1.35"
                     ]
                 ]
             }
@@ -175,7 +175,7 @@
                     [
                         "MULTIQC",
                         "multiqc",
-                        "1.34"
+                        "1.35"
                     ]
                 ]
             }
@@ -221,7 +221,7 @@
                     [
                         "MULTIQC",
                         "multiqc",
-                        "1.34"
+                        "1.35"
                     ]
                 ]
             }
@@ -314,7 +314,7 @@
                     [
                         "MULTIQC",
                         "multiqc",
-                        "1.34"
+                        "1.35"
                     ]
                 ]
             }
@@ -408,7 +408,7 @@
                     [
                         "MULTIQC",
                         "multiqc",
-                        "1.34"
+                        "1.35"
                     ]
                 ]
             }
diff --git a/modules/nf-core/samtools/index/environment.yml b/modules/nf-core/samtools/index/environment.yml
new file mode 100644
index 0000000..946bb36
--- /dev/null
+++ b/modules/nf-core/samtools/index/environment.yml
@@ -0,0 +1,10 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  # renovate: datasource=conda depName=bioconda/htslib
+  - bioconda::htslib=1.23.1
+  # renovate: datasource=conda depName=bioconda/samtools
+  - bioconda::samtools=1.23.1
diff --git a/modules/nf-core/samtools/index/main.nf b/modules/nf-core/samtools/index/main.nf
new file mode 100644
index 0000000..5b5756b
--- /dev/null
+++ b/modules/nf-core/samtools/index/main.nf
@@ -0,0 +1,38 @@
+process SAMTOOLS_INDEX {
+    tag "${meta.id}"
+    label 'process_low'
+
+    conda "${moduleDir}/environment.yml"
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/8c/8c5d2818c8b9f58e1fba77ce219fdaf32087ae53e857c4a496402978af26e78c/data'
+        : 'community.wave.seqera.io/library/htslib_samtools:1.23.1--5b6bb4ede7e612e5'}"
+
+    input:
+    tuple val(meta), path(input)
+
+    output:
+    tuple val(meta), path("*.{bai,csi,crai}"), emit: index
+    tuple val("${task.process}"), val('samtools'), eval("samtools version | sed '1!d;s/.* //'"), emit: versions_samtools, topic: versions
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args = task.ext.args ?: ''
+    """
+    samtools \\
+        index \\
+        -@ ${task.cpus} \\
+        ${args} \\
+        ${input}
+    """
+
+    stub:
+    def args = task.ext.args ?: ''
+    def extension = file(input).getExtension() == 'cram'
+        ? "crai"
+        : args.contains("-c") ? "csi" : "bai"
+    """
+    touch ${input}.${extension}
+    """
+}
diff --git a/modules/nf-core/samtools/index/meta.yml b/modules/nf-core/samtools/index/meta.yml
new file mode 100644
index 0000000..d4938bc
--- /dev/null
+++ b/modules/nf-core/samtools/index/meta.yml
@@ -0,0 +1,70 @@
+name: samtools_index
+description: Index SAM/BAM/CRAM file
+keywords:
+  - index
+  - bam
+  - sam
+  - cram
+tools:
+  - samtools:
+      description: |
+        SAMtools is a set of utilities for interacting with and post-processing
+        short DNA sequence read alignments in the SAM, BAM and CRAM formats, written by Heng Li.
+        These files are generated as output by short read aligners like BWA.
+      homepage: http://www.htslib.org/
+      documentation: http://www.htslib.org/doc/samtools.html
+      doi: 10.1093/bioinformatics/btp352
+      licence:
+        - "MIT"
+      identifier: biotools:samtools
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'test', single_end:false ]
+    - input:
+        type: file
+        description: input file
+        ontologies: []
+output:
+  index:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "*.{bai,csi,crai}":
+          type: file
+          description: BAM/CRAM/SAM index file
+          pattern: "*.{bai,csi,crai}"
+          ontologies: []
+  versions_samtools:
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - samtools version | sed '1!d;s/.* //':
+          type: eval
+          description: The expression to obtain the version of the tool
+topics:
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The name of the process
+      - samtools:
+          type: string
+          description: The name of the tool
+      - samtools version | sed '1!d;s/.* //':
+          type: eval
+          description: The expression to obtain the version of the tool
+authors:
+  - "@drpatelh"
+  - "@ewels"
+  - "@maxulysse"
+maintainers:
+  - "@ewels"
+  - "@maxulysse"
+  - "@matthdsm"
diff --git a/modules/nf-core/samtools/index/tests/csi.nextflow.config b/modules/nf-core/samtools/index/tests/csi.nextflow.config
new file mode 100644
index 0000000..4af6d82
--- /dev/null
+++ b/modules/nf-core/samtools/index/tests/csi.nextflow.config
@@ -0,0 +1,6 @@
+process {
+
+    withName: SAMTOOLS_INDEX {
+        ext.args = '-c'
+    }
+}
diff --git a/modules/nf-core/samtools/index/tests/main.nf.test b/modules/nf-core/samtools/index/tests/main.nf.test
new file mode 100644
index 0000000..f9d3922
--- /dev/null
+++ b/modules/nf-core/samtools/index/tests/main.nf.test
@@ -0,0 +1,155 @@
+nextflow_process {
+
+    name "Test Process SAMTOOLS_INDEX"
+    script "../main.nf"
+    process "SAMTOOLS_INDEX"
+    tag "modules"
+    tag "modules_nfcore"
+    tag "samtools"
+    tag "samtools/index"
+
+    test("bai") {
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true)
+                ])
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.index,
+                    process.out.findAll { key, val -> key.startsWith('versions') }
+                ).match() }
+            )
+        }
+    }
+
+    test("crai") {
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/cram/test.paired_end.recalibrated.sorted.cram', checkIfExists: true)
+                ])
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.index,
+                    process.out.findAll { key, val -> key.startsWith('versions') }
+                ).match() }
+            )
+        }
+    }
+
+    test("csi") {
+        config "./csi.nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true)
+                ])
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    file(process.out.index[0][1]).name,
+                    process.out.findAll { key, val -> key.startsWith('versions') }
+                ).match() }
+            )
+        }
+    }
+
+    test("bai - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true)
+                ])
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.index,
+                    process.out.findAll { key, val -> key.startsWith('versions') }
+                ).match() }
+            )
+        }
+    }
+
+    test("crai - stub") {
+        options "-stub"
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/cram/test.paired_end.recalibrated.sorted.cram', checkIfExists: true)
+                ])
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.index,
+                    process.out.findAll { key, val -> key.startsWith('versions') }
+                ).match() }
+            )
+        }
+    }
+
+    test("csi - stub") {
+        options "-stub"
+        config "./csi.nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true)
+                ])
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.index,
+                    process.out.findAll { key, val -> key.startsWith('versions') }
+                ).match() }
+            )
+        }
+    }
+}
diff --git a/modules/nf-core/samtools/index/tests/main.nf.test.snap b/modules/nf-core/samtools/index/tests/main.nf.test.snap
new file mode 100644
index 0000000..337dec7
--- /dev/null
+++ b/modules/nf-core/samtools/index/tests/main.nf.test.snap
@@ -0,0 +1,156 @@
+{
+    "csi - stub": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.paired_end.sorted.bam.csi:md5,d41d8cd98f00b204e9800998ecf8427e"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_INDEX",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:00:39.171613"
+    },
+    "crai - stub": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.paired_end.recalibrated.sorted.cram.crai:md5,d41d8cd98f00b204e9800998ecf8427e"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_INDEX",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:00:32.838795"
+    },
+    "bai - stub": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.paired_end.sorted.bam.bai:md5,d41d8cd98f00b204e9800998ecf8427e"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_INDEX",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:00:25.255379"
+    },
+    "csi": {
+        "content": [
+            "test.paired_end.sorted.bam.csi",
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_INDEX",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:00:18.414839"
+    },
+    "crai": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.paired_end.recalibrated.sorted.cram.crai:md5,14bc3bd5c89cacc8f4541f9062429029"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_INDEX",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:00:13.571297"
+    },
+    "bai": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.paired_end.sorted.bam.bai:md5,704c10dd1326482448ca3073fdebc2f4"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_INDEX",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:00:06.767362"
+    }
+}
\ No newline at end of file
diff --git a/modules/nf-core/samtools/sort/environment.yml b/modules/nf-core/samtools/sort/environment.yml
new file mode 100644
index 0000000..946bb36
--- /dev/null
+++ b/modules/nf-core/samtools/sort/environment.yml
@@ -0,0 +1,10 @@
+---
+# yaml-language-server: $schema=https://raw.githubusercontent.com/nf-core/modules/master/modules/environment-schema.json
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  # renovate: datasource=conda depName=bioconda/htslib
+  - bioconda::htslib=1.23.1
+  # renovate: datasource=conda depName=bioconda/samtools
+  - bioconda::samtools=1.23.1
diff --git a/modules/nf-core/samtools/sort/main.nf b/modules/nf-core/samtools/sort/main.nf
new file mode 100644
index 0000000..e75ce91
--- /dev/null
+++ b/modules/nf-core/samtools/sort/main.nf
@@ -0,0 +1,97 @@
+process SAMTOOLS_SORT {
+    tag "${meta.id}"
+    label 'process_medium'
+
+    conda "${moduleDir}/environment.yml"
+    container "${workflow.containerEngine in ['singularity', 'apptainer'] && !task.ext.singularity_pull_docker_container
+        ? 'https://community-cr-prod.seqera.io/docker/registry/v2/blobs/sha256/8c/8c5d2818c8b9f58e1fba77ce219fdaf32087ae53e857c4a496402978af26e78c/data'
+        : 'community.wave.seqera.io/library/htslib_samtools:1.23.1--5b6bb4ede7e612e5'}"
+
+    input:
+    tuple val(meta), path(bam)
+    tuple val(meta2), path(fasta), path(fai)
+    val index_format
+
+    output:
+    tuple val(meta), path("${prefix}.bam"), emit: bam, optional: true
+    tuple val(meta), path("${prefix}.cram"), emit: cram, optional: true
+    tuple val(meta), path("${prefix}.sam"), emit: sam, optional: true
+    tuple val(meta), path("${prefix}.${extension}.{crai,csi,bai}"), emit: index, optional: true
+    tuple val("${task.process}"), val('samtools'), eval("samtools version | sed '1!d;s/.* //'"), topic: versions, emit: versions_samtools
+
+    when:
+    task.ext.when == null || task.ext.when
+
+    script:
+    def args = task.ext.args ?: ''
+    prefix = task.ext.prefix ?: "${meta.id}"
+    extension = args.contains("--output-fmt sam")
+        ? "sam"
+        : args.contains("--output-fmt cram")
+            ? "cram"
+            : "bam"
+    def reference = fasta ? "--reference ${fasta}" : ""
+    //setting default values
+    def write_index = ""
+    def output_file = "${prefix}.${extension}"
+
+    // Update if index is requested
+    if (index_format != '' && index_format) {
+        write_index = "--write-index"
+        output_file = "${prefix}.${extension}##idx##${prefix}.${extension}.${index_format}"
+    }
+    def is_sam = (bam instanceof List ? bam[0] : bam).name.endsWith('.sam')
+    if (index_format) {
+        if (!index_format.matches('bai|csi|crai')) {
+            error("Index format not one of bai, csi, crai.")
+        }
+        else if (extension == "sam") {
+            error("Indexing not compatible with SAM output")
+        }
+    }
+    if ("${bam}" == "${prefix}.bam") {
+        error("Input and output names are the same, use \"task.ext.prefix\" to disambiguate!")
+    }
+    if ("${bam}" == "${prefix}.bam") {
+        error("Input and output names are the same, use \"task.ext.prefix\" to disambiguate!")
+    }
+
+    def input_source = is_sam ? "${bam}" : "-"
+    def pre_command = is_sam ? "" : "samtools cat ${bam} | "
+
+    """
+    ${pre_command}samtools sort \\
+        ${args} \\
+        -T ${prefix} \\
+        --threads ${task.cpus} \\
+        ${reference} \\
+        -o ${output_file} \\
+        ${write_index} \\
+        ${input_source}
+    """
+
+    stub:
+    def args = task.ext.args ?: ''
+    prefix = task.ext.prefix ?: "${meta.id}"
+    extension = args.contains("--output-fmt sam")
+        ? "sam"
+        : args.contains("--output-fmt cram")
+            ? "cram"
+            : "bam"
+
+    if (index_format) {
+        if (!index_format.matches('bai|csi|crai')) {
+            error("Index format not one of bai, csi, crai.")
+        }
+        else if (extension == "sam") {
+            error("Indexing not compatible with SAM output")
+        }
+    }
+
+    index = index_format ? "touch ${prefix}.${extension}.${index_format}" : ""
+
+    """
+    touch ${prefix}.${extension}
+    ${index}
+    """
+}
diff --git a/modules/nf-core/samtools/sort/meta.yml b/modules/nf-core/samtools/sort/meta.yml
new file mode 100644
index 0000000..0447a95
--- /dev/null
+++ b/modules/nf-core/samtools/sort/meta.yml
@@ -0,0 +1,126 @@
+name: samtools_sort
+description: Sort SAM/BAM/CRAM file
+keywords:
+  - sort
+  - bam
+  - sam
+  - cram
+tools:
+  - samtools:
+      description: |
+        SAMtools is a set of utilities for interacting with and post-processing
+        short DNA sequence read alignments in the SAM, BAM and CRAM formats, written by Heng Li.
+        These files are generated as output by short read aligners like BWA.
+      homepage: http://www.htslib.org/
+      documentation: http://www.htslib.org/doc/samtools.html
+      doi: 10.1093/bioinformatics/btp352
+      licence: ["MIT"]
+      identifier: biotools:samtools
+input:
+  - - meta:
+        type: map
+        description: |
+          Groovy Map containing sample information
+          e.g. [ id:'test', single_end:false ]
+    - bam:
+        type: file
+        description: BAM/CRAM/SAM file(s)
+        pattern: "*.{bam,cram,sam}"
+        ontologies: []
+  - - meta2:
+        type: map
+        description: |
+          Groovy Map containing reference information
+          e.g. [ id:'genome' ]
+    - fasta:
+        type: file
+        description: Reference genome FASTA file
+        pattern: "*.{fa,fasta,fna}"
+        optional: true
+        ontologies: []
+    - fai:
+        type: file
+        description: Reference genome FASTA index file
+        pattern: "*.{fai}"
+        optional: true
+        ontologies: []
+  - index_format:
+      type: string
+      description: Index format to use (optional)
+      pattern: "bai|csi|crai"
+output:
+  bam:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "${prefix}.bam":
+          type: file
+          description: Sorted BAM file
+          pattern: "*.{bam}"
+          ontologies: []
+  cram:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "${prefix}.cram":
+          type: file
+          description: Sorted CRAM file
+          pattern: "*.{cram}"
+          ontologies: []
+  sam:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "${prefix}.sam":
+          type: file
+          description: Sorted SAM file
+          pattern: "*.{sam}"
+          ontologies: []
+  index:
+    - - meta:
+          type: map
+          description: |
+            Groovy Map containing sample information
+            e.g. [ id:'test', single_end:false ]
+      - "${prefix}.${extension}.{crai,csi,bai}":
+          type: file
+          description: CRAM index file (optional)
+          pattern: "*.{crai,csi,bai}"
+          ontologies: []
+  versions_samtools:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - samtools:
+          type: string
+          description: The tool name
+      - "samtools version | sed '1!d;s/.* //'":
+          type: string
+          description: The command used to generate the version of the tool
+
+topics:
+  versions:
+    - - ${task.process}:
+          type: string
+          description: The process the versions were collected from
+      - samtools:
+          type: string
+          description: The tool name
+      - "samtools version | sed '1!d;s/.* //'":
+          type: string
+          description: The command used to generate the version of the tool
+
+authors:
+  - "@drpatelh"
+  - "@ewels"
+  - "@matthdsm"
+maintainers:
+  - "@drpatelh"
+  - "@ewels"
+  - "@matthdsm"
diff --git a/modules/nf-core/samtools/sort/tests/main.nf.test b/modules/nf-core/samtools/sort/tests/main.nf.test
new file mode 100644
index 0000000..b60edf8
--- /dev/null
+++ b/modules/nf-core/samtools/sort/tests/main.nf.test
@@ -0,0 +1,410 @@
+nextflow_process {
+
+    name "Test Process SAMTOOLS_SORT"
+    script "../main.nf"
+    process "SAMTOOLS_SORT"
+    tag "modules"
+    tag "modules_nfcore"
+    tag "samtools"
+    tag "samtools/sort"
+
+    test("bam_no_index") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.bam', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                        process.out.bam,
+                        process.out.index,
+                        process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("bam_bai_index") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.bam', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = 'bai'
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                        process.out.bam,
+                        process.out.index,
+                        process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("bam_csi_index") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.bam', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = 'csi'
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                        process.out.bam,
+                        process.out.index,
+                        process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("multiple bam") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test2.paired_end.sorted.bam', checkIfExists: true)
+                    ]
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                        process.out.bam,
+                        process.out.index.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                        process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("multiple bam bai index") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test2.paired_end.sorted.bam', checkIfExists: true)
+                    ]
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = 'bai'
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                        process.out.bam,
+                        process.out.index.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                        process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("multiple bam csi index") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test2.paired_end.sorted.bam', checkIfExists: true)
+                    ]
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = 'csi'
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                        process.out.bam,
+                        process.out.index.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                        process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("cram") {
+
+        config "./nextflow_cram.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/cram/test.paired_end.sorted.cram', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.cram.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                    process.out.index.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                    process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+    test("bam - stub") {
+
+        options "-stub"
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/bam/test.paired_end.bam', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/sarscov2/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(process.out.findAll { key, val -> key.startsWith("versions") }).match() }
+            )
+        }
+    }
+
+    test("multiple bam - stub") {
+
+        config "./nextflow.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    [
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test.paired_end.sorted.bam', checkIfExists: true),
+                        file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/bam/test2.paired_end.sorted.bam', checkIfExists: true)
+                    ]
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(process.out.findAll { key, val -> key.startsWith("versions") }).match() }
+            )
+        }
+    }
+
+    test("cram - stub") {
+
+        options "-stub"
+        config "./nextflow_cram.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/illumina/cram/test.paired_end.sorted.cram', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(process.out.findAll { key, val -> key.startsWith("versions") }).match() }
+            )
+        }
+    }
+
+    test("multi_sam") {
+
+        config "./nextflow_sam.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/pairtools/mock.sam', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.bam.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                    process.out.index.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                    process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+
+
+    test("sam") {
+
+        config "./nextflow_sam.config"
+
+        when {
+            process {
+                """
+                input[0] = Channel.of([
+                    [ id:'test', single_end:false ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/pairtools/mock.sam', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/pairtools/mock.sam', checkIfExists: true)
+                ])
+                input[1] = Channel.of([
+                    [ id:'fasta' ], // meta map
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta', checkIfExists: true),
+                    file(params.modules_testdata_base_path + 'genomics/homo_sapiens/genome/genome.fasta.fai', checkIfExists: true)
+                ])
+                input[2] = ''
+                """
+            }
+        }
+
+        then {
+            assertAll (
+                { assert process.success },
+                { assert snapshot(
+                    process.out.bam.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                    process.out.index.collect { it.collect { it instanceof Map ? it : file(it).name } },
+                    process.out.findAll { key, val -> key.startsWith("versions") }
+                ).match()}
+            )
+        }
+    }
+}
diff --git a/modules/nf-core/samtools/sort/tests/main.nf.test.snap b/modules/nf-core/samtools/sort/tests/main.nf.test.snap
new file mode 100644
index 0000000..5ce05c3
--- /dev/null
+++ b/modules/nf-core/samtools/sort/tests/main.nf.test.snap
@@ -0,0 +1,344 @@
+{
+    "cram": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.cram"
+                ]
+            ],
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.cram.crai"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:36.491063"
+    },
+    "bam_csi_index": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam:md5,53aea06779611856bc481c60beabecaa"
+                ]
+            ],
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam.csi:md5,f77a4adb3dde616d7eafd28db2ed147c"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:14.341977"
+    },
+    "bam - stub": {
+        "content": [
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:43.558376"
+    },
+    "multiple bam bai index": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam:md5,a15f775c655d4a3b080812a8aae84d34"
+                ]
+            ],
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam.bai"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:25.647565"
+    },
+    "cram - stub": {
+        "content": [
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:54.684578"
+    },
+    "multiple bam": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam:md5,f4343475d9ed2c261f31e1e49d67c1b6"
+                ]
+            ],
+            [
+                
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:20.2368"
+    },
+    "multiple bam - stub": {
+        "content": [
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:48.874947"
+    },
+    "bam_no_index": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam:md5,9277ba4bea590ae1b84e6ab06d11d79b"
+                ]
+            ],
+            [
+                
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:03.721646"
+    },
+    "multi_sam": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:05:00.624092"
+    },
+    "multiple bam csi index": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam:md5,f168809dc154156c40548c06d0f46791"
+                ]
+            ],
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam.csi"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:31.11865"
+    },
+    "sam": {
+        "content": [
+            [
+                
+            ],
+            [
+                
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:05:06.309319"
+    },
+    "bam_bai_index": {
+        "content": [
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam:md5,2ca2d7f2368251d3f06f84afa49865a5"
+                ]
+            ],
+            [
+                [
+                    {
+                        "id": "test",
+                        "single_end": false
+                    },
+                    "test.sorted.bam.bai:md5,66dca3dc2e314029035799f6f44f60d1"
+                ]
+            ],
+            {
+                "versions_samtools": [
+                    [
+                        "SAMTOOLS_SORT",
+                        "samtools",
+                        "1.23.1"
+                    ]
+                ]
+            }
+        ],
+        "meta": {
+            "nf-test": "0.9.3",
+            "nextflow": "25.10.4"
+        },
+        "timestamp": "2026-03-19T09:04:09.147615"
+    }
+}
\ No newline at end of file
diff --git a/modules/nf-core/samtools/sort/tests/nextflow.config b/modules/nf-core/samtools/sort/tests/nextflow.config
new file mode 100644
index 0000000..ca694ca
--- /dev/null
+++ b/modules/nf-core/samtools/sort/tests/nextflow.config
@@ -0,0 +1,6 @@
+process {
+
+    withName: SAMTOOLS_SORT {
+        ext.prefix = { "${meta.id}.sorted" }
+    }
+}
diff --git a/modules/nf-core/samtools/sort/tests/nextflow_cram.config b/modules/nf-core/samtools/sort/tests/nextflow_cram.config
new file mode 100644
index 0000000..8ebc9d9
--- /dev/null
+++ b/modules/nf-core/samtools/sort/tests/nextflow_cram.config
@@ -0,0 +1,7 @@
+process {
+
+    withName: SAMTOOLS_SORT {
+        ext.prefix = { "${meta.id}.sorted" }
+        ext.args   = "--write-index --output-fmt cram"
+    }
+}
diff --git a/modules/nf-core/samtools/sort/tests/nextflow_sam.config b/modules/nf-core/samtools/sort/tests/nextflow_sam.config
new file mode 100644
index 0000000..29ee6a8
--- /dev/null
+++ b/modules/nf-core/samtools/sort/tests/nextflow_sam.config
@@ -0,0 +1,7 @@
+process {
+
+    withName: SAMTOOLS_SORT {
+        ext.prefix = { "${meta.id}.sorted" }
+        ext.args   = "--output-fmt sam"
+    }
+}
diff --git a/nextflow.config b/nextflow.config
index 0e4332b..beb79d6 100644
--- a/nextflow.config
+++ b/nextflow.config
@@ -9,14 +9,29 @@
 // Global default params, used in configs
 params {
 
