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Merge pull request #14 from NIGMS/remove-module
remove-module
2 parents 3c7a080 + 7e844f0 commit 99d64a0

7 files changed

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‎Submodule_00_background.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"id": "8c479507-0e54-44e1-a727-d13dceaa1c7b",
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is an install that you need to run once to allow the quizes to be functional.\n",
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{
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"cell_type": "markdown",
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"id": "45b85d9f-4115-446d-8120-02c988a7769f",
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"source": [
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"## Why do we sequence RNA?\n",
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"RNA-sequencing (RNA-seq) is the most common means by which biological samples are characterized at the molecular level. In brief, it is a means of measuring which genes have RNA copies (transcripts) present in a sample and in what relative abundance. The sample is prepared in such a way that DNA and proteins are degraded away, and then the remaining RNA is prepared such that it can be read (as a series of DNA bases A, C, G, and T) on a modern sequencer. Sequencing machines are generally classified as short read, which produces sequence read lengths of 50 to 150 nucleotides, or long-read, which can generate up to tens of thousands of bases. Short-read sequencers have been available for a longer time, and remain more capable of high throughput quantitative output, and these reads are the focus of our work here.\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "b64e6f8a-84ba-4279-b002-25ae4f0755ae",
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"display_quiz(\"quiz-material/00-cp1.json\", shuffle_questions = True)"
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{
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"cell_type": "markdown",
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"id": "1b46f12f-f439-425e-ba98-28bfe1b5ec77",
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"source": [
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"## Jupyter Notebook Introduction\n",
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"\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "da4d8800-63cb-4dd8-bc2e-b28a52513c76",
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"metadata": {
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"tags": []
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"metadata": {},
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"outputs": [],
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"source": [
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"# Execute 1st:\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "a080a9dd-9ea6-4d6a-b5d5-ccd3382f09ed",
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"display_quiz(\"quiz-material/00-cp2.json\", shuffle_questions = True)"
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]
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}
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],
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"metadata": {
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"environment": {
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"kernel": "python3",
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"name": "common-cpu.m109",
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"type": "gcloud",
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"uri": "gcr.io/deeplearning-platform-release/base-cpu:m109"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Submodule_01_prog_setup.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"id": "be0fb298-b8ff-4f5f-b21b-90d9796f11e1",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"!sudo apt update\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "ac5b204a-f0db-4ceb-bf37-57eca6d77974",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"!curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "722415ad-e060-417c-8d41-f0a63f529421",
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"metadata": {
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"scrolled": true,
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"tags": []
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"metadata": {},
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"outputs": [],
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"source": [
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"!curl https://get.nextflow.io | bash\n",
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"cell_type": "code",
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"execution_count": null,
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"id": "d7409cc1-540d-42ba-b195-dd2979929fb8",
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"metadata": {
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"scrolled": true,
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"tags": []
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"metadata": {},
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"outputs": [],
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"source": [
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"!gsutil -m cp -r gs://nigms-sandbox/nosi-inbremaine-storage/TransPi ./"
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"cell_type": "code",
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"execution_count": null,
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"id": "1233004c-fcad-444e-a5f2-9c38d33b6e95",
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"metadata": {
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"scrolled": true,
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"tags": []
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"metadata": {},
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"outputs": [],
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"source": [
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"!gsutil -m cp -r gs://nigms-sandbox/nosi-inbremaine-storage/resources ./"
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"source": []
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}
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],
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"metadata": {
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"environment": {
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"kernel": "python3",
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"name": "common-cpu.m109",
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"type": "gcloud",
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"uri": "gcr.io/deeplearning-platform-release/base-cpu:m109"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Submodule_02_basic_assembly.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"id": "ee5985e3-93df-4779-afe1-4464e13bf619",
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"metadata": {
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"scrolled": true,
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"tags": []
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"metadata": {},
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"outputs": [],
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"source": [
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"!NXF_VER=22.10.1 ./nextflow run ./TransPi/TransPi.nf \\\n",
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]
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}
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],
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"metadata": {
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"environment": {
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"kernel": "python3",
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"name": "common-cpu.m109",
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"type": "gcloud",
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"uri": "gcr.io/deeplearning-platform-release/base-cpu:m109"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"language_info": {
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Submodule_03_annotation_only.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"id": "3bb702b3-0a36-4a32-aca5-50dc75f9d401",
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"metadata": {
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"scrolled": true,
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"metadata": {},
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"outputs": [],
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"source": [
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"!NXF_VER=22.10.1 ./nextflow run ./TransPi/TransPi.nf \\\n",
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{
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"cell_type": "markdown",
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"id": "8a0f8dfb-366d-4e0f-af4e-d96f6ee97d34",
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"metadata": {
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"tags": []
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"metadata": {},
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"source": [
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"**Step 5:** As with the basic assembly example of the last workbook, the output will be arranged in a directory structure that is automatically created by Nextflow. Let's get a listing."
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]
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]
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}
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],
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"environment": {
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"name": "common-cpu.m109",
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"uri": "gcr.io/deeplearning-platform-release/base-cpu:m109"
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"nbconvert_exporter": "python",
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"version": "3.10.11"
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}
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Submodule_04_google_batch_assembly.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"id": "5d39277a-1128-4b7b-a5a2-db8491bc89ca",
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"metadata": {},
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"!gsutil -m cp -r gs://nigms-sandbox/nosi-inbremaine-storage/resources $gbPath/resources"
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"cell_type": "code",
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"execution_count": null,
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"id": "fc8b9f7a-d069-444b-894a-5b974c2bb6d0",
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"!NXF_VER=22.10.1 ./nextflow run ./TransPi/TransPi.nf \\\n",
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"metadata": {},
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"!gsutil -m cp -r $gbPath/basicRun/output/pipeline_info ./GCBbasicRun/output"
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"id": "3d032206-02bc-473e-8b2e-c313a1f53e1e",
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"!NXF_VER=22.10.1 ./nextflow run ./TransPi/TransPi.nf \\\n",
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"metadata": {},
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"nbformat": 4,
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}

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