MPUSP/snakemake-simple-mapping

A Snakemake workflow for the mapping of reads to reference genomes, minimalistic and simple.

Overview

Latest release: v1.6.0, Last update: 2026-03-22

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

Quality control: linting: passed formatting: passed

Topics: bowtie2 bwa-mem2 genomics mapping next-generation-sequencing snakemake snakemake-workflow star-alignment variant-calling

Wrappers: bio/bcftools/call bio/bcftools/filter bio/bcftools/mpileup bio/bcftools/stats bio/bcftools/view bio/bowtie2/align bio/bowtie2/build bio/bwa-mem2/index bio/bwa-mem2/mem bio/deeptools/bamcoverage bio/fastp bio/fastqc bio/freebayes bio/gffread bio/minimap2/aligner bio/minimap2/index bio/multiqc bio/rseqc/bam_stat bio/rseqc/infer_experiment bio/samtools/index bio/samtools/sort bio/snpeff/annotate bio/star/align bio/star/index bio/vep/annotate bio/vep/plugins

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-simple-mapping . --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 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 is a best-practice workflow for mapping of reads to reference genomes, minimalistic and simple.

It will attempt to map reads to the reference using one of the included mappers, report read and experiment statistics, create coverage profiles, quantify variants (such as SNPs) using two different tools, and predict the effect of these variants. All of this is performed with minimal input and without lookups to external databases (e.g. for variant effects), which makes the workflow ideal for bacteria and other low-complexity non-model organisms.

The workflow is built using snakemake and consists of the following steps:

Download genome reference from NCBI (ncbi tools), or use manual input (fasta, gff format)
Check quality of input read data (FastQC)
Trim adapters and apply quality filtering (fastp)
Map reads to reference genome using:
1. (Bowtie2)[http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml] or
2. (BWA-MEM2)[https://github.com/bwa-mem2/bwa-mem2] or
3. (STAR)[https://github.com/alexdobin/STAR]
4. (minimap2)[https://github.com/lh3/minimap2]
Determine experiment type, get mapping stats (rseqc)
Generate bigwig or bedgaph coverage profiles (deeptools)
Quantify variations and SNPs (bcftools, freebayes)
Predict effect of variants such as premature stop codons (VEP or SnpEff)
Create consensus of variants and create a visual report (R markdown)
Collect statistics from tool output (MultiQC)

Running the workflow

Input data

The workflow requires sequencing data in *.fastq.gz format, and a reference genome to map to. The sample sheet listing read input files needs to have the following layout:

sample	description	read1	read2
sample1	strain XY	sample1_R1.fastq.gz	sample1_R2.fastq.gz
…	…	…	…

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 in tsv format	yes
get_genome			yes
. database	string	database to use for genome retrieval, ‘ncbi’ or ‘manual’	yes	ncbi
. assembly	string	RefSeq assembly accession to use for genome retrieval	yes	GCF_000307535.1
. fasta	[‘string’, ‘null’]	path to a custom FASTA file (optional)
. gff	[‘string’, ‘null’]	path to a custom GFF file (optional)
. gff_source_type	array	mapping of GFF source types to feature types	yes
processing			yes
. fastp
. . extra	string	additional arguments to pass to fastp
mapping			yes
. tool	string	mapping tool to use, one of ‘bowtie2’, ‘bwa_mem2’, ‘star’, or ‘minimap2’	yes	bwa_mem2
. bowtie2
. . index	string	additional arguments to bowtie build
. . extra	string	additional arguments to bowtie align
. bwa_mem2
. . extra	string	additional arguments to bwa-mem2
. . sort	string	sorting tool to use, e.g. ‘samtools’		samtools
. . sort_order	string	sorting order to use		coordinate
. . sort_extra	string	additional arguments to pass to the sorting tool
. star
. . index	string	additional arguments to STAR index
. . extra	string	additional arguments to STAR align
. minimap2
. . index	string	additional arguments to minimap2 index
. . extra	string	additional arguments to minimap2 align		-ax map-ont
. . sorting	string	sorting order to use		coordinate
. . sort_extra	string	additional arguments to pass to the sorting tool
. samtools_sort
. . extra	string	additional arguments to pass to Samtools sort		-m 4G
. samtools_index
. . extra	string	additional arguments to pass to Samtools index
mapping_stats			yes
. gffread			yes
. . extra	string	additional arguments to pass to GFFread
. rseqc_infer_experiment			yes
. . extra	string	additional arguments to pass to RSeQC infer_experiment.py
. rseqc_bam_stat			yes
. . extra	string	additional arguments to pass to RSeQC bam_stat.py
. deeptools_coverage			yes
. . genome_size	integer	genome size in base pairs		1000
. . extra	string	additional arguments to pass to DeepTools bamCoverage
variant_calling			yes
. tool	[‘string’, ‘array’]	the variant caller to use, one of ‘freebayes’, ‘bcftools’, or both
. bcftools_pileup			yes
. . uncompressed	boolean	whether to output uncompressed BCF files		false
. . extra	string	additional arguments to pass to BCFtools pileup
. bcftools_call			yes
. . uncompressed	boolean	whether to output uncompressed VCF files		false
. . caller	string	use ‘-c’ for consensus or ‘-m’ for multiallelic		-c
. . extra	string	additional arguments to pass to BCFtools call
. bcftools_view			yes
. . extra	string	additional arguments to pass to BCFtools view
. bcftools_filter			yes
. . filter	string	expression used to filter BCF/VCF records		-e ‘ALT=”.”’
. . extra	string	additional arguments to pass to BCFtools filter
. freebayes			yes
. . extra	string	additional arguments to pass to Freebayes call
variant_annotation			yes
. tool	string	annotation tool to use, one of ‘vep’, ‘snpeff’	yes	vep
. vep			yes
. . convert_gff	boolean	whether to convert NCBI GFF to Ensembl-style GFF for VEP compatibility		true
. . plugins	array	list of VEP plugins to use		[]
. . extra	string	additional arguments to pass to VEP
. snpeff			yes
. . extra	string	additional arguments to pass to SnpEff
qc			yes
. multiqc			yes
. . extra	string	additional arguments to pass to MultiQC
. fastqc			yes
. . extra	string	additional arguments to pass to FastQC
report			yes
. minimum_variant_count	integer	minimum number of samples/appearances for the same variant to be included in the final report