From Raw Spectra to Biological Insight
An open-source ecosystem for large-scale quantitative proteomics. Cloud-ready Nextflow pipelines, standalone Python libraries, and ML-powered tools — process DDA and DIA data with dedicated workflows, or use the standalone tools with any search engine.
# DDA proteomics pipeline (LFQ & TMT/iTRAQ) nextflow run bigbio/quantms \ -profile docker \ --input samplesheet.csv \ --database uniprot.fasta \ --outdir results/ # Runs on laptops, HPC, or AWS/GCP/Azure # DDA-LFQ, DDA-ISO (TMT/iTRAQ) workflows
# DIA proteomics pipeline (powered by DIA-NN) nextflow run bigbio/quantmsdiann \ -profile docker \ --input samplesheet.csv \ --database uniprot.fasta \ --outdir results/ # Dedicated DIA workflow with DIA-NN engine # Library-free or spectral library modes
# Convert search engine outputs to unified format qpx convert \ --tool diann \ --report diann_report.tsv \ --sdrf experiment.sdrf.tsv # Supported: DIA-NN, MaxQuant, FragPipe, # Spectronaut, quantms, Sage, MSFragger # Export to Parquet or AnnData (.h5ad)
# Protein quantification from any search engine # Works with quantms, quantmsdiann, MaxQuant, FragPipe mokume features2proteins \ -p features.parquet \ -o proteins.csv \ -s experiment.sdrf.tsv \ --quant-method maxlfq # TMT with IRS + differential expression mokume features2proteins \ -p features.parquet \ --quant-method median \ --irs --de
# Interactive QC reports for any experiment multiqc results/ \ --module pmultiqc # Generate mokume QC + comparison reports mokume features2proteins \ -p features.parquet \ -s experiment.sdrf.tsv \ --report qc_report.html # HTML reports: PCA, heatmaps, CVs, volcanos
The Pipeline
A Nextflow-orchestrated workflow where specialized tools handle each step.
End-to-End Quantitative Proteomics
Two dedicated Nextflow pipelines: quantms for DDA (LFQ & TMT/iTRAQ) and quantmsdiann for DIA — but the downstream tools work independently too. Use mokume to quantify proteins from any search engine output. Use pmultiqc for QC on any experiment.
- 1Convert & search raw spectra against protein databases
- 2Rescore PSMs with ML models for higher sensitivity
- 3Quantify proteins using iBAQ, MaxLFQ, TopN, or ratio methods
- 4Generate QC reports and differential expression results
Raw Data Acquisition
.mzML · .raw · .dIdentification & Rescoring
MS2PIP · DeepLC · PercolatorQuantification
iBAQ · MaxLFQ · TopN · RatioQuality Control & DE
Interactive reports · Volcano plotsData & Exploration
Browse reanalyzed proteomics datasets and explore results interactively.
Explore Reanalyzed Datasets
The quantms.org data portal hosts hundreds of public proteomics datasets reanalyzed from ProteomeXchange repositories (PRIDE, MassIVE, jPOST). Browse absolute and differential expression across tissues, diseases, and organisms. All ecosystem tools also work locally for your own data analysis.
AI-Powered Proteomics
Deep learning models trained on 500M+ spectra, powering the next generation of proteomics analysis.
From Data to Models
The quantms ecosystem reanalyzes hundreds of public proteomics datasets, producing standardized, metadata-rich results. These datasets train deep learning models that improve every step of the pipeline — from spectrum prediction to de novo sequencing.
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Public DatasetsPublic experiments from ProteomeXchange (PRIDE, MassIVE, jPOST)
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quantms ReanalysisStandardized processing with quantms & quantmsdiann
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AI Model TrainingAlphaPeptDeep, AutoRT, GPTime, HelixNovo
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Better Proteomics10-30% more identifications, de novo sequencing, RT prediction
Standards & Formats
Built on open formats for reproducible, metadata-rich proteomics.
