Snakemake workflow: bacterial-rnaseq-processing

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A Snakemake workflow for the processing of short read RNA-Seq data in bacteria. This workflow can be used in combination with subsequent workflows for follow-up analyses. For example, differential expression analysis can be performed using snakemake-bacterial-rnaseq-deseq.

• Snakemake workflow: bacterial-rnaseq-processing
  • Usage
  • Workflow overview
  • Deployment options
  • Authors
  • References

Usage

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

Detailed information about input data and workflow configuration can also be found in the config/README.md.

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this repository or its DOI.

Workflow overview

This workflow is a best-practice workflow for the processing of short read sequencing data in bacteria. The workflow is built using snakemake and consists of the following steps:

1. Obtain genome database in fasta and gff format (python, NCBI Datasets)
   1. Using automatic download from NCBI with a RefSeq ID
   2. Using user-supplied files
2. Check quality of input sequencing data (FastQC)
3. Cut adapters and filter by length and/or sequencing quality score (fastp)
4. Identify unique molecular identifier (UMI, UMI-tools)
5. Map reads to the reference genome (STAR aligner)
6. Sort and index aligned RNA-Seq data (Samtools)
7. Deduplicate reads by unique molecular identifier (UMI, UMI-tools)
8. Generate cpm normalized coverage files (deepTools)
9. Quantify biotype features (featureCounts)
10. Generate summary report for all processing steps (MultiQC)

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Figure 1: Directed acyclic graph (DAG) of the current workflow steps.

Deployment options

To run the workflow from command line, change the working directory.

cd path/to/snakemake-workflow-name

Adjust options in the default config file config/config.yml. Before running the complete workflow, you can perform a dry run using:

snakemake --dry-run

To run the workflow with test files using conda:

snakemake --cores 2 --sdm conda --directory .test

To run the workflow with apptainer:

snakemake --cores 2 --sdm conda apptainer --directory .test

Authors

• Dr Rina Ahmed-Begrich
  • Affiliation: Max-Planck-Unit for the Science of Pathogens (MPUSP), Berlin, Germany
  • ORCID profile: https://orcid.org/0000-0002-0656-1795
  • github page: https://github.com/rabioinf
• Dr. Michael Jahn
  • Affiliation: Max-Planck-Unit for the Science of Pathogens (MPUSP), Berlin, Germany
  • ORCID profile: https://orcid.org/0000-0002-3913-153X
  • github page: https://github.com/m-jahn

Visit the MPUSP github page at https://github.com/MPUSP for more info on this workflow and other projects.

References

• Essential tools are linked in the top section of this document