nf-core pipeline · archived · label-free-quantification, openms, proteomics
nf-core/proteomicslfq
Proteomics label-free quantification pipeline using OpenMS and MSstats for mass spectrometry data analysis.
What nf-core/proteomicslfq is
nf-core/proteomicslfq is a bioinformatics pipeline designed for label-free quantification (LFQ) of mass spectrometry proteomics experiments. Originally developed by Julianus Pfeuffer, Lukas Heumos, Leon Bichmann, Timo Sachsenberg, and Yasset Perez-Riverol, it is implemented using the Nextflow workflow manager to ensure portable execution across compute environments.
The pipeline automates the progression from mass spectrometry spectra to quantified proteins, addressing peptide identification, modification localization, feature extraction, and comparative statistics. It couples tools such as OpenMS and MSstats to perform protein inference, feature summarisation, and group-based statistical analysis across experimental designs.
What the pipeline does
The pipeline begins by converting input data into indexed mzML files and conducting database searches across multiple search engines. Search results undergo re-scoring with tools including Percolator, followed by merging, false discovery rate (FDR) filtering, modification site localisation via Luciphor2 (such as phospho-sites), and protein inference and grouping.
Label-free quantification is performed using either spectral counting or feature-based alignment and peak integration. Downstream operations include quality control evaluation via PTXQC and group-based statistical post-processing with MSstats.
Key facts
- Workflow manager
- Nextflow
- Primary software
- OpenMS, MSstats, Percolator, Luciphor2, PTXQC
- Required inputs
- mzML spectra files, FASTA protein database, experimental design TSV file
- Quantification approaches
- Spectral counting, feature-based alignment and integration
- Latest release
- 1.0.0 (2020-10-19)
- Development status
- Archived (no longer maintained)
- Original developers
- Julianus Pfeuffer, Lukas Heumos, Leon Bichmann, Timo Sachsenberg, Yasset Perez-Riverol
What you can use it for
- Feature-based label-free protein quantification
- Align and integrate peptide chromatographic features across mass spectrometry runs to measure relative protein abundances.
- Group-based differential abundance analysis
- Execute statistical comparisons and feature summarisation between defined experimental conditions using MSstats.
- Post-translational modification mapping
- Localise specific modification sites, such as phosphorylation, across identified peptide sequences using Luciphor2.
- Proteomics quality control assessment
- Evaluate run-level mass spectrometry metrics and sample quality through automated PTXQC reporting.
Strengths and limits
Good at
- Supports peptide identification using multiple database search engines coupled with Percolator re-scoring and FDR filtering.
- Provides flexibility between spectral counting and feature-based alignment and integration for label-free quantification.
- Includes automated post-translational modification site localisation using Luciphor2.
- Delivers integrated quality control reporting via PTXQC alongside MSstats statistical outputs.
Watch out for
- The repository is archived and is no longer maintained.
- Analyses require specific file types including mzML format, a FASTA database, and a tab-delimited experimental design file.
Questions
Can I run nf-core/proteomicslfq without my own cluster?
The research agent runs nf-core/proteomicslfq 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/proteomicslfq under?
MIT, like every nf-core pipeline.
What input files are required to run the pipeline?
The pipeline requires mass spectrometry data files in mzML format, a protein reference database in FASTA format, and an experimental design file in TSV format.
Which quantification strategies are supported?
The workflow supports label-free quantification through either spectral counting or feature-based alignment and chromatographic integration.
How are peptide modifications identified?
The pipeline uses Luciphor2 for modification localisation, such as identifying phosphorylation sites on peptides.
Which tools handle statistical analysis and quality control?
Group-based statistical analysis and feature summarisation are performed using MSstats, while quality control assessment is handled by PTXQC.
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/proteomicslfq's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.