nf-core pipeline ·
nf-core/bacmodel
A bioinformatics pipeline for functional annotation and metabolic reconstruction of bacterial genomes.
What nf-core/bacmodel is
nf-core/bacmodel is a systems-based workflow designed for the functional characterisation and metabolic modelling of bacterial genomes. Developed originally by Olga Brovkina at the Institute of Clinical Molecular Biology (IKMB), Kiel University, it automates the progression from assembled genome sequences to genome-scale metabolic reconstructions.
Bacterial genomic analysis often requires coordinating disparate analytical steps, ranging from primary feature annotation to macromolecular pathway discovery and constraint-based metabolic modelling. nf-core/bacmodel provides an integrated computational environment to execute these tasks reproducibly.
The pipeline processes bacterial genome assemblies to predict gene models, infer phenotypic capabilities, detect cellular macromolecular assemblies, reconstruct metabolic networks, and benchmark the resulting models for mathematical and biological consistency.
What the pipeline does
The workflow accepts bacterial genome assemblies in FASTA format via a comma- or tab-separated samplesheet. It begins by performing primary genome annotation using either Prokka by default or Bakta when selected. Following annotation, users can optionally activate intermediate steps for identifying macromolecular systems using MacSyFinder and predicting microbial phenotypes with Traitar.
For metabolic network generation, the annotated genomes can be channelled into reconstruction tools, specifically CarveMe or gapseq. When metabolic reconstructions are produced, the pipeline can additionally run MEMOTE to systematically evaluate the quality and standards-compliance of the resulting metabolic models.
Key facts
- Input formats
- FASTA (.fasta, .fasta.gz, or URL links via CSV/TSV samplesheet)
- Primary annotation engines
- Prokka (default) and Bakta
- Macromolecular systems detection
- MacSyFinder (optional)
- Phenotype prediction
- Traitar (optional)
- Metabolic model reconstruction
- CarveMe and gapseq (optional)
- Model evaluation
- MEMOTE (optional)
What you can use it for
- Bacterial genome annotation
- Annotating bacterial contigs or assemblies using automated annotation tools such as Prokka or Bakta.
- Genome-scale metabolic reconstruction
- Reconstructing automated bacterial metabolic networks from assembled genomic sequences using CarveMe or gapseq.
- Metabolic model quality assessment
- Benchmarking and testing generated metabolic models using MEMOTE to check reporting standards and metabolic consistency.
- Macromolecular system identification
- Scanning bacterial genomes for dedicated secretion systems and other macromolecular complexes using MacSyFinder.
- Bacterial trait and phenotype profiling
- Predicting microbiological phenotypes and functional capabilities directly from genome sequences via Traitar.
Run it here
The research agent runs nf-core/bacmodel 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
- Encompasses both upstream genome annotation and downstream genome-scale metabolic network reconstruction in a single pipeline.
- Permits tool flexibility by supporting either Prokka or Bakta for sequence annotation, and CarveMe or gapseq for metabolic modelling.
- Includes optional downstream quality control via MEMOTE to validate reconstructed metabolic models.
- Modular architecture enables users to selectively skip or enable optional analyses such as Traitar and MacSyFinder.
Watch out for
- Requires pre-assembled bacterial genome sequences and does not perform read quality filtering or de novo genome assembly.
- Designed specifically for bacterial genomes and is not configured for archaeal or eukaryotic organisms.
- Optional modules including MacSyFinder, Traitar, CarveMe, gapseq, and MEMOTE are not executed unless explicitly activated.
Questions
Can I run nf-core/bacmodel without my own cluster?
The research agent runs nf-core/bacmodel 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/bacmodel under?
MIT, like every nf-core pipeline.
What input data does nf-core/bacmodel require?
The pipeline requires a CSV or TSV samplesheet containing sample identifiers and file paths or URLs to bacterial genome assemblies in FASTA format, which can be uncompressed or gzipped.
Which annotation tool is used by default?
Prokka is used by default for genome annotation. Users can switch to Bakta by setting the annotation tool parameter.
Are metabolic modelling steps enabled by default?
No. Advanced functional modules including CarveMe, gapseq, MacSyFinder, Traitar, and MEMOTE are optional and must be explicitly enabled using pipeline flags.
How are reconstructed metabolic models evaluated?
The pipeline can evaluate metabolic models using MEMOTE, which tests model quality, completeness, and consistency.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/bacmodel's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.