nf-core pipeline · mass-spectrometry, proteomics
nf-core/mspepid
Identifies peptides and rescores spectra from proteomics and peptidomics mass spectrometry data.
What nf-core/mspepid is
nf-core/mspepid is a bioinformatics pipeline designed to identify peptides from tandem mass spectrometry (MS2) spectra in proteomics and peptidomics studies. Developed by the Medical Bioinformatics group at Ruhr University Bochum, it acts as a modular, identification-focused tool that matches experimental spectra against protein sequence databases and estimates false discovery rates (FDR).
Identifying peptide-spectrum matches (PSMs) from raw spectrometer files typically requires file format standardisation, sequence search engines, and statistical post-processing. nf-core/mspepid unifies these stages, accepting raw vendor data, converting them to standard formats, searching them with configurable engines, and applying machine learning algorithms to rescore matches. It is engineered to provide high-confidence, FDR-filtered identifications that can serve downstream quantification or statistical workflows such as nf-core/quantms and nf-core/msproteomics.
What the pipeline does
The workflow begins with optional entrapment database generation for FDR benchmarking using FDRBench, followed by decoy sequence creation via OpenMS DecoyDatabase (which can be skipped if decoys are already present). Vendor files are converted into open-standard mzML format using ThermoRawFileParser for Thermo .raw data or tdf2mzml for Bruker .d folders. The converted spectra are then searched against a provided FASTA database using one or more sequence search engines, including Comet and Sage.
Search outputs are harmonised into a common format using psm-utils before post-processing with machine-learning rescoring tools. Rescoring is conducted either through semi-supervised learning in Percolator or via MS2Rescore, which incorporates MS2PIP spectral prediction-based feature generation prior to Percolator re-ranking. The final output yields high-confidence, Percolator-scored PSM files (.pout) segregated by search engine and rescoring method.
Key facts
- Pipeline framework
- Nextflow (nf-core)
- Input formats
- Thermo .raw, Bruker TimsTOF .d, and .mzML (supporting .gz, .zip, .tar.gz compression), alongside FASTA databases
- Output formats
- Percolator-scored PSM files (.pout)
- Search engines
- Comet, Sage
- Rescoring tools
- Percolator, MS2Rescore
What you can use it for
- Proteomics peptide identification
- Search MS2 spectra against target protein FASTA databases using Comet or Sage to generate peptide-spectrum matches from Thermo or Bruker instruments.
- Machine learning-based PSM rescoring
- Re-rank identified PSMs using Percolator or MS2Rescore to boost identification rates and improve false discovery rate estimation.
- FDR benchmarking
- Construct entrapment databases using FDRBench to evaluate and benchmark false discovery rate control in mass spectrometry experiments.
Run it here
The research agent runs nf-core/mspepid 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
- Supports direct ingestion of proprietary vendor formats from Thermo (.raw) and Bruker TimsTOF (.d) alongside open .mzML files.
- Enables comparative multi-engine database searching with Comet and Sage.
- Integrates MS2Rescore to leverage predicted spectral features from MS2PIP alongside standard Percolator rescoring.
- Converts diverse search engine outputs into a standard format via psm-utils for uniform downstream handling.
Watch out for
- Focuses strictly on peptide identification and PSM rescoring, omitting downstream protein quantification and statistical differential abundance testing.
- Spectrum conversion is limited to Thermo and Bruker TimsTOF vendor inputs among proprietary raw formats.
- Database searching is currently limited to Comet and Sage.
Questions
Can I run nf-core/mspepid without my own cluster?
The research agent runs nf-core/mspepid 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/mspepid under?
MIT, like every nf-core pipeline.
Which raw mass spectrometry formats are directly supported?
The pipeline accepts Thermo .raw files, Bruker TimsTOF .d folders, and open-standard .mzML files, including compressed formats (.gz, .zip, or .tar.gz).
Can I provide a FASTA file that already contains decoy sequences?
Yes. If decoy sequences are already present in your database, automatic decoy generation by OpenMS DecoyDatabase can be bypassed using the --skip_decoy_generation flag.
What search engines can be configured in the pipeline?
The pipeline currently supports Comet and Sage for peptide-spectrum match identification.
What is the difference between the available rescoring options?
Percolator uses semi-supervised machine learning directly on search features for re-ranking and FDR estimation, whereas MS2Rescore generates additional features using MS2PIP spectral predictions before running Percolator.
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/mspepid's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.