-    // TODO nf-core: Specify your pipeline's command line flags
-    // Input options
     input                      = null
 
+    min_counts			= 10
+    base_qual			= 30
+    min_flank			= 2
+    error_correction		= 'false_doubles'
+    false_doubles_method	= 'mle'
+    false_doubles_codon_window	= 40
+    pdb				= null
+    mutagenesis_type		= 'nnk'
+    run_seqdepth		= true
+    reading_frame		= null
+    custom_codon_library	= '/NULL'
+    sliding_window_size		= 10
+    aimed_cov			= 100
+    fitness			= false
+    dimsum			= false
+    mutscan			= false
+
     // References
     genome                     = null
     igenomes_base              = 's3://ngi-igenomes/igenomes/'
-    igenomes_ignore            = false
+    igenomes_ignore            = true
 
     // MultiQC options
     multiqc_config             = null
@@ -98,7 +113,6 @@ profiles {
     }
     arm64 {
         process.arch            = 'arm64'
-        // TODO https://github.com/nf-core/modules/issues/6694
         // For now if you're using arm64 you have to use wave for the sake of the maintainers
         // wave profile
         apptainer.ociAutoPull   = true
@@ -181,8 +195,7 @@ includeConfig params.custom_config_base && (!System.getenv('NXF_OFFLINE') || !pa
 
 
 // Load nf-core/deepmutscan custom profiles from different institutions.
-// TODO nf-core: Optionally, you can add a pipeline-specific nf-core config at https://github.com/nf-core/configs
-// includeConfig params.custom_config_base && (!System.getenv('NXF_OFFLINE') || !params.custom_config_base.startsWith('http')) ? "${params.custom_config_base}/pipeline/deepmutscan.config" : "/dev/null"
+includeConfig params.custom_config_base && (!System.getenv('NXF_OFFLINE') || !params.custom_config_base.startsWith('http')) ? "${params.custom_config_base}/pipeline/deepmutscan.config" : "/dev/null"
 
 // Set default registry for Apptainer, Docker, Podman, Charliecloud and Singularity independent of -profile
 // Will not be used unless Apptainer / Docker / Podman / Charliecloud / Singularity are enabled
@@ -240,7 +253,6 @@ dag {
 manifest {
     name            = 'nf-core/deepmutscan'
     contributors    = [
-        // TODO nf-core: Update the field with the details of the contributors to your pipeline. New with Nextflow version 24.10.0
         [
             name: 'Benjamin Wehnert & Max Stammnitz',
             affiliation: '',
@@ -264,9 +276,6 @@ plugins {
     id 'nf-schema@2.5.1' // Validation of pipeline parameters and creation of an input channel from a sample sheet
 }
 
