nf-core pipeline ·
nf-core/magmap
Maps metagenomic and metatranscriptomic reads to large collections of genomes for gene quantification.
What nf-core/magmap is
nf-core/magmap is a bioinformatics analysis pipeline designed to map sequencing reads against collections of genomes. The pipeline was originally written by Danilo Di Leo, Emelie Nilsson, and Daniel Lundin, and is implemented in Nextflow DSL2 using containerised software dependencies.
The workflow is primarily intended for metagenomic and metatranscriptomic datasets focusing on prokaryotic genomes, though it can also be used for other sequencing data requiring alignment to contigs. Its primary output is gene-level quantification tables that can be analysed directly in R, Python, or downstream analysis pipelines such as nf-core/differentialabundance.
What the pipeline does
Sequencing data undergo quality control with FastQC, adapter removal and quality trimming via Trim Galore!, and filtering using BBduk. The pipeline then uses sourmash k-mer signatures to identify and select matching reference genomes from candidate sets. Genomes lacking GFF annotation files are annotated using either Prokka or Bakta, and gffread translates coding sequences to produce protein FASTA files.
Cleaned reads are mapped against an indexed reference assembly using BBmap. FeatureCounts subsequently quantifies reads across individual genomic features to produce raw count tables, while Collect_stats.R and MultiQC generate summary statistics and quality assessment reports across all tools.
Key facts
- Pipeline engine
- Nextflow DSL2
- Latest release
- 1.2.0 (2026-08-11)
- Inputs
- FASTQ files (samplesheet.csv) and genome FASTA/GFF files (genomeinfo.csv)
- Outputs
- TSV gene count tables, protein FASTA files, and MultiQC reports
- Container support
- Docker, Singularity
What you can use it for
- Metatranscriptomic expression quantification
- Quantifies gene expression across complex microbial communities by mapping metatranscriptome reads to prokaryotic genome collections.
- Metagenomic feature abundance estimation
- Aligns metagenomic reads to contigs or genomes to calculate raw gene abundance tables for downstream statistical testing.
- De novo reference genome annotation
- Annotates uncharacterised prokaryotic assemblies with Prokka or Bakta and translates coding sequences into protein FASTA files prior to read mapping.
Run it here
The research agent runs nf-core/magmap with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Filters and selects reference genomes dynamically using k-mer signatures from sourmash.
- Supports automatic genome annotation with Prokka or Bakta when reference GFF files are unavailable.
- Produces gene-level count tables structured for direct integration into differential abundance workflows.
- Executes individual tasks in isolated Docker or Singularity containers for reproducibility.
Watch out for
- Tailored specifically for prokaryotic genomes and contigs rather than single eukaryotic reference genomes.
- Requires a dedicated genome information CSV file detailing FASTA and optional GFF locations for custom reference sets.
Questions
Can I run nf-core/magmap without my own cluster?
The research agent runs nf-core/magmap with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/magmap under?
MIT, like every nf-core pipeline.
What type of sequencing samples is this pipeline designed for?
It is primarily designed for metatranscriptomic and metagenomic samples that require mapping against collections of prokaryotic genomes or contigs.
How are reference genomes selected?
The pipeline selects reference genomes based on k-mer signatures matching the sequenced reads using sourmash.
Can genomes without annotations be used?
Yes, genomes without a GFF file can be automatically annotated using either Prokka or Bakta via the pipeline annotator setting.
How does nf-core/magmap differ from nf-core/rnaseq?
The nf-core/rnaseq pipeline is intended for single organisms with established reference annotations, predominantly eukaryotic model species, whereas magmap is built for multi-genome prokaryotic collections.
How to cite it
10.5281/zenodo.17752714
Written from nf-core/magmap's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.