zifornd/shrnaseq

shrnaseq

Snakemake workflow of shRNA-seq and CRISPR-Cas9 genetic screen analysis using edgeR.

Table of Contents

• Overview
• Installation
• Usage
• Shiny application
• Documentation
• Contributing
• Authors
• Acknowledgements
• License

Overview

This workflow is used to analysis shRNA-seq and CRISPR/cas9 genetic screens. The workflow is built in Snakemake, a workflow managament system. It primarily uses edgeR, a Biocondutor R package which allows for differential expression analysis of RNA-seq expression profiles. The workflow performs data processing, quality control, differential expression analysis, gene set testing and gene enrichment, with batch correction implemented.

Installation

Install snakemake using the mamba package manager:

$ mamba create -c conda-forge -c bioconda -n snakemake snakemake

$ mamba activate snakemake

Pull the workflow to your project directory:

$ git pull https://github.com/zifornd/shrnaseq

Usage

Configure the workflow by editing the config.yaml file:

$ nano config/config.yaml

Run the workflow:

$ snakemake --use-conda --cores all

For further details on Snakemake, see the Snakemake documentation.

Alternatively, the Dockerfile can be used to run the workflow in a container.

Shiny application

The output of this workflow can be visualised in Shiny from RStudio, see the shRNAseq shiny documentation.

Documentation

See the Documentation file for configuration and output information.

Contributing

See CONTRIBUTING.md for ways to get started.

Please adhere to this project's code of conduct.

Authors

• Claire Prince
• James Ashmore

Acknowledgements

This workflow is based on the following research article:

Dai Z, Sheridan JM, Gearing LJ et al. edgeR: a versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens [version 2; peer review: 3 approved]. F1000Research 2014, 3:95 (https://doi.org/10.12688/f1000research.3928.2)

using the Snakemake management system:

Mölder F, Jablonski KP, Letcher B et al. Sustainable data analysis with Snakemake [version 2; peer review: 2 approved]. F1000Research 2021, 10:33 (https://doi.org/10.12688/f1000research.29032.2)

License

This workflow is licensed under the MIT license.
Copyright © 2022, Zifo RnD Solutions

shrnaseq's Issues

Dockerise and sync with S3

CONTEXT
Snakemake currently needs to be installed with mamba manually and is then run on local data files.

SOLUTION
Orchestrate docker containers to pull data from S3, process using Snakemake and then upload back to S3

aws-genomics-cli

AWS Genomics CLI allows a snakemake engine to be used and deployed.

Investigate a solution to utilise this.

aws-deployment

AWS deployment should be configured using Terraform. Adding data to S3 should trigger ECS task that runs workflow in a deployed container and then saves the data back to S3.

Remove Python dependency

Python scripts had been added previously to sync data with S3.

Alternative approaches have since been found including the snakemake --default-remote flag and aws genomics cli.

Python dependency and scripts should therefore be removed.