-validation {
-    defaultIgnoreParams = ["genomes"]
-    monochromeLogs = params.monochrome_logs
-}
+
 // Load modules.config for DSL2 module specific options
 includeConfig 'conf/modules.config'
diff --git a/nextflow_schema.json b/nextflow_schema.json
index 40d7d83..dfe43e8 100644
--- a/nextflow_schema.json
+++ b/nextflow_schema.json
@@ -2,7 +2,7 @@
     "$schema": "https://json-schema.org/draft/2020-12/schema",
     "$id": "https://raw.githubusercontent.com/nf-core/deepmutscan/master/nextflow_schema.json",
     "title": "nf-core/deepmutscan pipeline parameters",
-    "description": "Until now, most Deep Mutational Scanning (DMS) experiments relied on variant-specific barcoded libraries for sequencing. This method enabled DMS on large proteins and led to many great publications. Recently, efforts have increased to make use of the classic and more simple random fragmentation-based short-read sequencing (“shotgun-sequencing”). This saves time and money and due to its simpler experimental design is less prone to mistakes. dmscore handles the essential computational steps, processing the raw FASTQ files and generating a count table of variants. Along the way, it provides multiple QC metrics, enabling users to quickly evaluate the success of their experimental setup.",
+    "description": "Until now, most Deep Mutational Scanning (DMS) experiments relied on variant-specific barcoded libraries for sequencing. This method enabled DMS on large proteins and led to many great publications. Recently, efforts have increased to make use of the classic and more simple random fragmentation-based short-read sequencing (\u201cshotgun-sequencing\u201d). This saves time and money and due to its simpler experimental design is less prone to mistakes. dmscore handles the essential computational steps, processing the raw FASTQ files and generating a count table of variants. Along the way, it provides multiple QC metrics, enabling users to quickly evaluate the success of their experimental setup.",
     "type": "object",
     "$defs": {
         "input_output_options": {
@@ -10,7 +10,7 @@
             "type": "object",
             "fa_icon": "fas fa-terminal",
             "description": "Define where the pipeline should find input data and save output data.",
-            "required": ["input", "outdir"],
+            "required": ["input", "outdir", "fasta", "reading_frame"],
             "properties": {
                 "input": {
                     "type": "string",
@@ -40,6 +40,93 @@
                     "type": "string",
                     "description": "MultiQC report title. Printed as page header, used for filename if not otherwise specified.",
                     "fa_icon": "fas fa-file-signature"
+                },
+                "reading_frame": {
+                    "type": "string",
+                    "description": "Start and stop codon positions in the format 'start-stop', e.g., '352-1383'.",
+                    "pattern": "^\\d+-\\d+$"
+                },
+                "min_counts": {
+                    "type": "integer",
+                    "description": "minimum counts for variant to be recognized. All variants below min_counts will be set to 0",
+                    "minimum": 1,
+                    "default": 10
+                },
+                "base_qual": {
+                    "type": "integer",
+                    "description": "Minimum base quality (Phred) for a mutation to be counted by the variant counter.",
+                    "minimum": 0,
+                    "default": 30
+                },
+                "min_flank": {
+                    "type": "integer",
+                    "description": "Minimum distance (nt) a mutation or covered base must be from a read edge; also excludes those bases from coverage. Set to 0 to disable edge trimming.",
+                    "minimum": 0,
+                    "default": 2
+                },
+                "error_correction": {
+                    "type": "string",
+                    "description": "Sequencing-error correction of single-codon variant counts. 'false_doubles' (default) treats observed multi-codon variants as errors to estimate the per-nucleotide error rate; 'none' disables correction; 'wildtype' subtracts the position-specific error profile measured from an additional deep wildtype-only sample (requires samplesheet rows with type 'wildtype').",
+                    "enum": ["false_doubles", "none", "wildtype"],
+                    "default": "false_doubles"
+                },
+                "false_doubles_method": {
+                    "type": "string",
+                    "description": "Estimator used by the false-doubles correction. 'mle' (default) is a per-variant maximum-likelihood error rate; 'eb' is an empirical-Bayes estimate that pools across variants and shrinks per substitution class (steadier for sparse variants). Only applies when error_correction = 'false_doubles'.",
+                    "enum": ["mle", "eb"],
+                    "default": "mle"
+                },
+                "false_doubles_codon_window": {
+                    "type": "integer",
+                    "description": "Window (in codons, upstream and downstream) within which false double mutants are matched to a single-nucleotide variant during false-doubles correction.",
+                    "minimum": 1,
+                    "default": 40
+                },
+                "pdb": {
+                    "type": "string",
+                    "format": "file-path",
+                    "exists": true,
+                    "mimetype": "chemical/x-pdb",
+                    "description": "Optional wildtype 3D structure (PDB). When supplied together with `--fitness`, an interactive variant effect inspection tool (self-contained HTML) is built that projects fitness, counts and error-correction biases onto the structure.",
+                    "help_text": "Use a crystal structure, an AlphaFold DB model, or any precomputed PDB whose sequence matches the wildtype ORF. Structure prediction (nf-core/proteinfold) is not wired in this release."
+                },
+                "sliding_window_size": {
+                    "type": "integer",
+                    "description": "To flatten graphs in plots (e.g. `GLOBAL_POS_BIASES_COUNTS` function)",
+                    "default": 10
+                },
+                "aimed_cov": {
+                    "type": "integer",
+                    "description": "aimed coverage (assuming equal spread) to visualize threshold in plots",
+                    "default": 100
+                },
+                "mutagenesis_type": {
+                    "type": "string",
+                    "description": "Type of mutagenic primers. Choose from nnk, nns, nnh, nnn, nnk_nns, nnk_nns_nnh, custom. When using 'custom', also provide the parameter 'custom_codon_library'.",
+                    "default": "nnk"
+                },
+                "custom_codon_library": {
+                    "type": "string",
+                    "format": "file-path",
+                    "description": "Path to a file defining a custom codon library. Required when mutagenesis_type is set to `custom`. The script auto-detects the format: provide either a global comma-separated list of codons (e.g., 'AAA,AAC,AAG') OR a position-specific, headerless list where each row specifies the target sequence position followed by its allowed codons (e.g., '1,ACG,AAA,ACA' and '2,AAA,TTT,ACA' on separate lines). Both as .csv.",
+                    "default": "/NULL"
+                },
+                "dimsum": {
+                    "type": ["boolean", "string"],
+                    "description": "Run DiMSum for fitness/functionality scores from growth-based selection input & output samples"
+                },
+                "mutscan": {
+                    "type": ["boolean", "string"],
+                    "description": "Run mutscan (Soneson et al., 2023) to estimate fitness/functionality scores from input & output samples"
+                },
+                "fitness": {
+                    "type": ["boolean", "string"],
+                    "description": "Enable basic fitness calculation and preceded data preparation."
+                },
+                "run_seqdepth": {
+                    "type": "boolean",
+                    "default": true,
+                    "description": "Estimate sequencing-depth saturation by closed-form hypergeometric rarefaction (fast; on by default)."
                 }
             }
         },
@@ -217,10 +304,6 @@
                     "description": "Suffix to add to the trace report filename. Default is the date and time in the format yyyy-MM-dd_HH-mm-ss.",
                     "hidden": true
                 },
-                "help": {
-                    "type": ["boolean", "string"],
-                    "description": "Display the help message."
-                },
                 "help_full": {
                     "type": "boolean",
                     "description": "Display the full detailed help message."
diff --git a/nf-test.config b/nf-test.config
index f7aaeb4..c0c14da 100644
--- a/nf-test.config
+++ b/nf-test.config
@@ -34,5 +34,6 @@ config {
     // load the necessary plugins
     plugins {
         load "nft-utils@0.0.3"
+        load "nft-csv@0.1.0"
     }
 }
diff --git a/ro-crate-metadata.json b/ro-crate-metadata.json
index d0aa84b..1a0a29a 100644
--- a/ro-crate-metadata.json
+++ b/ro-crate-metadata.json
@@ -22,8 +22,8 @@
             "@id": "./",
             "@type": "Dataset",
             "creativeWorkStatus": "Stable",
-            "datePublished": "2026-04-30T13:32:46+00:00",
-            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-deepmutscan_logo_dark.png\">\n    <img alt=\"nf-core/deepmutscan\" src=\"docs/images/nf-core-deepmutscan_logo_light.png\">\n  </picture>\n</h1>\n\n[![Open in GitHub Codespaces](https://img.shields.io/badge/Open_In_GitHub_Codespaces-black?labelColor=grey&logo=github)](https://github.com/codespaces/new/nf-core/deepmutscan)\n[![GitHub Actions CI Status](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/nf-test.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/deepmutscan/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.0.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.0.2)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/deepmutscan)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmutscan-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmutscan)[![Follow on Bluesky](https://img.shields.io/badge/bluesky-%40nf__core-1185fe?labelColor=000000&logo=bluesky)](https://bsky.app/profile/nf-co.re)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/deepmutscan** is a bioinformatics pipeline that ...\n\n<!-- TODO nf-core:\n   Complete this sentence with a 2-3 sentence summary of what types of data the pipeline ingests, a brief overview of the\n   major pipeline sections and the types of output it produces. You're giving an overview to someone new\n   to nf-core here, in 15-20 seconds. For an example, see https://github.com/nf-core/rnaseq/blob/master/README.md#introduction\n-->\n\n<!-- TODO nf-core: Include a figure that guides the user through the major workflow steps. Many nf-core\n     workflows use the \"tube map\" design for that. See https://nf-co.re/docs/community/brand/workflow-schematics#examples for examples.   -->\n<!-- TODO nf-core: Fill in short bullet-pointed list of the default steps in the pipeline -->1. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))2. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-pipeline) with `-profile test` before running the workflow on actual data.\n\n<!-- TODO nf-core: Describe the minimum required steps to execute the pipeline, e.g. how to prepare samplesheets.\n     Explain what rows and columns represent. For instance (please edit as appropriate):\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nsample,fastq_1,fastq_2\nCONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz\n```\n\nEach row represents a fastq file (single-end) or a pair of fastq files (paired end).\n\n-->\n\nNow, you can run the pipeline using:\n\n<!-- TODO nf-core: update the following command to include all required parameters for a minimal example -->\n\n```bash\nnextflow run nf-core/deepmutscan \\\n   -profile <docker/singularity/.../institute> \\\n   --input samplesheet.csv \\\n   --outdir <OUTDIR>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/running/run-pipelines#using-parameter-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/deepmutscan/usage) and the [parameter documentation](https://nf-co.re/deepmutscan/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/deepmutscan/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/deepmutscan/output).\n\n## Credits\n\nnf-core/deepmutscan was originally written by Benjamin Wehnert & Max Stammnitz.\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n<!-- TODO nf-core: If applicable, make list of people who have also contributed -->\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](docs/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#deepmutscan` channel](https://nfcore.slack.com/channels/deepmutscan) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\n<!-- TODO nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->\n<!-- If you use nf-core/deepmutscan for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->\n\n<!-- TODO nf-core: Add bibliography of tools and data used in your pipeline -->\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
+            "datePublished": "2026-06-13T18:39:59+00:00",
+            "description": "<h1>\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-deepmutscan_logo_dark.png\">\n    <img alt=\"nf-core/deepmutscan\" src=\"docs/images/nf-core-deepmutscan_logo_light.png\">\n  </picture>\n</h1>\n\n[![GitHub Actions CI Status](https://github.com/nf-core/deepmutscan/actions/workflows/ci.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/ci.yml)\n[![GitHub Actions Linting Status](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml/badge.svg)](https://github.com/nf-core/deepmutscan/actions/workflows/linting.yml)[![AWS CI](https://img.shields.io/badge/CI%20tests-full%20size-FF9900?labelColor=000000&logo=Amazon%20AWS)](https://nf-co.re/deepmutscan/results)[![Cite with Zenodo](http://img.shields.io/badge/DOI-10.5281/zenodo.XXXXXXX-1073c8?labelColor=000000)](https://doi.org/10.5281/zenodo.XXXXXXX)\n[![nf-test](https://img.shields.io/badge/unit_tests-nf--test-337ab7.svg)](https://www.nf-test.com)\n\n[![Nextflow](https://img.shields.io/badge/version-%E2%89%A525.10.4-green?style=flat&logo=nextflow&logoColor=white&color=%230DC09D&link=https%3A%2F%2Fnextflow.io)](https://www.nextflow.io/)\n[![nf-core template version](https://img.shields.io/badge/nf--core_template-4.0.2-green?style=flat&logo=nfcore&logoColor=white&color=%2324B064&link=https%3A%2F%2Fnf-co.re)](https://github.com/nf-core/tools/releases/tag/4.0.2)\n[![run with conda](http://img.shields.io/badge/run%20with-conda-3EB049?labelColor=000000&logo=anaconda)](https://docs.conda.io/en/latest/)\n[![run with docker](https://img.shields.io/badge/run%20with-docker-0db7ed?labelColor=000000&logo=docker)](https://www.docker.com/)\n[![run with singularity](https://img.shields.io/badge/run%20with-singularity-1d355c.svg?labelColor=000000)](https://sylabs.io/docs/)\n[![Launch on Seqera Platform](https://img.shields.io/badge/Launch%20%F0%9F%9A%80-Seqera%20Platform-%234256e7)](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/deepmutscan)\n\n[![Get help on Slack](http://img.shields.io/badge/slack-nf--core%20%23deepmutscan-4A154B?labelColor=000000&logo=slack)](https://nfcore.slack.com/channels/deepmutscan)[![Follow on Twitter](http://img.shields.io/badge/twitter-%40nf__core-1DA1F2?labelColor=000000&logo=twitter)](https://twitter.com/nf_core)[![Follow on Mastodon](https://img.shields.io/badge/mastodon-nf__core-6364ff?labelColor=FFFFFF&logo=mastodon)](https://mstdn.science/@nf_core)[![Watch on YouTube](http://img.shields.io/badge/youtube-nf--core-FF0000?labelColor=000000&logo=youtube)](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/deepmutscan** is a workflow designed for the analysis of deep mutational scanning (DMS) data. DMS enables researchers to experimentally measure the fitness effects of thousands of genes or gene variants simultaneously, helping to classify disease causing mutants in human and animal populations, to learn the fundamental rules of protein architecture, small-molecule binding, mRNA splicing, viral evolution and many other quantifiable phenotypes.\n\nWhile DNA synthesis and sequencing technologies have advanced substantially, long open reading frame (ORF) targets still present a major challenge for DMS studies. Shotgun DNA sequencing can be used to greatly speed up the inference of long ORF mutant fitness landscapes, theoretically at no expense in accuracy. We have designed the `nf-core/deepmutscan` pipeline to unlock the power of shotgun sequencing based DMS studies on long ORFs, to simplify and standardise the complex bioinformatics steps involved in data processing of such experiments – from read alignment to QC reporting and fitness landscape inferences.\n\n![nf-core/deepmutscan workflow](docs/images/pipeline.png)\n\nThe pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!\n\nOn release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/deepmutscan/results).\n\n## Major features\n\n- End-to-end analyses of various DMS data\n- Modular, three-stage workflow: alignment → QC → error-aware fitness estimation\n- Integration with popular statistical fitness estimation tools like [DiMSum](https://github.com/lehner-lab/DiMSum), [Enrich2](https://github.com/FowlerLab/Enrich2), [rosace](https://github.com/pimentellab/rosace/) and [mutscan](https://github.com/fmicompbio/mutscan)\n- Support of multiple mutagenesis strategies, e.g. by nicking with degenerate NNK and NNS codons\n- Containerisation via Docker, Singularity and Apptainer\n- Scalability across HPC and Cloud systems\n- Monitoring of CPU, memory, and CO₂ usage\n\nFor more details on the pipeline and on potential future expansions, please consider reading our [usage description](https://nf-co.re/deepmutscan/usage).\n\n## Step-by-step pipeline summary\n\nThe pipeline processes deep mutational scanning (DMS) sequencing data in several stages:\n\n1. Alignment of reads to the reference open reading frame (ORF) (`BWA-mem`)\n2. Filtering of wildtype and erroneous reads (`samtools view`)\n3. Read merging for base error reduction (`vsearch merge`)\n4. Mutation counting\n5. Single nucleotide variant error correction\n6. DMS library quality control\n7. Data summarisation across samples\n8. Fitness estimation (`DiMSum`, `mutscan`)\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/get_started/environment_setup/overview) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/get_started/run-your-first-pipeline) with `-profile test` before running the workflow on actual data.\n\nFirst, prepare a samplesheet with your input/output data in which each row represents a pair of fastq files (paired end). This should look as follows:\n\n```csv title=\"samplesheet.csv\"\nsample,type,replicate,file1,file2\nORF1,input,1,/reads/forward1.fastq.gz,/reads/reverse1.fastq.gz\nORF1,input,2,/reads/forward2.fastq.gz,/reads/reverse2.fastq.gz\nORF1,output,1,/reads/forward3.fastq.gz,/reads/reverse3.fastq.gz\nORF1,output,2,/reads/forward4.fastq.gz,/reads/reverse4.fastq.gz\n```\n\nSecondly, specify the gene or gene region of interest using a reference FASTA file via `--fasta`. Provide the exact codon coordinates using `--reading_frame`.\n\nNow, you can run the pipeline using:\n\n```bash title=\"example_run.sh\"\nnextflow run nf-core/deepmutscan \\\n   -profile <docker/singularity/.../institute> \\\n   --input ./samplesheet.csv \\\n   --fasta ./ref.fa \\\n   --reading_frame 1-300 \\\n   --outdir ./results\n```\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/deepmutscan/results) tab on the nf-core website pipeline page.\n\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/deepmutscan/output).\n\n## Contributing\n\nWe welcome contributions from the community!\n\nFor technical challenges and feedback on the pipeline, please use our [Github repository](https://github.com/nf-core/deepmutscan). Please open an [issue](https://github.com/nf-core/deepmutscan/issues/new) or [pull request](https://github.com/nf-core/deepmutscan/compare) to:\n\n- Report bugs or solve data incompatibilities when running `nf-core/deepmutscan`\n- Suggest the implementation of new modules for custom DMS workflows\n- Help improve this documentation\n\nIf you are interested in getting involved as a developer, please consider joining our interactive [`#deepmutscan` Slack channel](https://nfcore.slack.com/channels/deepmutscan) (via [this invite](https://nf-co.re/join/slack)).\n\n## Credits\n\nnf-core/deepmutscan was originally written by [Benjamin Wehnert](https://github.com/BenjaminWehnert1008) and [Max Stammnitz](https://github.com/MaximilianStammnitz) at the [Centre for Genomic Regulation, Barcelona](https://www.crg.eu/), with the generous support of an EMBO Long-term Postdoctoral Fellowship and a Marie Skłodowska-Curie grant by the European Union.\n\nIf you use `nf-core/deepmutscan` in your analyses, please cite:\n\n> 📄 Wehnert et al., _bioRxiv_ preprint (coming soon)\n\nPlease also cite the `nf-core` framework:\n\n> 📄 Ewels et al., _Nature Biotechnology_, 2020\n> [https://doi.org/10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#deepmutscan` channel](https://nfcore.slack.com/channels/deepmutscan) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Scientific contact\n\nFor scientific discussions around the use of this pipeline (e.g. on experimental design or sequencing data requirements), please feel free to get in touch with us directly:\n\n- Benjamin Wehnert — wehnertbenjamin@gmail.com\n- Maximilian Stammnitz — maximilian.stammnitz@crg.eu\n",
             "hasPart": [
                 {
                     "@id": "main.nf"
@@ -43,6 +43,9 @@
                 {
                     "@id": "modules/"
                 },
+                {
+                    "@id": "modules/local/"
+                },
                 {
                     "@id": "modules/nf-core/"
                 },
@@ -99,7 +102,7 @@
             },
             "mentions": [
                 {
-                    "@id": "#7786fe09-7f6a-48ce-8926-af918fe3b5b1"
+                    "@id": "#7146010e-cfc6-420d-99f0-b8197ccd79c7"
                 }
             ],
             "name": "nf-core/deepmutscan"
@@ -121,14 +124,18 @@
         },
         {
             "@id": "main.nf",
-            "@type": ["File", "SoftwareSourceCode", "ComputationalWorkflow"],
+            "@type": [
+                "File",
+                "SoftwareSourceCode",
+                "ComputationalWorkflow"
+            ],
             "contributor": [
                 {
-                    "@id": "#f321369f-b518-4a4f-87b7-a49c86013f6e"
+                    "@id": "#72177bf6-a8bc-482d-af8f-8a6c470a26a6"
                 }
             ],
             "dateCreated": "",
-            "dateModified": "2026-04-30T13:32:46Z",
+            "dateModified": "2026-06-13T20:39:59Z",
             "dct:conformsTo": "https://bioschemas.org/profiles/ComputationalWorkflow/1.0-RELEASE/",
             "keywords": [
                 "nf-core",
@@ -138,16 +145,25 @@
                 "genotype-phenotype",
                 "shotgun-sequencing"
             ],
-            "license": ["MIT"],
-            "name": ["nf-core/deepmutscan"],
+            "license": [
+                "MIT"
+            ],
+            "name": [
+                "nf-core/deepmutscan"
+            ],
             "programmingLanguage": {
                 "@id": "https://w3id.org/workflowhub/workflow-ro-crate#nextflow"
             },
             "sdPublisher": {
                 "@id": "https://nf-co.re/"
             },
-            "url": ["https://github.com/nf-core/deepmutscan", "https://nf-co.re/deepmutscan/1.0.0/"],
-            "version": ["1.0.0"]
+            "url": [
+                "https://github.com/nf-core/deepmutscan",
+                "https://nf-co.re/deepmutscan/1.0.0/"
+            ],
+            "version": [
+                "1.0.0"
+            ]
         },
         {
             "@id": "https://w3id.org/workflowhub/workflow-ro-crate#nextflow",
@@ -162,11 +178,11 @@
             "version": "!>=25.10.4"
         },
         {
-            "@id": "#7786fe09-7f6a-48ce-8926-af918fe3b5b1",
+            "@id": "#7146010e-cfc6-420d-99f0-b8197ccd79c7",
             "@type": "TestSuite",
             "instance": [
                 {
-                    "@id": "#54531b61-2c83-48b9-909d-71fe62b8c005"
+                    "@id": "#a6466342-1368-4c50-86fc-cb19fd46c1cc"
                 }
             ],
             "mainEntity": {
@@ -175,7 +191,7 @@
             "name": "Test suite for nf-core/deepmutscan"
         },
         {
-            "@id": "#54531b61-2c83-48b9-909d-71fe62b8c005",
+            "@id": "#a6466342-1368-4c50-86fc-cb19fd46c1cc",
             "@type": "TestInstance",
             "name": "GitHub Actions workflow for testing nf-core/deepmutscan",
             "resource": "repos/nf-core/deepmutscan/actions/workflows/nf-test.yml",
@@ -217,6 +233,11 @@
             "@type": "Dataset",
             "description": "Modules used by the pipeline"
         },
+        {
+            "@id": "modules/local/",
+            "@type": "Dataset",
+            "description": "Pipeline-specific modules"
+        },
         {
             "@id": "modules/nf-core/",
             "@type": "Dataset",
@@ -304,9 +325,9 @@
             "url": "https://nf-co.re/"
         },
         {
-            "@id": "#f321369f-b518-4a4f-87b7-a49c86013f6e",
+            "@id": "#72177bf6-a8bc-482d-af8f-8a6c470a26a6",
             "@type": "Person",
             "name": "Benjamin Wehnert & Max Stammnitz"
         }
     ]
-}
+}
\ No newline at end of file
diff --git a/subworkflows/local/calculate_fitness/main.nf b/subworkflows/local/calculate_fitness/main.nf
new file mode 100644
index 0000000..6f7b4c4
--- /dev/null
+++ b/subworkflows/local/calculate_fitness/main.nf
@@ -0,0 +1,151 @@
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    IMPORT MODULES
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+include { MERGE_COUNTS }             from '../../../modules/local/fitness/merge_counts/main'
+include { EXPDESIGN_FITNESS }        from '../../../modules/local/fitness/fitness_experimental_design/main'
+include { FIND_SYNONYMOUS_MUTATION } from '../../../modules/local/fitness/find_synonymous_mutation/main'
+include { FITNESS_CALCULATION }      from '../../../modules/local/fitness/fitness_calculation/main'
+include { FITNESS_QC }               from '../../../modules/local/fitness/fitness_QC/main'
+include { FITNESS_HEATMAP }          from '../../../modules/local/fitness/fitness_heatmap/main'
+include { RUN_DIMSUM }               from '../../../modules/local/fitness/run_dimsum/main'
+include { RUN_MUTSCAN }              from '../../../modules/local/fitness/run_mutscan/main'
+
+/*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    SUBWORKFLOW DEFINITION
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+*/
+
+workflow CALCULATE_FITNESS {
+
+    take:
+    ch_fitness_input    // channel: output from COUNTS_TO_FITNESS (grouped by sample/replicate logic)
+    ch_samplesheet_csv  // channel: path to samplesheet csv
+    ch_fasta            // channel: tuple([meta], path(fasta))
+    val_reading_frame   // value: reading frame param
+    ch_aa_seq           // channel: output from DMSANALYSIS_AASEQ (for heatmap)
+
+    main:
+    ch_versions = Channel.empty()
+
+    // 1. Group per biological sample and merge counts
+    ch_fitness_input
+        .map { meta, tsv ->
+            def s  = meta.sample as String
+            def id = meta.id     as String
+            def base = s ? (s.replaceFirst(/_(input|output|quality)\d+$/, ''))
+                       : (id?.tokenize('_')?.first())
+            tuple(base as String, tuple(meta, tsv))
+        }
+        .groupTuple()
+        .map { base, pairs ->
+            // sort by replicate so the staged file order is deterministic (cacheable)
+            def inputs  = pairs.findAll { it[0].type == 'input'  }.sort { it[0].replicate }.collect { it[1] }
+            def outputs = pairs.findAll { it[0].type == 'output' }.sort { it[0].replicate }.collect { it[1] }
+            tuple([sample: base], inputs, outputs)
+        }
+        .filter { smeta, ins, outs -> ins && outs }
+        .set { ch_fitness_bundled }
+
+    // 2. Merge Counts
+    MERGE_COUNTS( ch_fitness_bundled )
+    ch_versions = ch_versions.mix(MERGE_COUNTS.out.versions)
+
+    // 3. Experimental Design
+    EXPDESIGN_FITNESS( ch_samplesheet_csv )
+    ch_versions = ch_versions.mix(EXPDESIGN_FITNESS.out.versions)
+
+    // 4. Find Synonymous Mutations
+    // Prepare inputs (broadcast logic)
+    def ch_fasta_path = ch_fasta.map { it[1] } // strip meta
+
+    FIND_SYNONYMOUS_MUTATION(
+        MERGE_COUNTS.out.merged_counts,
+        ch_fasta_path.combine(MERGE_COUNTS.out.merged_counts).map { it[0] },
+        val_reading_frame.combine(MERGE_COUNTS.out.merged_counts).map { it[0] }
+    )
+    ch_versions = ch_versions.mix(FIND_SYNONYMOUS_MUTATION.out.versions)
+
+
+    // 5. Align Channels for Fitness Calculation
+
+    // Key counts and WT by biological sample name
+    def ch_counts_keyed_d = MERGE_COUNTS.out.merged_counts
+        .map { smp, counts -> tuple(smp.sample as String, smp, counts) }
+
+    def ch_wt_keyed_d = FIND_SYNONYMOUS_MUTATION.out.synonymous_wt
+        .map { smp, wt -> tuple(smp.sample as String, wt) }
+
+    // Join by key
+    def ch_counts_wt_d = ch_counts_keyed_d.join(ch_wt_keyed_d)
+        .map { key, smp, counts, wt -> tuple(smp, counts, wt) }
+
+    // Broadcast experimental design
+    def ch_exp_for_each_d = EXPDESIGN_FITNESS.out.experimental_design
+        .combine(ch_counts_wt_d)
+        .map { it[0] }
+
+    // Final aligned channels
+    def ch_run_counts_d = ch_counts_wt_d.map { smp, counts, wt -> tuple(smp, counts) }
+    def ch_run_wt_d     = ch_counts_wt_d.map { smp, counts, wt -> wt }
+    def ch_run_exp_d    = ch_exp_for_each_d
+
+    // 6. Run Fitness Calculation & QC
+    FITNESS_CALCULATION(
+        ch_run_counts_d,
+        ch_run_exp_d,
+        ch_run_wt_d
+    )
+    ch_versions = ch_versions.mix(FITNESS_CALCULATION.out.versions)
+
+    FITNESS_QC( FITNESS_CALCULATION.out.fitness_estimation )
+    ch_versions = ch_versions.mix(FITNESS_QC.out.versions)
+
+    FITNESS_HEATMAP(
+        FITNESS_CALCULATION.out.fitness_estimation,
+        ch_aa_seq
+    )
+    ch_versions = ch_versions.mix(FITNESS_HEATMAP.out.versions)
+
+    // 7. Run DiMSum (optional based on params inside subworkflow or handled by control logic)
+    // Note: Logic checking for 'params.dimsum' needs to be handled.
+    // Since subworkflows inherit params, we can check params.dimsum here.
+
+    // DiMSum and mutscan are optional, so their artefact channels start empty and are only filled
+    // when the tool actually ran - the summary report then simply omits the section.
+    def ch_dimsum_dir    = Channel.empty()
+    def ch_mutscan_plots = Channel.empty()
+
+    if (params.dimsum) {
+        RUN_DIMSUM(
+            ch_run_counts_d,
+            ch_run_wt_d,
+            ch_run_exp_d
+        )
+        ch_versions = ch_versions.mix(RUN_DIMSUM.out.versions)
+        ch_dimsum_dir = RUN_DIMSUM.out.results_dir
+    }
+
+    // 8. Run Mutscan
+    if (params.mutscan) {
+        RUN_MUTSCAN(
+            ch_run_counts_d,
+            ch_run_wt_d,
+            ch_run_exp_d
+        )
+        ch_versions = ch_versions.mix(RUN_MUTSCAN.out.versions)
+        ch_mutscan_plots = RUN_MUTSCAN.out.qc_plots
+    }
+
+    emit:
+    fitness_estimation = FITNESS_CALCULATION.out.fitness_estimation
+    // artefacts the all-in-one report embeds
+    fitness_plots      = FITNESS_QC.out.counts_corr_pdf
+                            .mix(FITNESS_QC.out.fitness_corr_pdf)
+                            .mix(FITNESS_HEATMAP.out.fitness_heatmap)
+    mutscan_plots      = ch_mutscan_plots
+    dimsum_dir         = ch_dimsum_dir
+    versions           = ch_versions
+}
diff --git a/subworkflows/local/calculate_fitness/meta.yml b/subworkflows/local/calculate_fitness/meta.yml
new file mode 100644
index 0000000..872ba1e
--- /dev/null
+++ b/subworkflows/local/calculate_fitness/meta.yml
@@ -0,0 +1,42 @@
+name: "calculate_fitness"
+description: Consolidates variant counts and calculates fitness scores.
+keywords:
+  - "deep mutational scanning"
+  - "fitness estimation"
+  - "dms"
+  - "enrichment scores"
+components:
+  - "merge/counts"
+  - "fitness/calculation"
+  - "fitness/heatmap"
+  - "fitness/qc"
+  - "run/dimsum"
+  - "run/mutscan"
+  - "expdesign/fitness"
+  - "find/synonymous/mutation"
+input:
+  - ch_fitness_input:
+      type: channel
+      description: Variant count channels
+  - ch_samplesheet_csv:
+      type: channel
+      description: Pipeline samplesheet
+  - ch_fasta:
+      type: channel
+      description: Reference sequence channel
+  - val_reading_frame:
+      type: value
+      description: Reading frame parameter
+  - ch_aa_seq:
+      type: channel
+      description: Amino acid sequence channel
+output:
+  - fitness_estimation:
+      type: channel
+      description: Output fitness metrics
+  - versions:
+      type: channel
+      description: Software versions
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf
index 01c0ad7..50e4edb 100644
--- a/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf
+++ b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/main.nf
@@ -10,8 +10,6 @@
 
