sequana-mapper 1.5.0

Installation

If you already have all requirements, you can install the packages using pip:

pip install sequana_mapper --upgrade

You will need third-party software such as bwa and samtools. Please see below for details.

Usage

Scan FastQ files in a directory and set up the pipeline (replace DATAPATH and genome.fa with your inputs):

sequana_mapper --input-directory DATAPATH --reference-file genome.fa --aligner-choice bwa
sequana_mapper --input-directory DATAPATH --reference-file genome.fa --aligner-choice bwa --do-coverage
sequana_mapper --input-directory DATAPATH --reference-file genome.fa --aligner-choice bwa --create-bigwig

For long-read data, use the dedicated presets:

sequana_mapper --input-directory DATAPATH --reference-file genome.fa --pacbio     # sets minimap2 -x map-pb
sequana_mapper --input-directory DATAPATH --reference-file genome.fa --nanopore   # sets minimap2 -x map-ont

For capture-seq projects (feature counting):

sequana_mapper --input-directory DATAPATH --reference-file genome.fa --capture-annotation-file targets.saf

This creates a mapper/ directory with the pipeline and configuration file. Execute the pipeline locally:

cd mapper
bash mapper.sh

See .sequana/profile/config.yaml to tune Snakemake behaviour (cores, cluster settings, etc.).

Usage with apptainer

With apptainer, initiate the working directory as follows:

sequana_mapper --input-directory DATAPATH --reference-file genome.fa --use-apptainer

Images are downloaded in the working directory but you can store them in a shared location:

sequana_mapper --input-directory DATAPATH --reference-file genome.fa --use-apptainer --apptainer-prefix ~/.sequana/apptainers

and then:

cd mapper
bash mapper.sh

Requirements

This pipeline requires the following executables (install via bioconda/conda):

• bwa — short-read aligner (default)

• minimap2 — long-read aligner (PacBio / Nanopore)

• bowtie2 — alternative short-read aligner

• samtools / sambamba — BAM processing

• bamtools — BAM statistics

• fastqc — raw-read quality control (optional, --do-fastqc)

• deeptools — bigwig generation (bamCoverage)

• bedtools — genome arithmetic

• subread — feature counting (featureCounts, capture-seq only)

• mosdepth — fast coverage depth

• seqkit — FASTQ statistics

• multiqc — aggregated HTML report

• sequana_coverage — coverage analysis (prokaryotes)

Install all dependencies at once:

mamba env create -f environment.yml

Details

This pipeline maps FastQ files (paired or single-end) in parallel onto a reference genome and produces filtered BAM files, a MultiQC HTML report, and optionally coverage tracks and feature counts.

Aligner choice (--aligner-choice):

• bwa (default) — BWA-MEM; index algorithm is auto-selected (is or bwtsw) based on reference size

• bwa_split — experimental; splits large FastQs into 1 M-read chunks for parallel BWA jobs, then merges

• minimap2 — long-read aligner; use --pacbio (sets -x map-pb) or --nanopore (sets -x map-ont)

• bowtie2 — standard short-read aligner

BAM filtering: unmapped reads are removed to minimise file size. Statistics reported by MultiQC (in {sample}/bamtools_stats/) still include both mapped and unmapped read counts.

Optional outputs:

• --do-coverage — runs sequana_coverage for depth-of-coverage analysis (prokaryotes)

• --do-fastqc — runs fastqc on the raw input reads (included in the MultiQC report)

• --create-bigwig — generates bigwig files via bamCoverage (deeptools)

• --capture-annotation-file — enables featureCounts for capture-seq efficiency metrics

Rules and configuration details

Here is the latest documented configuration file to be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.

Changelog

Contribute & Code of Conduct

To contribute to this project, please take a look at the Contributing Guidelines first. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.