shrnaseq

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

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.

Deployment

Terraform scripts are provided to deploy the workflow to ECS with files stored in S3. See the deployment documentation for more details.

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

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)

jma1991/shrnaseq