MPUSP/snakemake-bacterial-riboseq

Bacterial-Riboseq: A Snakemake workflow for the analysis of riboseq data in bacteria.

Overview

Latest release: v1.6.0, Last update: 2026-04-07

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

Quality control: linting: passed formatting: passed

Topics: bioinformatics-pipeline conda riboseq ribosome-profiling singularity snakemake workflow

Wrappers: bio/cutadapt/se bio/fastqc

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-riboseq . --tag v1.6.0

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 with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

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

snakemake --cores all --sdm conda apptainer

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.

Running the workflow

Input data

Reference genome

An NCBI Refseq ID, e.g. GCF_000006945.2. 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_003197.2)
*.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

Ribosome footprint sequencing data in *.fastq.gz format. The currently supported input data are single-end, strand-specific 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	fq1
RPF-RTP1	RPF-RTP	1	data/RPF-RTP1_R1_001.fastq.gz
RPF-RTP2	RPF-RTP	2	data/RPF-RTP2_R1_001.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. For example:

Minimum and maximum read length after adapter removal (see option cutadapt: default). Here, the test data has a minimum read length of 15 + 7 = 22 (2 nt on 5’end + 5 nt on 3’end), and a maximum of 45 + 7 = 52.
Unique molecular identifiers (UMIs). For example, the protocol by McGlincy & Ingolia, 2017 creates a UMI that is located on both the 5’-end (2 nt) and the 3’-end (5 nt). These UMIs are extracted with umi_tools (see options umi_extraction: method and pattern).

Example configuration files for different sequencing protocols can be found in resources/protocols/.

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 samplesheet, mandatory	yes	config/samples.tsv
get_genome		reference genome source and files	yes
. database	[‘string’, ‘null’]	one of `manual`, `ncbi`		ncbi
. assembly	[‘string’, ‘null’]	RefSeq ID		GCF_000006785.2
. fasta	[‘string’, ‘null’]	optional path to fasta file
. gff	[‘string’, ‘null’]	optional path to gff file
. gff_source_type	array	list of name/value pairs for GFF source
cutadapt		adapter trimming parameters	yes
. adapters	string	sequence of 5’ (-g) / 3’ (-a) adapter		-a ATCGTAGATCGGAAGAGCACACGTCTGAA
. default	array	additional options passed to cutadapt		[‘-q 10 ‘, ‘-m 22 ‘, ‘-M 52’, ‘–overlap=3’]
umi_extraction		UMI extraction settings	yes
. method	string	one of `string` or `regex`, see manual		regex
. pattern	string	string or regular expression		^(?P<umi_0>.{5}).*(?P<umi_1>.{2})$
umi_dedup	array	default options for deduplication	yes
star		STAR alignment settings	yes
. index	[‘string’, ‘null’]	location of genome index; if Null, is made
. genomeSAindexNbases	number	length of pre-indexing string, see STAR man		9
. multi	number	max number of loci read is allowed to map		10
. sam_multi	number	max number of alignments reported for read		1
. intron_max	number	max length of intron; 0 = automatic choice		1
. default	array	default options for STAR aligner
extract_features		feature extraction and filtering	yes
. biotypes	array	biotypes to exclude from mapping		[‘rRNA’, ‘tRNA’]
. CDS	array	CDS type to include for mapping		[‘protein_coding’]
bedtools_intersect		bedtools intersect options	yes
. defaults	array	remove hits, sense strand, min overlap 20%		[‘-v ‘, ‘-s ‘, ‘-f 0.2’]
annotate_orfs		ORF annotation settings	yes
. window_size	number	size of 5’-UTR added to CDS		30
shift_reads		read shifting parameters	yes
. window_size	number	start codon window to determine shift		30
. read_length	array	size range of reads to use for shifting		[27, 45]
. end_alignment	string	end used for alignment of RiboSeq reads		3prime
. shift_table	[‘string’, ‘null’]	optional table with offsets per read length
. export_bigwig	boolean	export shifted reads as bam file		true
. export_ofst	boolean	export shifted reads as ofst file		false
. skip_shifting	boolean	skip read shifting entirely		false
. skip_length_filter	boolean	skip filtering reads by length		false
multiqc		MultiQC reporting parameters	yes
. fastqc_stage	string
. config	string	path to multiqc config		config/multiqc_config.yml
report		report rendering parameters	yes
. export_figures	boolean	export figures as .svg and .png		true
. export_dir	string	sub-directory for figure export		figures/
. figure_width	number	standard figure width in px		875
. figure_height	number	standard figure height in px		500
. figure_resolution	number	standard figure resolution in dpi		125