Skip to content

README.md

Latest commit

 

History

History
55 lines (35 loc) · 2.7 KB

README.md

File metadata and controls

55 lines (35 loc) · 2.7 KB

quantms-web — DDA Label-Free Quantification

A browser-based Data-Dependent Acquisition (DDA) Label-Free Quantification workflow for proteomics. Upload mzML files and a protein FASTA; get identified and quantified proteins with volcano plots, PCA, and clustered heatmaps. No CLI, no Nextflow config.

quantms-web mirrors the dda-lfq branch of the quantms Nextflow workflow but runs as a Streamlit app powered by OpenMS TOPP tools.

Pipeline

Stage Tool What it does
1. Identification Comet Peptide-spectrum matching against a protein database
2. Rescoring Percolator ML-based statistical validation of PSMs
3. Filtering IDFilter FDR-controlled peptide identification filtering
4. Quantification ProteomicsLFQ Label-free quantification across samples
5. Analysis Built-in Volcano plots, PCA, heatmaps, spectral library export

Run locally

Install the Python dependencies and launch:

git clone https://github.com/OpenMS/quantms-web.git
cd quantms-web
pip install -r requirements.txt
streamlit run app.py

The full pipeline runs locally once the OpenMS Command Line Tools are on your PATH — they provide Comet, Percolator, ProteomicsLFQ, and the rest of the TOPP suite. With Python alone, the pyOpenMS-backed parts of the UI still work.

Run with Docker

Ships OpenMS and the search engines together so the full pipeline works out of the box:

docker-compose up -d --build

Open http://localhost:8501.

Windows installer

Download the latest .msi from Releases and double-click to install. Standalone — no Python or Docker required.

Workspaces

Every analysis session runs in an isolated workspace that persists inputs, parameters, and results. In online deployments the workspace ID is part of the URL, so runs are resumable and shareable.

Citation

Müller, T. D., Siraj, A., et al. OpenMS WebApps: Building User-Friendly Solutions for MS Analysis. Journal of Proteome Research (2025). doi:10.1021/acs.jproteome.4c00872

References

  • Pfeuffer, J., Bielow, C., Wein, S. et al. OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data. Nat Methods 21, 365–367 (2024). doi:10.1038/s41592-024-02197-7
  • Röst HL, Schmitt U, Aebersold R, Malmström L. pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 14, 74–77 (2014). doi:10.1002/pmic.201300246