 include { UTILS_NFSCHEMA_PLUGIN     } from '../../nf-core/utils_nfschema_plugin'
 include { paramsSummaryMap          } from 'plugin/nf-schema'
-include { samplesheetToList         } from 'plugin/nf-schema'
-include { paramsHelp                } from 'plugin/nf-schema'
 include { completionEmail           } from '../../nf-core/utils_nfcore_pipeline'
 include { completionSummary         } from '../../nf-core/utils_nfcore_pipeline'
 include { UTILS_NFCORE_PIPELINE     } from '../../nf-core/utils_nfcore_pipeline'
@@ -32,13 +30,10 @@ workflow PIPELINE_INITIALISATION {
     nextflow_cli_args //   array: List of positional nextflow CLI args
     outdir            //  string: The output directory where the results will be saved
     input             //  string: Path to input samplesheet
-    help              // boolean: Display help message and exit
-    help_full         // boolean: Show the full help message
-    show_hidden       // boolean: Show hidden parameters in the help message
 
     main:
 
-    ch_versions = channel.empty()
+    ch_versions = Channel.empty()
 
     //
     // Print version and exit if required and dump pipeline parameters to JSON file
@@ -82,13 +77,14 @@ workflow PIPELINE_INITIALISATION {
     UTILS_NFSCHEMA_PLUGIN (
         workflow,
         validate_params,
+        params.help,
+        params.help_full,
+        params.show_hidden,
         null,
-        help,
-        help_full,
-        show_hidden,
         before_text,
         after_text,
-        command
+        command,
+        "${projectDir}/nextflow_schema.json"
     )
 
     //
@@ -107,26 +103,40 @@ workflow PIPELINE_INITIALISATION {
     // Create channel from input file provided through params.input
     //
 
-    channel
-        .fromList(samplesheetToList(input, "${projectDir}/assets/schema_input.json"))
-        .map {
-            meta, fastq_1, fastq_2 ->
-                if (!fastq_2) {
-                    return [ meta.id, meta + [ single_end:true ], [ fastq_1 ] ]
-                } else {
-                    return [ meta.id, meta + [ single_end:false ], [ fastq_1, fastq_2 ] ]
-                }
-        }
-        .groupTuple()
-        .map { samplesheet ->
-            validateInputSamplesheet(samplesheet)
+    Channel
+    .fromPath(params.input)
+    .splitCsv(header: true)
+    .filter { row ->
+        // Skip rows where file1 or file2 are BAM files
+        !(row.file1.endsWith('.bam') || (row.file2 && row.file2.endsWith('.bam')))
+    }
+    .map { row ->
+        // Determine suffix based on the presence of file2
+        def suffix = row.file2 ? "_pe" : "_se"
+
+        // Construct metadata object with updated ID
+        def meta = [
+            id        : "${row.sample}_${row.type}_${row.replicate}${suffix}", // Base ID with suffix
+            sample    : row.sample,
+            type      : row.type,
+            replicate : row.replicate as int
+        ]
+
+        // Generate file paths based on the presence of file1 and file2
+        def reads = []
+        if (row.file1) {
+            reads << row.file1 // Add file1 path
         }
-        .map {
-            meta, fastqs ->
-                return [ meta, fastqs.flatten() ]
+        if (row.file2) {
+            reads << row.file2 // Add file2 path
         }
-        .set { ch_samplesheet }
 
+        // Return metadata and file paths as a tuple
+        return [meta, reads]
+    }
+    .set { ch_samplesheet }
+
+    // Emit the samplesheet channel and an empty version channel for use in the workflow
     emit:
     samplesheet = ch_samplesheet
     versions    = ch_versions
@@ -232,7 +242,6 @@ def genomeExistsError() {
 // Generate methods description for MultiQC
 //
 def toolCitationText() {
-    // TODO nf-core: Optionally add in-text citation tools to this list.
     // Can use ternary operators to dynamically construct based conditions, e.g. params["run_xyz"] ? "Tool (Foo et al. 2023)" : "",
     // Uncomment function in methodsDescriptionText to render in MultiQC report
     def citation_text = [
@@ -246,7 +255,6 @@ def toolCitationText() {
 }
 
 def toolBibliographyText() {
-    // TODO nf-core: Optionally add bibliographic entries to this list.
     // Can use ternary operators to dynamically construct based conditions, e.g. params["run_xyz"] ? "<li>Author (2023) Pub name, Journal, DOI</li>" : "",
     // Uncomment function in methodsDescriptionText to render in MultiQC report
     def reference_text = [
@@ -281,9 +289,8 @@ def methodsDescriptionText(mqc_methods_yaml) {
     meta["tool_citations"] = ""
     meta["tool_bibliography"] = ""
 
-    // TODO nf-core: Only uncomment below if logic in toolCitationText/toolBibliographyText has been filled!
-    // meta["tool_citations"] = toolCitationText().replaceAll(", \\.", ".").replaceAll("\\. \\.", ".").replaceAll(", \\.", ".")
-    // meta["tool_bibliography"] = toolBibliographyText()
+    meta["tool_citations"] = toolCitationText().replaceAll(", \\.", ".").replaceAll("\\. \\.", ".").replaceAll(", \\.", ".")
+    meta["tool_bibliography"] = toolBibliographyText()
 
 
     def methods_text = mqc_methods_yaml.text
diff --git a/subworkflows/local/utils_nfcore_deepmutscan_pipeline/meta.yml b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/meta.yml
new file mode 100644
index 0000000..2f5669a
--- /dev/null
+++ b/subworkflows/local/utils_nfcore_deepmutscan_pipeline/meta.yml
@@ -0,0 +1,58 @@
+name: "PIPELINE_INITIALISATION"
+description: Local utility functions, pipeline initialization, completion workflows, and parameter validation wrappers for the deepmutscan pipeline.
+keywords:
+  - "utility"
+  - "validation"
+  - "manifest"
+  - "initialisation"
+  - "completion"
+components:
+  - "utils_nfschema_plugin"
+  - "utils_nfcore_pipeline"
+  - "utils_nextflow_pipeline"
+  - "completionemail"
+  - "completionsummary"
+
+input:
+  - version:
+      type: boolean
+      description: Display version and exit
+  - validate_params:
+      type: boolean
+      description: Boolean whether to validate parameters against the schema at runtime
+  - monochrome_logs:
+      type: boolean
+      description: Do not use coloured log outputs
+  - nextflow_cli_args:
+      type: array
+      description: List of positional nextflow CLI args
+  - outdir:
+      type: string
+      description: The output directory where the results will be saved
+  - input:
+      type: string
+      description: Path to input samplesheet
+  - email:
+      type: string
+      description: Email address for completion summary
+  - email_on_fail:
+      type: string
+      description: Email address sent on pipeline failure
+  - plaintext_email:
+      type: boolean
+      description: Send plain-text email instead of HTML
+  - multiqc_report:
+      type: string
+      description: Path to MultiQC report
+
+output:
+  - samplesheet:
+      type: channel
+      description: Processed samplesheet channel containing metadata objects and file paths
+  - versions:
+      type: channel
+      description: Empty version channel for use in the workflow
+
+authors:
+  - "@BenjaminWehnert1008"
+  - "@MaximilianStammnitz"
diff --git a/subworkflows/nf-core/utils_nextflow_pipeline/main.nf b/subworkflows/nf-core/utils_nextflow_pipeline/main.nf
index d6e593e..37939ac 100644
--- a/subworkflows/nf-core/utils_nextflow_pipeline/main.nf
+++ b/subworkflows/nf-core/utils_nextflow_pipeline/main.nf
@@ -73,11 +73,23 @@ def getWorkflowVersion() {
 def dumpParametersToJSON(outdir) {
     def timestamp = new java.util.Date().format('yyyy-MM-dd_HH-mm-ss')
     def filename  = "params_${timestamp}.json"
-    def temp_pf   = new File(workflow.launchDir.toString(), ".${filename}")
-    def jsonStr   = groovy.json.JsonOutput.toJson(params)
+    def temp_pf       = workflow.launchDir.resolve(".${filename}")
+    def jsonGenerator = new groovy.json.JsonGenerator.Options()
+        .excludeNulls()
+        .addConverter(Path) { Path path -> path.toUriString() }
+        .addConverter(Duration) { Duration duration -> duration.toMillis() }
+        .addConverter(MemoryUnit) { MemoryUnit memory -> memory.toBytes() }
+        .addConverter(nextflow.script.types.VersionNumber) { nextflow.script.types.VersionNumber version -> version.toString() }
+        .build()
+    def jsonStr   = jsonGenerator.toJson(params)
     temp_pf.text  = groovy.json.JsonOutput.prettyPrint(jsonStr)
-
-    nextflow.extension.FilesEx.copyTo(temp_pf.toPath(), "${outdir}/pipeline_info/params_${timestamp}.json")
+    if (outdir instanceof Path) {
+        temp_pf.copyTo(outdir.resolve("pipeline_info/${filename}"))
+    } else if (outdir instanceof String) {
+        temp_pf.copyTo("${outdir}/pipeline_info/params_${timestamp}.json")
+    } else {
+        log.warn("Could not determine type of outdir, parameters JSON file will not be copied to output directory!")
+    }
     temp_pf.delete()
 }
 
