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 DESeq2
for 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.
- Matteo Massidda, University of Sassari
- Vincenzo Rallo, Institute for Genetic and Biomedical Research (IRGB) - National Research Council (CNR)
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).
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