MPUSP/snakemake-bacterial-rnaseq-processing

A Snakemake workflow for the processing of short read rnaseq data in bacteria.

Overview

Latest release: v2.0.0, Last update: 2026-04-29

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=MPUSP/snakemake-bacterial-rnaseq-processing

Quality control: linting: passed formatting: passed

Topics: bioinformatics bioinformatics-pipeline computational-biology conda rnaseq-pipeline snakemake workflow rnaseq biosciences snakemake-workflow

Wrappers: bio/deeptools/bamcoverage bio/fastp bio/fastqc bio/multiqc bio/samtools/flagstat bio/samtools/index bio/samtools/sort bio/star/align bio/star/index

Workflow Rule Graph

This visualization of the workflow’s rule graph was automatically generated using Snakevision

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/MPUSP/snakemake-bacterial-rnaseq-processing . --tag v2.0.0

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow using a combination of conda and apptainer/singularity for software deployment, use

snakemake --cores all --sdm conda apptainer

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Workflow overview

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.

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:

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
Check quality of input sequencing data (FastQC)
Cut adapters and filter by length and/or sequencing quality score (fastp)
Identify unique molecular identifier (UMI, UMI-tools)
Map reads to the reference genome (STAR aligner)
Sort and index aligned RNA-Seq data (Samtools)
Deduplicate reads by unique molecular identifier (UMI, UMI-tools)
Generate cpm normalized coverage files (deepTools)
Quantify biotype features (featureCounts)
Generate summary report for all processing steps (MultiQC)

Running the workflow

Input

Reference genome

An NCBI Refseq ID, e.g. GCF_000006785.2. Find your genome assembly and corresponding ID on NCBI genomes. Alternatively use a custom pair of *.fasta file and *.gff file that describe the genome of choice.

Important requirements when using custom *.fasta and *.gff files:

*.gff genome annotation must have the same chromosome/region name as the *.fasta file (example: NC_002737.2)
*.gff genome annotation must have gene and CDS type annotation that is automatically parsed to extract transcripts
all chromosomes/regions in the *.gff genome annotation must be present in the *.fasta sequence
but not all sequences in the *.fasta file need to have annotated genes in the *.gff file

Read data

RNA sequencing data in *.fastq.gz format. The currently supported input data are second generation reads. Input data files are supplied via a mandatory table, whose location is indicated in the config.yml file (default: samples.tsv). The sample sheet has the following layout:

sample	condition	replicate	read1	read2	readumi
RNA-1	RNA	1	RNA-1_R1.fastq.gz	RNA-1_R2.fastq.gz	-
RNA-2	RNA	2	RNA-2_R2.fastq.gz	RNA-2_R2.fastq.gz	-

Some configuration parameters of the pipeline may be specific for your data and library preparation protocol. The options should be adjusted in the config.yml file.

Configuration files for different sequencing protocols can be found in resources/protocols/. Currently, you may find protocols for i.e. rnaseq_nextflex, rnaseq_neb_umi and a custom protocol rnaseq_mpusp_custom.

To run the workflow with the respective test data for the different protocols, use the following commands:

snakemake --sdm conda --cores 12 --directory .test --configfile resources/protocols/rnaseq_mpusp_custom.yml
snakemake --sdm conda --cores 12 --directory .test --configfile resources/protocols/rnaseq_neb_umi.yml
snakemake --sdm conda --cores 12 --directory .test --configfile resources/protocols/rnaseq_nextflex.yml

Output

Output File/Folder	Description
`results/genome/`	Downloaded or user-supplied reference genome and annotation files.
`results/fastp/`	Adapter-trimmed and quality-filtered FASTQ files.
`results/mapped/`	Aligned reads in BAM format, coverage in BigWig format
`results/deduplicated/`	Aligned and UMI-deduplicated reads in BAM format, coverage in BigWig format.
`results/qc/`	Quality control reports for raw and processed reads (FastQC HTML files).
`results/quantify_biotypes/`	Gene/feature count tables (tab-delimited text files).
`results/multiqc/`	MultiQC report aggregating QC metrics from all steps.

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter	Type	Description	Required	Default
samplesheet	string	Path to the sample sheet TSV file.	yes	config/samplesheet/samples.tsv
libtype	string	Library strandedness used during quantification.	yes	antisense
get_genome			yes
. database	[‘string’, ‘null’]	Genome source database identifier.		ncbi
. assembly	[‘string’, ‘null’]	Assembly accession or identifier to download.		GCF_043231225.1
. fasta	[‘string’, ‘null’]	Optional path to a local FASTA file.
. gff	[‘string’, ‘null’]	Optional path to a local GFF annotation file.
. gff_source_type	array	Source/feature-type pairs used to select annotation records.		[{‘RefSeq’: ‘gene’}, {‘RefSeq’: ‘pseudogene’}, {‘RefSeq’: ‘CDS’}, {‘Protein Homology’: ‘CDS’}]
extract_features			yes
. biotypes	array	Feature biotypes to keep for downstream summarization.		[‘protein_coding’, ‘pseudogene’, ‘ncRNA’, ‘rRNA’, ‘tRNA’]
umi_extraction			yes
. method	string	UMI extraction mode, one of “regex”, “string”, or “none”.		none
. pattern	string	UMI pattern used by the selected extraction method.
umi_dedup	string	Additional command-line options for UMI deduplication.	yes	–edit-distance-threshold=0
fastp			yes
. extra	string	Extra arguments passed to fastp.		see config.yml
star			yes
. index	[‘string’, ‘null’]	STAR indexing options.		–genomeSAindexNbases 9
. extra	array	Extra arguments passed to STAR during mapping.		[’–outFilterMultimapNmax 10’, ‘–outSAMmultNmax 1’, ‘–outMultimapperOrder Random’, ‘–alignIntronMax 1’]
samtools			yes
. sort	string	Extra options passed to samtools sort.
. index	string	Extra options passed to samtools index.
feature_counts			yes
. defaults	array	Default options passed to featureCounts.		[‘-F GTF’, ‘-t gene’, ‘-g locus_tag’, ‘-M’, ‘–fracOverlap 0.2’, ‘–largestOverlap’]
deeptools			yes
. genome_size	number	Effective genome size used for coverage normalization.		2000000
. extra	string	Extra options passed to bamCoverage.		–binSize 1 –normalizeUsing CPM –exactScaling –extendReads
fastqc			yes
. extra	string	Extra arguments passed to FastQC.		–quiet –nogroup
multiqc			yes
. config	string	Path to the MultiQC configuration file.		config/multiqc_config.yml
. extra	string	Extra arguments passed to MultiQC.		–dirs