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test b/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test
deleted file mode 100644
index 8940d32..0000000
--- a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test
+++ /dev/null
@@ -1,29 +0,0 @@
-nextflow_workflow {
-
-    name "Test Workflow UTILS_NFCORE_PIPELINE"
-    script "../main.nf"
-    config "subworkflows/nf-core/utils_nfcore_pipeline/tests/nextflow.config"
-    workflow "UTILS_NFCORE_PIPELINE"
-    tag "subworkflows"
-    tag "subworkflows_nfcore"
-    tag "utils_nfcore_pipeline"
-    tag "subworkflows/utils_nfcore_pipeline"
-
-    test("Should run without failures") {
-
-        when {
-            workflow {
-                """
-                input[0] = []
-                """
-            }
-        }
-
-        then {
-            assertAll(
-                { assert workflow.success },
-                { assert snapshot(workflow.out).match() }
-            )
-        }
-    }
-}
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap b/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap
deleted file mode 100644
index 859d103..0000000
--- a/subworkflows/nf-core/utils_nfcore_pipeline/tests/main.workflow.nf.test.snap
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-    "Should run without failures": {
-        "content": [
-            {
-                "0": [
-                    true
-                ],
-                "valid_config": [
-                    true
-                ]
-            }
-        ],
-        "meta": {
-            "nf-test": "0.8.4",
-            "nextflow": "23.10.1"
-        },
-        "timestamp": "2024-02-28T12:03:25.726491"
-    }
-}
\ No newline at end of file
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/main.nf b/subworkflows/nf-core/utils_nfschema_plugin/main.nf
index 1df8b76..9ff0681 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/main.nf
+++ b/subworkflows/nf-core/utils_nfschema_plugin/main.nf
@@ -22,6 +22,7 @@ workflow UTILS_NFSCHEMA_PLUGIN {
     before_text         // string:   text to show before the help message and parameters summary
     after_text          // string:   text to show after the help message and parameters summary
     command             // string:   an example command of the pipeline
+    cli_typecast        // boolean:  whether to perform typecasting of CLI parameters. Set this to `null` to use the default behaviour
 
     main:
 
@@ -34,11 +35,11 @@ workflow UTILS_NFSCHEMA_PLUGIN {
             fullHelp: help_full,
         ]
         if(parameters_schema) {
-            help_options << [parametersSchema: parameters_schema]
+            help_options << [parameters_schema: parameters_schema]
         }
         log.info paramsHelp(
             help_options,
-            (params.help instanceof String && params.help != "true") ? params.help : "",
+            (help instanceof String && help != "true") ? help : "",
         )
         exit 0
     }
@@ -50,7 +51,7 @@ workflow UTILS_NFSCHEMA_PLUGIN {
 
     summary_options = [:]
     if(parameters_schema) {
-        summary_options << [parametersSchema: parameters_schema]
+        summary_options << [parameters_schema: parameters_schema]
     }
     log.info before_text
     log.info paramsSummaryLog(summary_options, input_workflow)
@@ -63,7 +64,10 @@ workflow UTILS_NFSCHEMA_PLUGIN {
     if(validate_params) {
         validateOptions = [:]
         if(parameters_schema) {
-            validateOptions << [parametersSchema: parameters_schema]
+            validateOptions << [parameters_schema: parameters_schema]
+        }
+        if(cli_typecast != null) {
+            validateOptions << [cast_cli_params: cli_typecast]
         }
         validateParameters(validateOptions)
     }
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/meta.yml b/subworkflows/nf-core/utils_nfschema_plugin/meta.yml
index f7d9f02..1d8c75a 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/meta.yml
+++ b/subworkflows/nf-core/utils_nfschema_plugin/meta.yml
@@ -25,6 +25,30 @@ input:
         option. When this input is empty it will automatically use the configured schema or
         "${projectDir}/nextflow_schema.json" as default. The schema should not be given in this way
         for meta pipelines.
+  - help:
+      type: boolean, string
+      description: |
+        Show the help message and exit. When a parameter name is given, show the help message for that parameter instead of the general help message.
+  - help_full:
+      type: boolean
+      description: Show the full help message and exit.
+  - show_hidden:
+      type: boolean
+      description: Show hidden parameters in the help message.
+  - before_text:
+      type: string
+      description: Text to show before the parameters summary and help message.
+  - after_text:
+      type: string
+      description: Text to show after the parameters summary and help message.
+  - command:
+      type: string
+      description: An example command to run the pipeline, to show in the help message and the summary.
+  - cli_typecast:
+      type: boolean
+      description: |
+        Whether to apply typecasting to the parameters given via the CLI before validation.
+        Set this to `null` to use the default behavior.
 output:
   - dummy_emit:
       type: boolean
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test b/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test
index c977917..1fd1eac 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test
+++ b/subworkflows/nf-core/utils_nfschema_plugin/tests/main.nf.test
@@ -31,6 +31,7 @@ nextflow_workflow {
                 input[6] = ""
                 input[7] = ""
                 input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -63,6 +64,7 @@ nextflow_workflow {
                 input[6] = ""
                 input[7] = ""
                 input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -95,6 +97,7 @@ nextflow_workflow {
                 input[6] = ""
                 input[7] = ""
                 input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -127,6 +130,7 @@ nextflow_workflow {
                 input[6] = ""
                 input[7] = ""
                 input[8] = ""
+                input[9] = null
                 """
             }
         }
@@ -160,6 +164,7 @@ nextflow_workflow {
                 input[6] = "Before"
                 input[7] = "After"
                 input[8] = "nextflow run test/test"
+                input[9] = null
                 """
             }
         }
diff --git a/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config b/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config
index f6537cc..fd71cb8 100644
--- a/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config
+++ b/subworkflows/nf-core/utils_nfschema_plugin/tests/nextflow.config
@@ -1,5 +1,5 @@
 plugins {
-    id "nf-schema@2.6.1"
+    id "nf-schema@2.7.2"
 }
 
 validation {
diff --git a/tests/.nftignore b/tests/.nftignore
index e128a12..0c8b5c9 100644
--- a/tests/.nftignore
+++ b/tests/.nftignore
@@ -10,3 +10,26 @@ multiqc/multiqc_plots/{svg,pdf,png}/*.{svg,pdf,png}
 multiqc/multiqc_report.html
 fastqc/*_fastqc.{html,zip}
 pipeline_info/*.{html,json,txt,yml}
+library_QC/**/*.pdf
+fitness/DiMSum_results/**/*.pdf
+fitness/DiMSum_results/**/dimsum_results_workspace.RData
+fitness/DiMSum_results/**/report.html
+fitness/DiMSum_results/**/*.png
+**/*.pdf
+**/*.RData
+**/*_error_correction_report.html
+**/variant_effect_inspection_tool.html
+deepmutscan_report.html
+**/structure_data.json
+fitness/DiMSum_results/**/report.html
+**/fitness_estimation_mutscan_edgeR.tsv
+**/fitness_estimation_mutscan_limma.tsv
+**/dimsum_results_*_merge.tsv
+**/fitness_synonymous.txt
+**/*_fitness_input.tsv
+**/counts_merged.tsv
+**/fitness_estimation.tsv
+**/*.variant_counts.tsv
+**/annotated_variantCounts.csv
+**/library_completed_variantCounts*.csv
+**/variantCounts_filtered_by_library*.csv
diff --git a/tests/default.nf.test b/tests/default.nf.test
index 6187c32..2fa68e0 100644
--- a/tests/default.nf.test
+++ b/tests/default.nf.test
@@ -14,9 +14,15 @@ nextflow_pipeline {
 