Reproducibility by Design
Every experiment in the ecosystem is described with SDRF (Sample and Data Relationship Format), ensuring complete metadata annotation. The qpx library bridges the gap between search engines — converting outputs from quantms, quantmsdiann, DIA-NN, FragPipe, MaxQuant, and Spectronaut into a unified format.
Convert outputs from any search engine into a unified Parquet format. Export to AnnData for integration with scverse and other analysis ecosystems.
qpx documentation →SDRF — Sample Metadata
Standardized experimental design: organisms, tissues, labels, conditions. Used by all quantms tools for automatic sample grouping.
qpx — Format Conversion
Convert from DIA-NN, FragPipe, MaxQuant, Spectronaut, quantms. Unified Parquet output with PSM-level data.
AnnData — Interoperability
Export protein matrices to .h5ad for integration with scanpy, scverse, and the broader Python data science ecosystem.
Quantification Library
mokume — the protein quantification engine at the heart of quantms.
mokume
A comprehensive Python library for protein quantification from peptide-level data. Works standalone with output from any search engine — not just quantms. Supports 6+ quantification methods, normalization, IRS for multi-plex TMT, batch correction, and differential expression.
# LFQ quantification mokume features2proteins \ -p features.parquet \ -o proteins.csv \ -s experiment.sdrf.tsv \ --quant-method maxlfq # TMT + IRS + DE analysis mokume features2proteins \ -p features.parquet \ --quant-method median \ --irs --de \ --de-contrasts "NASH-HL"
from mokume.pipeline import ( QuantificationPipeline, PipelineConfig ) from mokume.pipeline.config import ( InputConfig, QuantificationConfig ) config = PipelineConfig( input=InputConfig( parquet="features.parquet", sdrf="experiment.sdrf.tsv", ), quantification=QuantificationConfig( method="maxlfq", ), ) pipeline = QuantificationPipeline(config) proteins = pipeline.run()
// Run quantms pipeline nextflow run bigbio/quantms \ -profile docker \ --input samplesheet.csv \ --database uniprot.fasta \ --outdir results/ // Cloud-ready: AWS, GCP, Azure, HPC
Visualization & Reports
Publication-ready QC reports and interactive visualizations.
From QC to Volcano Plots
pmultiqc generates interactive HTML reports with comprehensive QC metrics for every experiment. mokume adds differential expression reports, interactive plots, and workflow comparison dashboards.
- Sample correlation heatmaps & PCA
- CV distributions & missing value analysis
- Volcano plots & differential expression tables
- Workflow comparison across methods
- Interactive HTML — no extra tools needed
The Ecosystem
Seven specialized tools and a data portal, one unified platform.
quantmsdiann
DIA proteomics powered by DIA-NN. Library-free and spectral library modes.
mokume
Protein quantification, normalization, batch correction, DE. Successor to ibaqpy.
sdrf-pipelines
SDRF validation, annotation, and conversion for proteomics metadata.
Why quantms
Standards-First
SDRF metadata, open file formats, and reproducible workflows ensure every experiment is fully documented and shareable.
Large-Scale & Cloud-Ready
Reanalyze hundreds of public datasets at cloud scale. Nextflow orchestration with Docker/Singularity runs on laptops, HPC clusters, or AWS/GCP/Azure. Python libraries handle local analysis just as well.
Works With Your Tools
Not locked in. Use mokume and pmultiqc with data from quantms, quantmsdiann, MaxQuant, FragPipe, Spectronaut, or DIA-NN. Export to AnnData for the scverse ecosystem.
Publication-Ready
Interactive QC reports, volcano plots, PCA, heatmaps, and differential expression tables — ready for your next manuscript.
Open Science, Open Community
Contributors
- Yasset Perez-Riverol
- Chengxin Dai
- Julianus Pfeuffer
- Timo Sachsenberg
- Fabian Egli
- Henry Webel
- Mingze Bai
- See all core contributors
Join 20+ contributors from EMBL-EBI, OpenMS, and research institutions worldwide.
Contribute on GitHub