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This module teaches you how to perform a short-read RNA-seq Transcriptome Assembly with a Cloud Computing Platform using a Nextflow pipeline. In addition to the overview given in this README, you will find README related to each platform (AWS, Google Cloud) and Jupyter notebooks that teach you different components of RNA-seq in the cloud.
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This module teaches you how to perform a short-read RNA-seq Transcriptome Assembly on Amazon Web Services (AWS) using a Nextflow pipeline.
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## Learning goals:
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1. From a *biological perspective*, demonstration of the **process of transcriptome assembly** from raw RNA-seq data.
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+ Notebook 5 ([Submodule_05_Bonus_Notebook.ipynb](./Submodule_05_Bonus_Notebook.ipynb)) is a more hands-off notebook to test basic skills taught in this module.
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## **Data**
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The test dataset used in the majority of this module is a downsampled version of a dataset that can be obtained in its complete form from the SRA database (Bioproject [**PRJNA318296**](https://www.ncbi.nlm.nih.gov/bioproject/PRJNA318296), GEO Accession [**GSE80221**](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80221)). The data was originally generated by **Hartig et al., 2016**. We downsampled the data files in order to streamline the performance of the tutorials and stored them in a Google Cloud Storage bucket. The sub-sampled data, in individual sample files as well as a concatenated version of these files are available in our Google Cloud Storage bucket at `gs://nigms-sandbox/nosi-inbremaine-storage/resources/seq2`.
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The test dataset used in the majority of this module is a downsampled version of a dataset that can be obtained in its complete form from the SRA database (Bioproject [**PRJNA318296**](https://www.ncbi.nlm.nih.gov/bioproject/PRJNA318296), GEO Accession [**GSE80221**](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80221)). The data was originally generated by **Hartig et al., 2016**. <mark>We downsampled the data files in order to streamline the performance of the tutorials and stored them in a Google Cloud Storage bucket. The sub-sampled data, in individual sample files as well as a concatenated version of these files are available in our Google Cloud Storage bucket at `gs://nigms-sandbox/nosi-inbremaine-storage/resources/seq2`</mark>.
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Additional datasets for demonstration of the annotation features of TransPi were obtained from the NCBI Transcriptome Shotgun Assembly archive. These files can be found in our Google Cloud Storage bucket at `gs://nigms-sandbox/nosi-inbremaine-storage/resources/trans`.
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Additional datasets for demonstration of the annotation features of TransPi were obtained from the NCBI Transcriptome Shotgun Assembly archive. <mark>These files can be found in our Google Cloud Storage bucket at `gs://nigms-sandbox/nosi-inbremaine-storage/resources/trans`</mark>.
- Originally generated by **Galbraith DA et al., 2015**.
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## **Getting Started**
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This repository contains several Jupyter notebook files which serve as bioinformatics WGBS workflow tutorials. To view these notebooks on AWS, the following steps will guide you through setting up a notebook instance on SageMaker AI, downloading our tutorial files, and running those files.
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### Creating a notebook instance
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**1)** Follow the steps highlighted [here](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/HowToCreateAWSSagemakerNotebooks.md) to create a new notebook instance in Amazon SageMaker. Follow steps and be especially careful to enable idle shutdown as highlighted. For this module, in [step 4](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/HowToCreateAWSSagemakerNotebooks.md) in the "Notebook instance type" tab, select ml.m5.xlarge from the dropdown box. Select conda_python3 kernel in [step 8](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/HowToCreateAWSSagemakerNotebooks.md).
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**2)** You will need to download the tutorial files from GitHub. The easiest way to do this would be to clone the repository from NIGMS into your Amazon SageMaker notebook. To clone this repository, use the Git symbole on left menu and then insert the link `https://github.com/NIGMS/Transcriptome-Assembly-Refinement-and-Applications.git` as it is illustrated in [step 7](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/HowToCreateAWSSagemakerNotebooks.md). Please make sure you only enter the link for the repository that you want to clone. There are other bioinformatics related learning modules available in the [NIGMS Repository](https://github.com/NIGMS). This will download our tutorial files into a folder called `Transcriptome-Assembly-Refinement-and-Applications`.
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### Running Tutorial Files
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All our tutorial workflows are in [Jupyter notebook](https://docs.jupyter.org/en/latest/"Juypter notebook documentation") format. To run these notebooks (.ipynb) you need only to double-click the tutorial files and this will open the Jupyter file in Jupyter notebook. From here you can run each section, or 'cell', of the code, one by one, by pushing the 'Play' button on the above menu.
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Some 'cells' of code take longer for the computer to process than others. You will know a cell is running when a cell has an asterisk next to it **[*]**. When the cell finishes running, that asterisk will be replaced with a number which represents the order that cell was run in.
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### Stopping Your Notebook
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Make sure that after you are done with the module, close the tab that appeared when you clicked **OPEN JUPYTERLAB**, then check the box next to the name of the notebook you created in [step 3](https://github.com/NIGMS/NIGMS-Sandbox/blob/main/docs/HowToCreateAWSSagemakerNotebooks.md). Then click on **STOP** at the top of the Workbench menu. Wait and make sure that the icon next to your notebook is grayed out.
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## **Troubleshooting**
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- If a quiz is not rendering:
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- If you are unable to create a bucket using the `gsutil mb` command, check your `nextflow-service-account` roles. Make sure that you have `Storage Admin` added.
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- If you are trying to execute a terminal command in a Jupyter code cell and it is not working, make sure that you have an `!` before the command.
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- e.g., `mkdir example-1` -> `!mkdir example-1`
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## **Funding**
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MDIBL Computational Biology Core efforts are supported by two Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant numbers P20GM103423 and P20GM104318.
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## **License for Data**
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Text and materials are licensed under a Creative Commons CC-BY-NC-SA license. The license allows you to copy, remix and redistribute any of our publicly available materials, under the condition that you attribute the work (details in the license) and do not make profits from it. More information is available [here](https://tilburgsciencehub.com/about).
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/)
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The TransPi Nextflow workflow was developed and released by Ramon Rivera and can be obtained from its [GitHub repository](https://github.com/PalMuc/TransPi)
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