nf-core pipeline · archived · dia, lfq, mass-spec
nf-core/quantms
Mass spectrometry workflow for label-free and isobaric quantitative proteomics across DDA and DIA experiments.
What nf-core/quantms is
nf-core/quantms is a Nextflow bioinformatics pipeline designed for quantitative mass spectrometry proteomics. It processes data from three widespread analytical modes: data-dependent acquisition label-free quantification (DDA-LFQ), data-dependent isobaric labelling (DDA-ISO, supporting formats such as TMT and iTRAQ), and data-independent acquisition label-free quantification (DIA-LFQ).
Originally developed by Chengxin Dai, Julianus Pfeuffer, and Yasset Perez-Riverol, the pipeline standardises multi-step computational proteomics across diverse compute platforms. It integrates experimental layout metadata using the Sample-to-Data Relationship Format (SDRF) alongside FASTA reference sequences to process complex, multi-sample experimental designs.
The nf-core repository for this workflow is archived and no longer maintained within nf-core, with ongoing development moving to the bigbio repository. Version 1.2.0 remains available as a standardised release executing modular containerised tasks.
What the pipeline does
The workflow converts raw mass spectrometry vendor outputs, such as Thermo RAW files via ThermoRawFileParser or Bruker .d formats via tdf2mzml, into open mzML files. For DDA workflows, peptide sequences are identified using Comet, MS-GF+, or both, followed by machine learning rescoring with Percolator, false discovery rate filtering with OpenMS tools, and modification site localisation via Luciphor. Label-free DDA runs are quantified with proteomicsLFQ, whereas isobaric labelled runs extract reporter signals using IsobaricAnalyzer and perform protein inference using ProteinInference or Bayesian inference via Epifany before quantification with ProteinQuantifier.
For DIA-LFQ datasets, raw or converted files are processed directly using DIA-NN for spectrum identification and feature quantification. Across all workflows, quantitative matrices are evaluated for normalisation, missing value imputation, and group differential testing using MSstats, with run metrics compiled into quality control reports by pmultiqc.
Key facts
- Pipeline engine
- Nextflow (DSL2)
- Latest release
- 1.2.0 (2023-11-02)
- Status
- Archived (development continued at bigbio/quantms)
- Original developers
- Chengxin Dai, Julianus Pfeuffer, Yasset Perez-Riverol
- Supported quantification modes
- DDA-LFQ, DDA-Isobaric (TMT, iTRAQ), DIA-LFQ
- Primary inputs
- Mass spectrometry raw files (.raw, .d, .mzML), SDRF metadata (.tsv), FASTA sequence database
- Search and quantification engines
- Comet, MS-GF+, DIA-NN, OpenMS (proteomicsLFQ, IsobaricAnalyzer, Epifany)
- Statistical and reporting tools
- MSstats, pmultiqc
What you can use it for
- Data-dependent label-free proteomics
- Identify and quantify proteins without chemical labels using Comet or MS-GF+ peptide searches, Percolator rescoring, and OpenMS proteomicsLFQ feature extraction.
- Isobaric multiplexed quantification
- Analyse TMT or iTRAQ experiments by extracting reporter ion intensities with IsobaricAnalyzer and performing protein inference via Epifany or ProteinInference.
- Data-independent acquisition profiling
- Process DIA mass spectrometry runs from Thermo RAW or converted Bruker .d files directly through DIA-NN to generate quantitative peptide matrices.
- Standardised re-analysis of public datasets
- Re-analyse public mass spectrometry projects directly using community-curated SDRF metadata files and reference FASTA proteomes.
Strengths and limits
Good at
- Unifies three major mass spectrometry quantitative workflows within a single configurable framework.
- Uses standard SDRF experimental metadata files to automate sample grouping, run configurations, and multi-condition comparisons.
- Provides end-to-end downstream statistical analysis using MSstats alongside comprehensive pmultiqc quality control reports.
- Employs Nextflow DSL2 with per-tool containerisation via Docker and Singularity for reproducibility.
Watch out for
- The repository is officially archived on nf-core and no longer receives updates in this location.
- Support for Bruker .d raw inputs is restricted to an optional conversion step in the DIA branch, primarily prioritising Thermo RAW and mzML files.
- Requires structured SDRF tabular metadata conforming strictly to format specifications.
Questions
Can I run nf-core/quantms without my own cluster?
The research agent runs nf-core/quantms 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/quantms under?
MIT, like every nf-core pipeline.
Which mass spectrometry acquisition modes can the pipeline analyse?
It analyses data-dependent acquisition label-free quantification (DDA-LFQ), data-dependent isobaric labelling such as TMT and iTRAQ (DDA-ISO), and data-independent acquisition label-free quantification (DIA-LFQ).
What inputs are mandatory to start a run?
Runs require an SDRF metadata file describing sample relationships, a FASTA protein sequence database, and the corresponding raw or mzML mass spectrometry data files.
Which tools are used for statistical testing of differential abundance?
MSstats is executed downstream across the workflows to perform normalisation, imputation, and differential significance testing.
Is nf-core/quantms still maintained?
The nf-core repository has been archived. Further development and maintenance of the workflow continue under the bigbio organisation repository.
How to cite it
10.5281/zenodo.7754148
Written from nf-core/quantms's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.