Snakemake workflow: RNASeq

A Snakemake-based pipeline for RNASeq data analysis.

Starting from fastq files, the pipeline merges files from different units and perform reads quality trimming.

The pseudoaligner kallisto is used to estimate transcripts abundance, with resulting .h5 files that can be imported into DESeq2for DE Analysis.

STAR 2-pass mapping is used for read alignment.

Quality Control is perfomed with FastQC and RSeQC and included in an interactive MultiQC report.

Authors

• Matteo Massidda, University of Sassari
• Vincenzo Rallo, Institute for Genetic and Biomedical Research (IRGB) - National Research Council (CNR)

Usage

The usage of this workflow is described in the Snakemake Workflow Catalog.

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and its DOI (see above).

INSTRUCTIONS

Create a virtual environment with the command:

mamba create -c bioconda -c conda-forge --name snakemake snakemake=7.18 snakedeploy

and activate it:

conda activate snakemake

You can perform the pipeline deploy defining a directory my_dest_dir for analysis output and a pipeline tag for a specific version:

snakedeploy deploy-workflow https://github.com/GeneBANGS/RNASeq.git 
                    my_desd_dir 
                    --tag v1.1.0

To run the pipeline, go inside the deployed pipeline folder and use the command:

snakemake --use-conda -p --cores all