         then {
             // stable_path: All files + folders in ${params.outdir}/ with a stable path (including file name)
-            def stable_path = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}'])
+            // DiMSum's diagnostic plots are excluded from the path list as well as from the content
+            // hashes (tests/.nftignore): which of them DiMSum emits depends on its graphics stack, so
+            // their mere presence is not a stable contract to assert on.
+            def stable_path = getAllFilesFromDir(params.outdir, relative: true, includeDir: true, ignore: ['pipeline_info/*.{html,json,txt}', 'multiqc/multiqc_plots', 'multiqc/multiqc_plots/**', 'fitness/DiMSum_results/**/reports/*.{pdf,png}'])
             // stable_content: All files in ${params.outdir}/ with stable content
             def stable_content = getAllFilesFromDir(params.outdir, ignoreFile: 'tests/.nftignore')
+            // Parse the unstable TSVs to snapshot their structure instead of their hashes
+            def edger_tsv = path("$outputDir/fitness/mutscan_results/fitness_estimation_mutscan_edgeR.tsv").csv(sep: '\t')
+            def limma_tsv = path("$outputDir/fitness/mutscan_results/fitness_estimation_mutscan_limma.tsv").csv(sep: '\t')
             assert workflow.success
             assertAll(
                 { assert snapshot(
@@ -25,7 +31,12 @@ nextflow_pipeline {
                     // All stable path name, with a relative path
                     stable_path,
                     // All files with stable contents
-                    stable_content
+                    stable_content,
+                    // Partial structural snapshot of the unstable TSV files
+                    [
+                        edger_rowCount: edger_tsv.rowCount,
+                        limma_rowCount: limma_tsv.rowCount
+                    ]
                 ).match() }
             )
         }
diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap
new file mode 100644
index 0000000..c8ecb88
--- /dev/null
+++ b/tests/default.nf.test.snap
@@ -0,0 +1,390 @@
+{
+    "-profile test": {
+        "content": [
+            {
+                "BWA_INDEX": {
+                    "bwa": "0.7.19-r1273"
+                },
+                "BWA_MEM": {
+                    "bwa": "0.7.19-r1273",
+                    "samtools": "1.22.1"
+                },
+                "DIMSUM_RUN": {
+                    "r-base": "4.4.2"
+                },
+                "DMSANALYSIS_AASEQ": {
+                    "r-base": "4.5.1",
+                    "r-Biostrings": "2.78.0"
+                },
+                "DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES": {
+                    "r-base": "4.5.1",
+                    "r-stringr": "1.6.0",
+                    "r-Biostrings": "2.78.0"
+                },
+                "DMSANALYSIS_POSSIBLE_MUTATIONS": {
+                    "r-base": "4.5.1",
+                    "biostrings": "2.78.0"
+                },
+                "EXPDESIGN_FITNESS": {
+                    "r-base": "4.5.1"
+                },
+                "FASTQC": {
+                    "fastqc": "0.12.1"
+                },
+                "FIND_SYNONYMOUS_MUTATION": {
+                    "r-base": "4.5.1",
+                    "r-Biostrings": "2.78.0"
+                },
+                "FITNESS_CALCULATION": {
+                    "r-base": "4.5.1",
+                    "r-Biostrings": "2.78.0"
+                },
+                "FITNESS_HEATMAP": {
+                    "r-base": "4.5.1",
+                    "r-dplyr": "1.1.4",
+                    "r-ggplot2": "4.0.2",
+                    "r-methods": "4.5.1",
+                    "r-grid": "4.5.1"
+                },
+                "FITNESS_QC": {
+                    "r-base": "4.5.1"
+                },
+                "MERGE_COUNTS": {
+                    "r-base": "4.5.1"
+                },
+                "RUN_MUTSCAN": {
+                    "r-base": "4.5.1",
+                    "r-mutscan": "1.0.0",
+                    "r-Biostrings": "2.78.0"
+                },
+                "SAMTOOLS_INDEX": {
+                    "samtools": "1.23.1"
+                },
+                "SAMTOOLS_SORT": {
+                    "samtools": "1.23.1"
+                },
+                "VARIANTCOUNTING": {
+                    "python": "3.12.13",
+                    "pysam": "0.24.0",
+                    "polars": "1.33.1",
+                    "pyarrow": "24.0.0",
+                    "numpy": "2.5.1",
+                    "biopython": 1.87
+                },
+                "VARIANT_EFFECT_INSPECTION_TOOL": {
+                    "python": "3.12.3",
+                    "pandas": "2.2.1",
+                    "numpy": "1.26.4"
+                },
+                "VISUALIZATION_ERROR_CORRECTION_REPORT": {
+                    "python": "3.12.3",
+                    "pandas": "2.2.1",
+                    "numpy": "1.26.4"
+                },
+                "VISUALIZATION_SEQDEPTH": {
+                    "r-base": "4.5.1",
+                    "r-ggplot2": "4.0.2"
+                },
+                "Workflow": {
+                    "nf-core/deepmutscan": "v1.0.0"
+                }
+            },
+            [
+                "deepmutscan_report.html",
+                "fastqc",
+                "fastqc/GID1A_input_1_pe_1_fastqc.html",
+                "fastqc/GID1A_input_1_pe_1_fastqc.zip",
+                "fastqc/GID1A_input_1_pe_2_fastqc.html",
+                "fastqc/GID1A_input_1_pe_2_fastqc.zip",
+                "fastqc/GID1A_input_2_pe_1_fastqc.html",
+                "fastqc/GID1A_input_2_pe_1_fastqc.zip",
+                "fastqc/GID1A_input_2_pe_2_fastqc.html",
+                "fastqc/GID1A_input_2_pe_2_fastqc.zip",
+                "fastqc/GID1A_output_1_pe_1_fastqc.html",
+                "fastqc/GID1A_output_1_pe_1_fastqc.zip",
+                "fastqc/GID1A_output_1_pe_2_fastqc.html",
+                "fastqc/GID1A_output_1_pe_2_fastqc.zip",
+                "fastqc/GID1A_output_2_pe_1_fastqc.html",
+                "fastqc/GID1A_output_2_pe_1_fastqc.zip",
+                "fastqc/GID1A_output_2_pe_2_fastqc.html",
+                "fastqc/GID1A_output_2_pe_2_fastqc.zip",
+                "fitness",
+                "fitness/DiMSum_results",
+                "fitness/DiMSum_results/dimsum_results",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_fitness_intermediate.RData",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_fitness_replicates.RData",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_indel_variant_data_merge.tsv",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_nobarcode_variant_data_merge.tsv",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_rejected_variant_data_merge.tsv",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_sessionInfo.RData",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_variant_data_merge.RData",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_variant_data_merge.tsv",
+                "fitness/DiMSum_results/dimsum_results/dimsum_results_workspace.RData",
+                "fitness/DiMSum_results/dimsum_results/fitness_doubles.txt",
+                "fitness/DiMSum_results/dimsum_results/fitness_singles.txt",
+                "fitness/DiMSum_results/dimsum_results/fitness_singles_MaveDB.csv",
+                "fitness/DiMSum_results/dimsum_results/fitness_synonymous.txt",
+                "fitness/DiMSum_results/dimsum_results/fitness_wildtype.txt",
+                "fitness/DiMSum_results/dimsum_results/report.html",
+                "fitness/DiMSum_results/dimsum_results/reports",
+                "fitness/DiMSum_results/dimsum_results/reports/report.Rmd",
+                "fitness/DiMSum_results/dimsum_results/reports/report_settings.RData",
+                "fitness/DiMSum_results/dimsum_results/tmp",
+                "fitness/DiMSum_results/dimsum_results/tmp/mutation_stats_dicts.RData",
+                "fitness/DiMSum_results/single_rep_counts",
+                "fitness/DiMSum_results/single_rep_counts/GID1A_input_1_pe_fitness_input.tsv",
+                "fitness/DiMSum_results/single_rep_counts/GID1A_input_2_pe_fitness_input.tsv",
+                "fitness/DiMSum_results/single_rep_counts/GID1A_output_1_pe_fitness_input.tsv",
+                "fitness/DiMSum_results/single_rep_counts/GID1A_output_2_pe_fitness_input.tsv",
+                "fitness/counts_merged.tsv",
+                "fitness/default_results",
+                "fitness/default_results/fitness_estimation.tsv",
+                "fitness/default_results/fitness_estimation_count_correlation.pdf",
+                "fitness/default_results/fitness_estimation_fitness_correlation.pdf",
+                "fitness/default_results/fitness_heatmap.pdf",
+                "fitness/experimentalDesign.tsv",
+                "fitness/mutscan_results",
+                "fitness/mutscan_results/fitness_estimation_mutscan_edgeR.tsv",
+                "fitness/mutscan_results/fitness_estimation_mutscan_limma.tsv",
+                "fitness/mutscan_results/mutscan_counts_corr.pdf",
+                "fitness/mutscan_results/mutscan_edgeR_volcano.pdf",
+                "fitness/mutscan_results/mutscan_limma_volcano.pdf",
+                "fitness/synonymous_wt.txt",
+                "intermediate_files",
+                "intermediate_files/aa_seq.txt",
+                "intermediate_files/bam_files",
+                "intermediate_files/bam_files/bwa",
+                "intermediate_files/bam_files/bwa/mem",
+                "intermediate_files/bam_files/bwa/mem/GID1A_input_1_pe.bam",
+                "intermediate_files/bam_files/bwa/mem/GID1A_input_2_pe.bam",
+                "intermediate_files/bam_files/bwa/mem/GID1A_output_1_pe.bam",
+                "intermediate_files/bam_files/bwa/mem/GID1A_output_2_pe.bam",
+                "intermediate_files/bam_files/filtered",
+                "intermediate_files/bam_files/filtered/GID1A_input_1_pe_filtered.bam",
+                "intermediate_files/bam_files/filtered/GID1A_input_2_pe_filtered.bam",
+                "intermediate_files/bam_files/filtered/GID1A_output_1_pe_filtered.bam",
+                "intermediate_files/bam_files/filtered/GID1A_output_2_pe_filtered.bam",
+                "intermediate_files/bam_files/premerged",
+                "intermediate_files/bam_files/premerged/GID1A_input_1_pe_merged.bam",
+                "intermediate_files/bam_files/premerged/GID1A_input_2_pe_merged.bam",
+                "intermediate_files/bam_files/premerged/GID1A_output_1_pe_merged.bam",
+                "intermediate_files/bam_files/premerged/GID1A_output_2_pe_merged.bam",
+                "intermediate_files/bam_files/sorted",
+                "intermediate_files/bam_files/sorted/GID1A_input_1_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_input_1_pe.sorted.bam.bai",
+                "intermediate_files/bam_files/sorted/GID1A_input_2_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_input_2_pe.sorted.bam.bai",
+                "intermediate_files/bam_files/sorted/GID1A_output_1_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_output_1_pe.sorted.bam.bai",
+                "intermediate_files/bam_files/sorted/GID1A_output_2_pe.sorted.bam",
+                "intermediate_files/bam_files/sorted/GID1A_output_2_pe.sorted.bam.bai",
+                "intermediate_files/bwa",
+                "intermediate_files/bwa/GID1A.amb",
+                "intermediate_files/bwa/GID1A.ann",
+                "intermediate_files/bwa/GID1A.bwt",
+                "intermediate_files/bwa/GID1A.pac",
+                "intermediate_files/bwa/GID1A.sa",
+                "intermediate_files/possible_mutations.csv",
+                "intermediate_files/processed_variant_counts",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/library_completed_variantCounts_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/seq_error_rate.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/variantCounts_filtered_by_library_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_1_pe/variantCounts_for_heatmaps_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/library_completed_variantCounts_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/seq_error_rate.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/variantCounts_filtered_by_library_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_input_2_pe/variantCounts_for_heatmaps_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/library_completed_variantCounts_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/seq_error_rate.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/variantCounts_filtered_by_library_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_1_pe/variantCounts_for_heatmaps_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/annotated_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/library_completed_variantCounts.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/library_completed_variantCounts_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/seq_error_rate.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/variantCounts_filtered_by_library.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/variantCounts_filtered_by_library_error_corrected.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/variantCounts_for_heatmaps.csv",
+                "intermediate_files/processed_variant_counts/GID1A_output_2_pe/variantCounts_for_heatmaps_error_corrected.csv",
+                "intermediate_files/variant_counts",
+                "intermediate_files/variant_counts/GID1A_input_1_pe",
+                "intermediate_files/variant_counts/GID1A_input_1_pe/GID1A_input_1_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_input_1_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_input_2_pe",
+                "intermediate_files/variant_counts/GID1A_input_2_pe/GID1A_input_2_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_input_2_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_output_1_pe",
+                "intermediate_files/variant_counts/GID1A_output_1_pe/GID1A_output_1_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_output_1_pe/variant_counts_columns.tsv",
+                "intermediate_files/variant_counts/GID1A_output_2_pe",
+                "intermediate_files/variant_counts/GID1A_output_2_pe/GID1A_output_2_pe.variant_counts.tsv",
+                "intermediate_files/variant_counts/GID1A_output_2_pe/variant_counts_columns.tsv",
+                "library_QC",
+                "library_QC/GID1A_input_1_pe",
+                "library_QC/GID1A_input_1_pe/SeqDepth.pdf",
+                "library_QC/GID1A_input_1_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_input_1_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_input_1_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_input_1_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_counts.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_input_1_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_input_1_pe/seqdepth_curve.csv",
+                "library_QC/GID1A_input_2_pe",
+                "library_QC/GID1A_input_2_pe/SeqDepth.pdf",
+                "library_QC/GID1A_input_2_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_input_2_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_input_2_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_input_2_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_counts.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_input_2_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_input_2_pe/seqdepth_curve.csv",
+                "library_QC/GID1A_output_1_pe",
+                "library_QC/GID1A_output_1_pe/SeqDepth.pdf",
+                "library_QC/GID1A_output_1_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_output_1_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_output_1_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_output_1_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_counts.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_output_1_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_output_1_pe/seqdepth_curve.csv",
+                "library_QC/GID1A_output_2_pe",
+                "library_QC/GID1A_output_2_pe/SeqDepth.pdf",
+                "library_QC/GID1A_output_2_pe/counts_heatmap.pdf",
+                "library_QC/GID1A_output_2_pe/counts_per_cov_heatmap.pdf",
+                "library_QC/GID1A_output_2_pe/logdiff_plot.pdf",
+                "library_QC/GID1A_output_2_pe/logdiff_varying_bases.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_counts.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_counts_per_cov.pdf",
+                "library_QC/GID1A_output_2_pe/rolling_coverage.pdf",
+                "library_QC/GID1A_output_2_pe/seqdepth_curve.csv",
+                "library_QC/error_correction_report.html",
+                "multiqc",
+                "multiqc/multiqc_data",
+                "multiqc/multiqc_data/fastqc-status-check-heatmap.txt",
+                "multiqc/multiqc_data/fastqc_adapter_content_plot.txt",
+                "multiqc/multiqc_data/fastqc_overrepresented_sequences_plot.txt",
+                "multiqc/multiqc_data/fastqc_per_base_n_content_plot.txt",
+                "multiqc/multiqc_data/fastqc_per_base_sequence_quality_plot.txt",
+                "multiqc/multiqc_data/fastqc_per_sequence_gc_content_plot_Counts.txt",
+                "multiqc/multiqc_data/fastqc_per_sequence_gc_content_plot_Percentages.txt",
+                "multiqc/multiqc_data/fastqc_per_sequence_quality_scores_plot.txt",
+                "multiqc/multiqc_data/fastqc_sequence_counts_plot.txt",
+                "multiqc/multiqc_data/fastqc_sequence_duplication_levels_plot.txt",
+                "multiqc/multiqc_data/fastqc_top_overrepresented_sequences_table.txt",
+                "multiqc/multiqc_data/llms-full.txt",
+                "multiqc/multiqc_data/multiqc.log",
+                "multiqc/multiqc_data/multiqc.parquet",
+                "multiqc/multiqc_data/multiqc_citations.txt",
+                "multiqc/multiqc_data/multiqc_data.json",
+                "multiqc/multiqc_data/multiqc_fastqc.txt",
+                "multiqc/multiqc_data/multiqc_general_stats.txt",
+                "multiqc/multiqc_data/multiqc_software_versions.txt",
+                "multiqc/multiqc_data/multiqc_sources.txt",
+                "multiqc/multiqc_report.html",
+                "pipeline_info",
+                "pipeline_info/nf_core_deepmutscan_software_mqc_versions.yml",
+                "variant_effect_inspection_tool",
+                "variant_effect_inspection_tool/structure_data.json",
+                "variant_effect_inspection_tool/variant_effect_inspection_tool.html"
+            ],
+            [
+                "fitness_doubles.txt:md5,68b329da9893e34099c7d8ad5cb9c940",
+                "fitness_singles.txt:md5,df4effcdf5477327ab7e74034498ddc8",
+                "fitness_singles_MaveDB.csv:md5,160ad250f0774e770b5128840b62ba80",
+                "fitness_wildtype.txt:md5,934b2d1b64746b4130d99e7bd46d5595",
+                "report.Rmd:md5,a19acae87f16d0059c6699bc1e818d95",
+                "experimentalDesign.tsv:md5,f95dea4a3e6cf7e206e789a96e502604",
+                "synonymous_wt.txt:md5,bb94c5975185b80f8d63acebfbf4a0da",
+                "aa_seq.txt:md5,421287586f566191f7da4a7aad7d24a8",
+                "GID1A_input_1_pe.bam:md5,57f54fa2bad68dc8ff39d2bb79d94742",
+                "GID1A_input_2_pe.bam:md5,5da708382cddfaa90e7b53e3d79d8f8e",
+                "GID1A_output_1_pe.bam:md5,f888077ed2234b2b7b033ceea621b33f",
+                "GID1A_output_2_pe.bam:md5,5b147ba80d87ee3ff3be6c3f34d7f67d",
+                "GID1A_input_1_pe_filtered.bam:md5,9a8f3691ec0478928227fe0da590054b",
+                "GID1A_input_2_pe_filtered.bam:md5,50f50c79514041c8746fb95070fbf321",
+                "GID1A_output_1_pe_filtered.bam:md5,b743b1fd86941c392c7d42474a82217d",
+                "GID1A_output_2_pe_filtered.bam:md5,20e02c4c5f0ba2ff9f9b553e80a89343",
+                "GID1A_input_1_pe_merged.bam:md5,e83e32f077c1f8018dd7cda8a27828fd",
+                "GID1A_input_2_pe_merged.bam:md5,eeb0a34e585e1858d3b758d6bde3b8db",
+                "GID1A_output_1_pe_merged.bam:md5,ed034511d33464cd9f032b8824663842",
+                "GID1A_output_2_pe_merged.bam:md5,9bafd9eee748877ac559815b1420b865",
+                "GID1A_input_1_pe.sorted.bam:md5,80311313f5d8b1387130ba376bd8f2b9",
+                "GID1A_input_1_pe.sorted.bam.bai:md5,644255e89a4f621627ee67d5884fe79e",
+                "GID1A_input_2_pe.sorted.bam:md5,084adf0c95308ceef7eba177999ed6fe",
+                "GID1A_input_2_pe.sorted.bam.bai:md5,8f5580680b4aa02ea4800f422d366a8b",
+                "GID1A_output_1_pe.sorted.bam:md5,95a6016ac42a49925b3300ecb57465f3",
+                "GID1A_output_1_pe.sorted.bam.bai:md5,38e8757743fca3dbd3b1f44c1cb49475",
+                "GID1A_output_2_pe.sorted.bam:md5,73b5c65751b796b6565ba18b316935ab",
+                "GID1A_output_2_pe.sorted.bam.bai:md5,f032b0115c80e79b05eb757bd75445e1",
+                "GID1A.amb:md5,383ef17f6abb846ab669ebac30f5659b",
+                "GID1A.ann:md5,30c4a2204f9166d9594c790e0ca58793",
+                "GID1A.bwt:md5,120149e0239465625c2dd20a171701fd",
+                "GID1A.pac:md5,104f743d81d39b2527b5ae52db26654c",
+                "GID1A.sa:md5,1f697dcb3fedd2f3f29642598bdd795b",
+                "possible_mutations.csv:md5,9ce59fe91b726b1d88587a13fa899567",
+                "seq_error_rate.csv:md5,350a018c981eca2506ac6291a6e9e161",
+                "variantCounts_for_heatmaps.csv:md5,842bd5c00760d594e2a450aec929e4aa",
+                "variantCounts_for_heatmaps_error_corrected.csv:md5,842bd5c00760d594e2a450aec929e4aa",
+                "seq_error_rate.csv:md5,350a018c981eca2506ac6291a6e9e161",
+                "variantCounts_for_heatmaps.csv:md5,44069d5abca76323ccaed3c182447104",
+                "variantCounts_for_heatmaps_error_corrected.csv:md5,44069d5abca76323ccaed3c182447104",
+                "seq_error_rate.csv:md5,350a018c981eca2506ac6291a6e9e161",
+                "variantCounts_for_heatmaps.csv:md5,3a9ce900a567cb663bc71033d3771d8a",
+                "variantCounts_for_heatmaps_error_corrected.csv:md5,3a9ce900a567cb663bc71033d3771d8a",
+                "seq_error_rate.csv:md5,350a018c981eca2506ac6291a6e9e161",
+                "variantCounts_for_heatmaps.csv:md5,f263448ec0d7ec68f6e23aa050b961e6",
+                "variantCounts_for_heatmaps_error_corrected.csv:md5,f263448ec0d7ec68f6e23aa050b961e6",
+                "variant_counts_columns.tsv:md5,c94801255a553ce5ff4a00d3170f6768",
+                "variant_counts_columns.tsv:md5,c94801255a553ce5ff4a00d3170f6768",
+                "variant_counts_columns.tsv:md5,c94801255a553ce5ff4a00d3170f6768",
+                "variant_counts_columns.tsv:md5,c94801255a553ce5ff4a00d3170f6768",
+                "seqdepth_curve.csv:md5,eceecf3eff21b17b1d818c6ccefa72d9",
+                "seqdepth_curve.csv:md5,04649f2dd97ae3f7f4d7a94efb299d78",
+                "seqdepth_curve.csv:md5,5bde4b2a2b0cce57f4696913f4cdf8e7",
+                "seqdepth_curve.csv:md5,0deb002959055376128c81bcfba8e0e2",
+                "error_correction_report.html:md5,45455144c768473a8242381380deeba8",
+                "fastqc-status-check-heatmap.txt:md5,58a9580c11af4210b1af65753383e120",
+                "fastqc_adapter_content_plot.txt:md5,2990ad34b6fa29151dd4b9a38828afbe",
+                "fastqc_overrepresented_sequences_plot.txt:md5,c703a9df0ea3a5c1c5f515a1fd88bbe8",
+                "fastqc_per_base_n_content_plot.txt:md5,6af419871cdec77149cf013b72e82417",
+                "fastqc_per_base_sequence_quality_plot.txt:md5,460b8e65cf8d58087b3143ba78b30e17",
+                "fastqc_per_sequence_gc_content_plot_Counts.txt:md5,7f1eacaa19e912e18a4419b23e749e3a",
+                "fastqc_per_sequence_gc_content_plot_Percentages.txt:md5,0e405d9394ae8aeaa6ae162e18f7c195",
+                "fastqc_per_sequence_quality_scores_plot.txt:md5,5d1ee338c146901ca9f1690f7ee47200",
+                "fastqc_sequence_counts_plot.txt:md5,8b438078b7f3475b6ce659cc4b8dca11",
+                "fastqc_sequence_duplication_levels_plot.txt:md5,a0839f96103959fd7265cbb986e2724a",
+                "multiqc_citations.txt:md5,4c806e63a283ec1b7e78cdae3a923d4f",
+                "multiqc_fastqc.txt:md5,72cb1ee38d354d674ced1b4b21bbeff4",
+                "multiqc_general_stats.txt:md5,7c2fd44ded06835128a208d3365aa23b"
+            ],
+            {
+                "edger_rowCount": 1231,
+                "limma_rowCount": 1231
+            }
+        ],
+        "timestamp": "2026-07-22T20:08:15.261389",
+        "meta": {
+            "nf-test": "0.9.4",
+            "nextflow": "26.04.1"
+        }
+    }
+}
\ No newline at end of file
diff --git a/tests/nextflow.config b/tests/nextflow.config
index bcd98b2..b4075e0 100644
--- a/tests/nextflow.config
+++ b/tests/nextflow.config
@@ -4,11 +4,26 @@
 ========================================================================================
 */
 
