nf-core pipeline · data-independent-proteomics, dia-proteomics, openms
nf-core/diaproteomics
Automated quantitative analysis of data-independent acquisition (DIA) proteomics mass spectrometry measurements.
What nf-core/diaproteomics is
nf-core/diaproteomics is a bioinformatics analysis pipeline designed for the automated quantitative processing of data-independent acquisition (DIA) proteomics and peptidomics datasets. Mass spectrometry data generated via DIA and SWATH-MS acquisition contains multiplexed fragment ion spectra that require sophisticated targeted extraction and alignment workflows. The pipeline automates these computationally intensive tasks to produce reliable peptide- and protein-level quantifications.
Originally written by Leon Bichmann with contributions from Shubham Gupta, George Rosenberger, Leon Kuchenbecker, Timo Sachsenberg, Oliver Alka, Julianus Pfeuffer, Oliver Kohlbacher, and Hannes Röst, the workflow bridges spectral library preparation, signal extraction, false discovery rate estimation, and multi-run statistical modelling into a standardised execution framework.
What the pipeline does
The workflow operates on DIA mass spectrometry measurements provided in mzML format. Targeted extraction is performed using OpenSwathWorkflow guided by an input spectral library. If a pre-existing library is unavailable, the pipeline can optionally construct one from matched data-dependent acquisition (DDA) data using EasyPQP, align retention times across library runs pairwise, and concatenate them into a single reference library. Retention time spaces between libraries and DIA runs are aligned using either provided internal retention time standards (iRTs) or pipeline-generated pseudo-iRTs.
Extracted peak groups undergo statistical rescoring with PyProphet using a competitive target-decoy approach, enabling false discovery rate (FDR) control at the peak group, peptide, or protein level. DIAlignR subsequently carries out cross-run chromatogram alignment and quantification. Finally, MSstats performs statistical post-processing, yielding exported CSV files of peptide quantities, protein statistics, and diagnostic visualisations.
Key facts
- Input formats
- mzML (DIA data), TSV (sample sheets, spectral libraries, and iRT tables)
- Targeted extraction engine
- OpenSwathWorkflow
- Spectral library generation
- EasyPQP (from matched DDA runs)
- FDR rescoring tool
- PyProphet (target-decoy approach)
- Chromatogram alignment
- DIAlignR
- Statistical analysis
- MSstats
- Primary developer
- Leon Bichmann
- Latest release
- 1.2.4 (2021-04-29)
What you can use it for
- Quantitative DIA and SWATH-MS analysis
- Extracting and quantifying targeted peptide signals from DIA mzML runs using an existing spectral library.
- De novo spectral library generation
- Building, aligning, and merging experimental spectral libraries from matched DDA measurements using EasyPQP prior to DIA analysis.
- Multi-run chromatogram alignment
- Aligning retention times and integrating chromatogram peaks across large sample series using DIAlignR.
- Differential protein abundance testing
- Calculating peptide quantities and deriving comparative group-level protein statistics via MSstats.
Run it here
The research agent runs nf-core/diaproteomics 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
- Combines OpenSwathWorkflow, PyProphet, DIAlignR, and MSstats into an automated pipeline.
- Supports both user-provided spectral libraries and de novo library generation from matched DDA files using EasyPQP.
- Provides flexibility in retention time calibration by supporting either supplied iRT standards or internally generated pseudo-iRTs.
- Enables target-decoy false discovery rate rescoring across peak group, peptide, and protein levels.
Watch out for
- Requires mass spectrometry data to be pre-converted into the open mzML format rather than native instrument formats.
- Demands either matched DDA runs for library generation or an appropriate pre-existing spectral library covering the target proteome.
Questions
Can I run nf-core/diaproteomics without my own cluster?
The research agent runs nf-core/diaproteomics 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/diaproteomics under?
MIT, like every nf-core pipeline.
What input file formats does the pipeline accept?
Mass spectrometry measurements must be supplied as mzML files. Metadata, library definitions, and retention time standards are provided via TSV sheets.
Can the pipeline create a spectral library if one is not provided?
Yes. By supplying matched DDA runs and search results, the pipeline uses EasyPQP to generate spectral libraries, align their retention times, and concatenate them into a single library.
How are retention times aligned between library and DIA runs?
Retention times are calibrated using internal retention time standards (iRTs), which can either be supplied in an input sheet or generated automatically as pseudo-iRTs.
How is the false discovery rate controlled?
PyProphet calculates error rates using a competitive target-decoy approach, allowing thresholding at the peak group level or globally across peptides and proteins.
What final outputs does the workflow generate?
The workflow produces CSV files of peptide quantities, differential protein statistics from MSstats, and several analytical visualisations.
How to cite it
Leon Bichmann, Shubham Gupta, George Rosenberger, Leon Kuchenbecker, Timo Sachsenberg, Oliver Alka, Julianus Pfeuffer, Oliver Kohlbacher & Hannes Rost. DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics. bioRxiv: "https://www.biorxiv.org/content/10.1101/2020.12.08.415844v1"
Written from nf-core/diaproteomics's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.