-// TODO nf-core: Specify any additional parameters here
-// Or any resources requirements
+process {
+    resourceLimits = [
+        cpus: 4,
+        memory: '8.GB',
+        time: '1.h'
+    ]
+}
+
 params {
     modules_testdata_base_path = 'https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/'
     pipelines_testdata_base_path = 'https://raw.githubusercontent.com/nf-core/test-datasets/refs/heads/deepmutscan/'
+
+    // Input data
+    input = params.pipelines_testdata_base_path + 'samplesheet/GID1A_test.csv'
+    fasta = params.pipelines_testdata_base_path + 'testdata/GID1A.fasta'
+    reading_frame = '352-1383'
+    min_counts = 2
+    mutagenesis_type = 'nnk_nns'
+    run_seqdepth = true
+    fitness = true
 }
 
 aws.client.anonymous = true // fixes S3 access issues on self-hosted runners
diff --git a/workflows/deepmutscan.nf b/workflows/deepmutscan.nf
index aaafe3f..36ed769 100644
--- a/workflows/deepmutscan.nf
+++ b/workflows/deepmutscan.nf
@@ -5,6 +5,31 @@
 */
 include { FASTQC                 } from '../modules/nf-core/fastqc/main'
 include { MULTIQC                } from '../modules/nf-core/multiqc/main'
+include { BWA_INDEX              } from '../modules/nf-core/bwa/index/main'
+include { BWA_MEM                } from '../modules/nf-core/bwa/mem/main'
+include { BAMFILTER_DMS          } from '../modules/local/bamprocessing/bam_filter/main'
+include { PREMERGE               } from '../modules/local/bamprocessing/premerge/main'
+include { SAMTOOLS_SORT              } from '../modules/nf-core/samtools/sort/main'
+include { SAMTOOLS_INDEX             } from '../modules/nf-core/samtools/index/main'
+include { VARIANTCOUNTING            } from '../modules/local/variantcounting/main'
+include { DMSANALYSIS_AASEQ      } from '../modules/local/dmsanalysis/aa_seq/main'
+include { DMSANALYSIS_POSSIBLE_MUTATIONS      } from '../modules/local/dmsanalysis/possible_mutations/main'
+include { DMSANALYSIS_PROCESS_VARIANT_COUNTS      } from '../modules/local/dmsanalysis/process_variant_counts/main'
+include { DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES } from '../modules/local/dmsanalysis/error_correction_false_doubles/main'
+include { DMSANALYSIS_ERROR_CORRECTION_WILDTYPE      } from '../modules/local/dmsanalysis/error_correction_wildtype/main'
+include { VISUALIZATION_COUNTS_PER_COV      } from '../modules/local/visualization/counts_per_cov/main'
+include { VISUALIZATION_COUNTS_HEATMAP      } from '../modules/local/visualization/counts_heatmap/main'
+include { VISUALIZATION_GLOBAL_POS_BIASES_COUNTS      } from '../modules/local/visualization/global_pos_biases_counts/main'
+include { VISUALIZATION_GLOBAL_POS_BIASES_COV      } from '../modules/local/visualization/global_pos_biases_cov/main'
+include { VISUALIZATION_LOGDIFF      } from '../modules/local/visualization/logdiff/main'
+include { VISUALIZATION_SEQDEPTH      } from '../modules/local/visualization/seqdepth/main'
+include { VISUALIZATION_ERROR_CORRECTION_REPORT } from '../modules/local/visualization/error_correction_report/main'
+include { VISUALIZATION_SUMMARY_REPORT } from '../modules/local/visualization/summary_report/main'
+include { VARIANT_EFFECT_INSPECTION_TOOL } from '../modules/local/structure/variant_effect_inspection_tool/main'
+include { COUNTS_TO_FITNESS          } from '../modules/local/fitness/counts_to_fitness/main'
+
+include { CALCULATE_FITNESS } from '../subworkflows/local/calculate_fitness/main'
+
 include { paramsSummaryMap       } from 'plugin/nf-schema'
 include { paramsSummaryMultiqc   } from '../subworkflows/nf-core/utils_nfcore_pipeline'
 include { softwareVersionsToYAML } from '../subworkflows/nf-core/utils_nfcore_pipeline'
@@ -27,14 +52,380 @@ workflow DEEPMUTSCAN {
 
     main:
 
-    def ch_versions = channel.empty()
-    def ch_multiqc_files = channel.empty()
+    def ch_versions = Channel.empty()
+    def ch_multiqc_files = Channel.empty()
+
+    // Define input channels from parameters
+    def ch_fasta = Channel
+        .fromPath(params.fasta, checkIfExists: true)
+        .map { fasta -> tuple( [id: 'ref'], fasta ) }
+
+    def reading_frame_ch        = Channel.value(params.reading_frame)
+    def min_counts_ch           = Channel.value(params.min_counts)
+    def custom_codon_library_ch = Channel.value(params.custom_codon_library)
+    def mutagenesis_type_ch     = Channel.value(params.mutagenesis_type)
+    def sliding_window_size_ch  = Channel.value(params.sliding_window_size)
+    def aimed_cov_ch            = Channel.value(params.aimed_cov)
+    def run_seqdepth_ch         = Channel.value(params.run_seqdepth)
+    def base_qual_ch            = Channel.value(params.base_qual)
+    def min_flank_ch            = Channel.value(params.min_flank)
+
+    // Raw samplesheet path channel for downstream subworkflows
+    def ch_samplesheet_csv      = Channel.fromPath(params.input, checkIfExists: true)
+
+    // '--dimsum' only has an effect together with '--fitness'
+    if (params.dimsum && !params.fitness) {
+        log.warn("'--dimsum true' only works together with '--fitness true'. DiMSum will be skipped.")
+    }
+
+    // '--error_correction wildtype' needs a 'wildtype' sample for every biological sample
+    if (params.error_correction == 'wildtype') {
+        ch_samplesheet
+            .map { meta, _reads -> tuple(meta.sample as String, meta.type as String) }
+            .toList()
+            .map { pairs ->
+                pairs.groupBy { it[0] }.each { sample, rows ->
+                    def types = rows.collect { it[1] }
+                    if (types.any { it != 'wildtype' } && !types.contains('wildtype')) {
+                        error(
+                            "[--error_correction wildtype] No 'wildtype' sample found for '${sample}' in the samplesheet.\n" +
+                            "  Add a samplesheet row with type 'wildtype' and sample '${sample}' (deep wildtype-only sequencing),\n" +
+                            "  or use the internal correction instead (default: --error_correction false_doubles),\n" +
+                            "  or disable error correction with --error_correction none."
+                        )
+                    }
+                }
+                pairs
+            }
+            .subscribe { }
+    }
+
+    // Nudge fitness users who did not supply a structure towards the interactive tool
+    if (params.fitness && !params.pdb) {
+        log.info(
+            "\n" +
+            "  ┌─ Optional: interactive variant effect inspection tool ───────────────────┐\n" +
+            "     Supply a wildtype structure with '--pdb <structure.pdb>' to also build an\n" +
+            "     interactive HTML tool that projects fitness, counts and error-correction\n" +
+            "     biases onto the 3D structure to support data interpretation.\n" +
+            "  └───────────────────────────────────────────────────────────────────────────┘\n"
+        )
+    }
+
     //
     // MODULE: Run FastQC
     //
     FASTQC(ch_samplesheet)
     ch_multiqc_files = ch_multiqc_files.mix(FASTQC.out.zip.map{ _meta, file -> file })
 
+    //
+    // MODULE: BWA Index
+    //
+    BWA_INDEX (
+        ch_fasta
+    )
+
+    // Broadcast index to all samples
+    def ch_bwa_index = BWA_INDEX.out.index
+
+    // Broadcast the index to all samples
+    def ch_bwa_index_broadcast = ch_samplesheet
+      .combine(ch_bwa_index)
+      .map { [it[2], it[3]] }
+
+    // Broadcast the fasta to all samples
+    def ch_fasta_broadcast = ch_fasta
+      .combine(ch_samplesheet)
+      .map { [it[0], it[1]] }
+
+    // Broadcast the sort flag to all samples
+    def ch_sort_bam = ch_samplesheet.map { false }
+
+    // Run BWA_MEM with all four inputs aligned
+    BWA_MEM(
+      ch_samplesheet,
+      ch_bwa_index_broadcast,
+      ch_fasta_broadcast,
+      ch_sort_bam
+    )
+
+    BAMFILTER_DMS (
+      BWA_MEM.out.bam
+    )
+
+    // Broadcast the FASTA path to every BAM emitted by BAMFILTER_DMS
+    def ch_fasta_path_broadcast = ch_fasta
+      .combine(BAMFILTER_DMS.out.bam)   // flattened item: [meta3, fasta, meta, bam]
+      .map { it[1] }                    // keep only the fasta path (N emissions)
+
+    PREMERGE(
+      BAMFILTER_DMS.out.bam,     // tuple(val(meta), path(bam))
+      ch_fasta_path_broadcast    // path(fasta)
+    )
+
+    // Coordinate-sort + index the premerged BAM (required by the read-based counter)
+    SAMTOOLS_SORT(
+      PREMERGE.out.bam,                                        // tuple(val(meta), path(bam))
+      PREMERGE.out.bam.map { _meta, _bam -> [ [:], [], [] ] }, // empty fasta tuple (BAM sort)
+      Channel.value('')                                        // index_format '' -> index separately
+    )
+    SAMTOOLS_INDEX(SAMTOOLS_SORT.out.bam)
+
+    // Join sorted BAM with its index -> tuple(val(meta), path(bam), path(bai))
+    def ch_sorted_indexed = SAMTOOLS_SORT.out.bam.join(SAMTOOLS_INDEX.out.index)
+
+    // Broadcast singletons to N (one per sample), anchored on the sorted+indexed channel
+    def ch_fasta_for_counts = ch_fasta.combine(ch_sorted_indexed).map { it[1] }          // path -- N
+    def ch_rf_for_counts    = reading_frame_ch.combine(ch_sorted_indexed).map { it[0] }  // val  -- N
+    def ch_min_for_counts   = min_counts_ch.combine(ch_sorted_indexed).map { it[0] }     // val  -- N
+    def ch_bq_for_counts    = base_qual_ch.combine(ch_sorted_indexed).map { it[0] }      // val  -- N
+    def ch_flank_for_counts = min_flank_ch.combine(ch_sorted_indexed).map { it[0] }      // val  -- N
+
+    VARIANTCOUNTING(
+      ch_sorted_indexed,   // tuple(val(meta), path(bam), path(bai))
+      ch_fasta_for_counts, // path(fasta)
+      ch_rf_for_counts,    // val(reading_frame string, 1-based inclusive)
+      ch_min_for_counts,   // val(min_counts)
+      ch_bq_for_counts,    // val(base_qual)
+      ch_flank_for_counts  // val(min_flank)
+    )
+    ch_versions = ch_versions.mix(VARIANTCOUNTING.out.versions)
+
+    DMSANALYSIS_AASEQ (
+      ch_fasta,
+      reading_frame_ch
+    )
+    ch_versions = ch_versions.mix(DMSANALYSIS_AASEQ.out.versions)
+
+    DMSANALYSIS_POSSIBLE_MUTATIONS(
+      ch_fasta,
+      reading_frame_ch,           // pos_range (as val)
+      mutagenesis_type_ch,        // mutagenesis_type (as val)
+      custom_codon_library_ch     // custom_codon_library (as path)
+    )
+    ch_versions = ch_versions.mix(DMSANALYSIS_POSSIBLE_MUTATIONS.out.versions)
+
+    // Anchor (N items; one per sample)
+    def ch_vc = VARIANTCOUNTING.out.variant_counts   // tuple(val(meta), path)
+
+    // Build per-sample inputs using inline combinations (replaces fanout)
+    def ch_possible_mut_for_proc = DMSANALYSIS_POSSIBLE_MUTATIONS.out.possible_mutations.map { it[1] }.combine(ch_vc).map { it[0] }
+    def ch_aa_seq_for_proc       = DMSANALYSIS_AASEQ.out.aa_seq.map { it[1] }.combine(ch_vc).map { it[0] }
+    def ch_min_counts_for_proc   = min_counts_ch.combine(ch_vc).map { it[0] }
+
+    // Call with all inputs aligned (each has N items now)
+    DMSANALYSIS_PROCESS_VARIANT_COUNTS(
+      ch_vc,                        // tuple(val(meta), path(variantCounts))  -- N
+      ch_possible_mut_for_proc,     // path(possible_mutations)               -- N
+      ch_aa_seq_for_proc,           // path(aa_seq)                           -- N
+      ch_min_counts_for_proc        // val(min_counts)                        -- N
+    )
+
+    def annotated_variantCounts_ch           = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, a) }
+    def variantCounts_filtered_by_library_ch = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, b) }
+    def library_completed_variantCounts_ch   = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, c) }
+    def variantCounts_for_heatmaps_ch        = DMSANALYSIS_PROCESS_VARIANT_COUNTS.out.processed_variantCounts.map { meta, a, b, c, d -> tuple(meta, d) }
+
+    //
+    // Sequencing-error correction of single-codon counts (default: false_doubles).
+    // Replaces the filtered + heatmap channels with corrected versions; downstream fitness
+    // picks up correction via the `counts_corrected` column, count heatmaps via canonical names.
+    //
+    if (params.error_correction == 'false_doubles') {
+      // per sample: raw (pre-filter) counts + library-filtered counts + completed library table
+      def ch_fd_in       = ch_vc.join(variantCounts_filtered_by_library_ch).join(library_completed_variantCounts_ch)  // (meta, raw, filtered, completed)
+      def aa_seq_for_ec  = DMSANALYSIS_AASEQ.out.aa_seq.map { it[1] }.combine(ch_fd_in).map { it[0] }
+      def min_for_ec     = min_counts_ch.combine(ch_fd_in).map { it[0] }
+      // the reference the reads were aligned to (base_mut is in this fasta's coordinate frame),
+      // broadcast one-per-sample; the estimator and the mode/window are run-level constants.
+      // ch_fasta is tuple(meta, fasta), so the path is it[1] - it[0] is the [id:ref] meta map.
+      def fasta_for_ec   = ch_fasta.combine(ch_fd_in).map { it[1] }
+      DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES(
+        ch_fd_in, fasta_for_ec, aa_seq_for_ec, min_for_ec,
+        params.false_doubles_method, params.false_doubles_codon_window
+      )
+      ch_versions = ch_versions.mix(DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.versions)
+      variantCounts_filtered_by_library_ch = DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.corrected.map { meta, filt, heat, comp -> tuple(meta, filt) }
+      variantCounts_for_heatmaps_ch        = DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.corrected.map { meta, filt, heat, comp -> tuple(meta, heat) }
+      library_completed_variantCounts_ch   = DMSANALYSIS_ERROR_CORRECTION_FALSE_DOUBLES.out.corrected.map { meta, filt, heat, comp -> tuple(meta, comp) }
+    }
+    else if (params.error_correction == 'wildtype') {
+      // pair each non-wildtype sample with the wildtype sample of the same `sample` base
+      def filt_comp = variantCounts_filtered_by_library_ch.join(library_completed_variantCounts_ch)  // (meta, filtered, completed)
+      def wt_ch     = filt_comp.filter { meta, f, c -> meta.type == 'wildtype' }.map { meta, f, c -> tuple(meta.sample, f) }
+      def tgt_ch    = filt_comp.filter { meta, f, c -> meta.type != 'wildtype' }.map { meta, f, c -> tuple(meta.sample, meta, f, c) }
+      def ch_wt_in  = tgt_ch.combine(wt_ch, by: 0).map { _base, meta, filt, comp, wtf -> tuple(meta, filt, wtf, comp) }  // (meta, filtered, wt_filtered, completed)
+      def aa_seq_for_ec = DMSANALYSIS_AASEQ.out.aa_seq.map { it[1] }.combine(ch_wt_in).map { it[0] }
+      def min_for_ec    = min_counts_ch.combine(ch_wt_in).map { it[0] }
+      DMSANALYSIS_ERROR_CORRECTION_WILDTYPE( ch_wt_in, aa_seq_for_ec, min_for_ec )
+      ch_versions = ch_versions.mix(DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.versions)
+      variantCounts_filtered_by_library_ch = DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.corrected.map { meta, filt, heat, comp -> tuple(meta, filt) }
+      variantCounts_for_heatmaps_ch        = DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.corrected.map { meta, filt, heat, comp -> tuple(meta, heat) }
+      library_completed_variantCounts_ch   = DMSANALYSIS_ERROR_CORRECTION_WILDTYPE.out.corrected.map { meta, filt, heat, comp -> tuple(meta, comp) }
+    }
+    // else 'none': keep the uncorrected channels
+
+    // Artefacts the all-in-one report embeds, collected as [[kind, group, name], file]. Optional
+    // parts simply never mix anything in, and the report drops the corresponding section.
+    def ch_report_assets = Channel.empty()
+
+    //
+    // MODULE: per-sample error-correction report (only when correction was applied)
+    //
+    if (params.error_correction != 'none') {
+      // One run-level report over every file. Sorting first keeps the id list and the staged files in
+      // the same order, which is the only thing tying a column of numbers to the file it came from.
+      def ch_ec = variantCounts_filtered_by_library_ch.toSortedList { a, b -> a[0].id <=> b[0].id }
+
+      VISUALIZATION_ERROR_CORRECTION_REPORT(
+        ch_ec.map { rows -> rows[0][0].sample ?: 'run' },
+        ch_ec.map { rows -> groovy.json.JsonOutput.toJson(rows.collect { [id: it[0].id, type: it[0].type, replicate: it[0].replicate] }).bytes.encodeBase64().toString() },
+        ch_ec.map { rows -> rows.collect { it[1] } },
+        params.error_correction,
+        params.false_doubles_method,
+        file("${projectDir}/assets/error_correction_report/ec_report_template.html", checkIfExists: true)
+      )
+      ch_versions = ch_versions.mix(VISUALIZATION_ERROR_CORRECTION_REPORT.out.versions)
+      ch_report_assets = ch_report_assets.mix(
+        VISUALIZATION_ERROR_CORRECTION_REPORT.out.report.map { f -> [['report', 'ec', 'Error correction'], f] }
+      )
+    }
+
+    // --- For VISUALIZATION_COUNTS_PER_COV & HEATMAP (replaces fanoutTo)
+    def min_counts_for_cov_ch          = min_counts_ch.combine(variantCounts_for_heatmaps_ch).map { it[0] }
+    def min_counts_for_heatmap_ch      = min_counts_ch.combine(variantCounts_for_heatmaps_ch).map { it[0] }
+
+    // --- For VISUALIZATION_GLOBAL_POS_BIASES_*
+    def aa_seq_for_bias_ch             = DMSANALYSIS_AASEQ.out.aa_seq.map { it[1] }.combine(variantCounts_filtered_by_library_ch).map { it[0] }
+    def sliding_window_size_N          = sliding_window_size_ch.combine(variantCounts_filtered_by_library_ch).map { it[0] }
+    def aimed_cov_N                    = aimed_cov_ch.combine(variantCounts_filtered_by_library_ch).map { it[0] }
+
+    // --- For VISUALIZATION_SEQDEPTH
+    def possible_mutations_N           = DMSANALYSIS_POSSIBLE_MUTATIONS.out.possible_mutations.map { it[1] }.combine(variantCounts_filtered_by_library_ch).map { it[0] }
+    def min_counts_for_seqdepth_ch     = min_counts_ch.combine(variantCounts_filtered_by_library_ch).map { it[0] }
+
+    VISUALIZATION_COUNTS_PER_COV(
+      variantCounts_for_heatmaps_ch,
+      min_counts_for_cov_ch
+    )
+
+    VISUALIZATION_COUNTS_HEATMAP(
+      variantCounts_for_heatmaps_ch,
+      min_counts_for_heatmap_ch
+    )
+
+    VISUALIZATION_GLOBAL_POS_BIASES_COUNTS(
+      variantCounts_filtered_by_library_ch,
+      aa_seq_for_bias_ch,
+      sliding_window_size_N
+    )
+
+    VISUALIZATION_GLOBAL_POS_BIASES_COV(
+      variantCounts_filtered_by_library_ch,
+      aa_seq_for_bias_ch,
+      sliding_window_size_N,
+      aimed_cov_N
+    )
+
+    VISUALIZATION_LOGDIFF(
+      library_completed_variantCounts_ch
+    )
+
+    // library QC plots (original R PDFs) + the tables the report re-plots interactively
+    ch_report_assets = ch_report_assets
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COV.out.rolling_coverage.map          { meta, f -> [['qc', meta.id, 'rolling_coverage.pdf'], f] })
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COUNTS.out.rolling_counts.map         { meta, f -> [['qc', meta.id, 'rolling_counts.pdf'], f] })
+      .mix(VISUALIZATION_GLOBAL_POS_BIASES_COUNTS.out.rolling_counts_per_cov.map { meta, f -> [['qc', meta.id, 'rolling_counts_per_cov.pdf'], f] })
+      .mix(VISUALIZATION_COUNTS_HEATMAP.out.counts_heatmap.map                   { meta, f -> [['qc', meta.id, 'counts_heatmap.pdf'], f] })
+      .mix(VISUALIZATION_COUNTS_PER_COV.out.counts_per_cov_heatmap.map           { meta, f -> [['qc', meta.id, 'counts_per_cov_heatmap.pdf'], f] })
+      .mix(VISUALIZATION_LOGDIFF.out.logdiff_plot.map                            { meta, f -> [['qc', meta.id, 'logdiff_plot.pdf'], f] })
+      .mix(VISUALIZATION_LOGDIFF.out.logdiff_varying_bases.map                   { meta, f -> [['qc', meta.id, 'logdiff_varying_bases.pdf'], f] })
+      .mix(variantCounts_filtered_by_library_ch.map                              { meta, f -> [['filtered', meta.id, 'counts.csv'], f] })
+      .mix(variantCounts_for_heatmaps_ch.map                                     { meta, f -> [['heatmap', meta.id, 'heatmap.csv'], f] })
+      // Fixes the ORF axis at the wildtype length: positions with no library coverage still have to
+      // occupy a slot, or every rolling window downstream is computed over the wrong neighbourhood.
+      .mix(DMSANALYSIS_AASEQ.out.aa_seq.map                                      { _meta, f -> [['aa_seq', 'run', 'aa_seq.txt'], f] })
+      // Needed to name the exact nucleotide change behind each heatmap cell: the fitness table gives
+      // the variant ORFs, and the wildtype to diff them against only exists in the reference.
+      .mix(ch_fasta.map                                                          { _meta, f -> [['fasta', 'run', 'reference.fasta'], f] })
+
+    if (params.run_seqdepth) {
+      VISUALIZATION_SEQDEPTH(
+        variantCounts_filtered_by_library_ch,
+        possible_mutations_N,
+        min_counts_for_seqdepth_ch
+      )
+      ch_versions = ch_versions.mix(VISUALIZATION_SEQDEPTH.out.versions)
+      // The rarefaction curve travels to the summary report, which re-plots it per file under
+      // Sequencing QC (the PDF is still written to the results folder for the record).
+      ch_report_assets = ch_report_assets.mix(
+        VISUALIZATION_SEQDEPTH.out.curve.map { meta, f -> [['seqdepth', meta.id, 'seqdepth.csv'], f] }
+      )
+    }
+
+    // Broadcast singletons to N (one per sample), anchored on variantCounts_filtered_by_library_ch
+    def ch_fasta_for_fitness    = ch_fasta.combine(variantCounts_filtered_by_library_ch).map { it[1] }      // path(fasta) -- N
+    def ch_rf_for_fitness       = reading_frame_ch.combine(variantCounts_filtered_by_library_ch).map { it[0] }  // val(range) -- N
+
+    // Call with aligned inputs
+    COUNTS_TO_FITNESS(
+      variantCounts_filtered_by_library_ch, // tuple(val(meta), path)
+      ch_fasta_for_fitness,         // path(fasta)
+      ch_rf_for_fitness             // val(reading_frame)
+    )
+
+    // Execution of fitness subworkflow, if --fitness true
+    if (params.fitness) {
+
+        CALCULATE_FITNESS (
+            COUNTS_TO_FITNESS.out.fitness_input, // Input from previous step
+            ch_samplesheet_csv,                   // Path to samplesheet
+            ch_fasta,                             // The original Fasta tuple
+            reading_frame_ch,                     // Reading frame value channel
+            DMSANALYSIS_AASEQ.out.aa_seq          // Amino Acid Sequence (for Heatmap)
+        )
+
+        // Collect versions
+        ch_versions = ch_versions.mix(CALCULATE_FITNESS.out.versions)
+
+        ch_report_assets = ch_report_assets
+          .mix(CALCULATE_FITNESS.out.fitness_estimation.first().map { _m, f -> [['fitness_table', 'default', 'fitness_estimation.tsv'], f] })
+          .mix(CALCULATE_FITNESS.out.fitness_plots.map { _m, f -> [['fitness_pdf', 'default', f.name], f] })
+          // mutscan emits all of its plots as one list, so flatten to a file per entry
+          .mix(CALCULATE_FITNESS.out.mutscan_plots.transpose().map { _m, f -> [['mutscan_pdf', 'mutscan', f.name], f] })
+          .mix(CALCULATE_FITNESS.out.dimsum_dir.flatten().filter { it.name == 'report.html' }.map { f -> [['report', 'dimsum', 'DiMSum'], f] })
+
+        //
+        // MODULE: interactive variant effect inspection tool (one self-contained HTML per sample).
+        // Runs only when a wildtype structure is supplied via --pdb. Projects fitness, counts and
+        // error-correction biases onto the 3D structure. (Structure prediction is not wired here.)
+        //
+        if (params.pdb) {
+            def ch_structure = Channel
+                .fromPath(params.pdb, checkIfExists: true)
+                .map { pdb -> tuple([id: 'wildtype'], pdb) }
+            def ch_viewer_input = CALCULATE_FITNESS.out.fitness_estimation
+                .combine(ch_structure)
+                .map { smeta, fitness_tsv, _stmeta, pdb -> tuple(smeta, pdb, fitness_tsv) }
+            VARIANT_EFFECT_INSPECTION_TOOL(
+                ch_viewer_input,
+                variantCounts_filtered_by_library_ch.map { _meta, file -> file }.collect(),
+                DMSANALYSIS_AASEQ.out.aa_seq.map { _meta, file -> file }.first(),
+                file("${projectDir}/assets/variant_effect_inspection_tool/3Dmol-min.js",         checkIfExists: true),
+                file("${projectDir}/assets/variant_effect_inspection_tool/viewer_template.html", checkIfExists: true),
+            )
+            ch_versions = ch_versions.mix(VARIANT_EFFECT_INSPECTION_TOOL.out.versions)
+            ch_report_assets = ch_report_assets.mix(
+                VARIANT_EFFECT_INSPECTION_TOOL.out.viewer.map { _meta, f -> [['report', 'viewer', 'Variant effect inspection tool'], f] }
+            )
+            // The raw structure also travels to the summary report, which builds its own slow-spinning
+            // fitness-coloured viewer on the Variant effects page from it.
+            ch_report_assets = ch_report_assets.mix(
+                ch_structure.map { _meta, pdb -> [['pdb', 'structure', 'structure.pdb'], pdb] }
+            )
+        }
+    }
+
     //
     // Collate and save software versions
     //
@@ -76,6 +467,7 @@ workflow DEEPMUTSCAN {
         : file("${projectDir}/assets/methods_description_template.yml", checkIfExists: true)
     def ch_methods_description = channel.value(methodsDescriptionText(ch_multiqc_custom_methods_description))
     ch_multiqc_files = ch_multiqc_files.mix(ch_methods_description.collectFile(name: 'methods_description_mqc.yaml', sort: true))
+
     MULTIQC(
         ch_multiqc_files.flatten().collect().map { files ->
             [
@@ -90,8 +482,81 @@ workflow DEEPMUTSCAN {
             ]
         }
     )
-    emit:multiqc_report = MULTIQC.out.report.map { _meta, report -> [report] }.toList() // channel: /path/to/multiqc_report.html
+
+    //
+    // MODULE: the single all-in-one report. Runs last, because it embeds every other artefact -
+    // the MultiQC report included - as a base64 data URI, so the file stands alone when shared.
+    //
+    ch_report_assets = ch_report_assets.mix(
+        MULTIQC.out.report.map { _meta, f -> [['report', 'multiqc', 'MultiQC'], f] }
+    )
+    // The collated software versions drive the Citation page: it cites only the tools this run used.
+    ch_report_assets = ch_report_assets.mix(
+        ch_collated_versions.map { f -> [['versions', 'run', 'versions.yml'], f] }
+    )
+    // Nextflow's own execution trace drives the Overview run-statistics table. It is still being
+    // appended to as the report is built, so it captures every compute-heavy task (all but the report
+    // task itself) - the page labels it as of report generation. checkIfExists is off because the file
+    // is engine-managed, not a process output.
+    ch_report_assets = ch_report_assets.mix(
+        Channel.fromPath("${outdir}/pipeline_info/execution_trace_${params.trace_report_suffix}.txt", checkIfExists: false)
+            .map { f -> [['trace', 'run', 'execution_trace.txt'], f] }
+    )
+
+    // Sorting keeps the manifest and the staged files in one deterministic, matching order.
+    def ch_summary_input = ch_report_assets
+        .toSortedList { a, b -> a[0].join('|') <=> b[0].join('|') }
+        .map { rows ->
+            def manifest = rows.collect { [kind: it[0][0], group: it[0][1], name: it[0][2]] }
+            tuple(
+                groovy.json.JsonOutput.toJson(manifest).bytes.encodeBase64().toString(),
+                rows.collect { it[1] },
+            )
+        }
+
+    def ch_run_meta = variantCounts_filtered_by_library_ch
+        .first()
+        .map { meta, _f ->
+            tuple(
+                [id: 'run', sample: meta.sample],
+                groovy.json.JsonOutput.toJson([
+                    sample: meta.sample,
+                    reading_frame: params.reading_frame,
+                    mutagenesis_type: params.mutagenesis_type,
+                    error_correction: params.error_correction,
+                    version: workflow.manifest.version,
+                    // The report smooths client-side with the same window the R plots use, and draws
+                    // the same required-coverage reference line, so both must travel with the run.
+                    sliding_window_size: params.sliding_window_size,
+                    aimed_cov: params.aimed_cov,
+                    fitness: params.fitness,
+                    dimsum: params.dimsum,
+                    mutscan: params.mutscan,
+                    outdir: params.outdir,
+                    // Lets the Overview link straight to Nextflow's own execution report + timeline,
+                    // which sit next to this file as execution_{report,timeline}_<suffix>.html.
+                    trace_report_suffix: params.trace_report_suffix,
+                    false_doubles_method: params.false_doubles_method,
+                ]).bytes.encodeBase64().toString(),
+            )
+        }
+
+    VISUALIZATION_SUMMARY_REPORT(
+        ch_run_meta,
+        ch_summary_input.map { manifest, _files -> manifest },
+        ch_summary_input.map { _manifest, files -> files },
+        file("${projectDir}/assets/summary_report/summary_report_template.html", checkIfExists: true),
+        file("${projectDir}/assets/variant_effect_inspection_tool/3Dmol-min.js", checkIfExists: true),
+    )
+    ch_versions = ch_versions.mix(VISUALIZATION_SUMMARY_REPORT.out.versions)
+
+    emit:
+    multiqc_report = MULTIQC.out.report.map { _meta, report -> [report] }.toList() // channel: /path/to/multiqc_report.html
     versions       = ch_versions                 // channel: [ path(versions.yml) ]
+    bwa_index      = BWA_INDEX.out.index
+    aligned_bam    = BWA_MEM.out.bam
+    filtered_bam   = BAMFILTER_DMS.out.bam
+    premerged_bam  = PREMERGE.out.bam
 }
 
 /*