pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics
The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, FragPipe, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.
Authors
- Vadim Petrovich Demichev (ORCID: https://orcid.org/0000-0002-2424-9412)
- Julian Uszkoreit (ORCID: https://orcid.org/0000-0001-7522-4007)
- Philip Ewels (ORCID: https://orcid.org/0000-0003-4101-2502)
- Chakradhar Bandla (ORCID: https://orcid.org/0000-0001-6392-3759)
- Henry Webel (ORCID: https://orcid.org/0000-0001-8833-7617)
- Timo T. Sachsenberg (ORCID: https://orcid.org/0000-0002-2833-6070)
- Oliver Kohlbacher (ORCID: https://orcid.org/0000-0003-1739-4598)
- Asier Larrea‐Sebal (ORCID: https://orcid.org/0000-0001-9107-4299)
- Chris Bielow (ORCID: https://orcid.org/0000-0001-5756-3988)
- Selvakumar Kamatchinathan (ORCID: https://orcid.org/0009-0001-3644-2586)
- Mingze Bai (ORCID: https://orcid.org/0000-0002-0112-2750)
- Qi-Xuan Yue (ORCID: https://orcid.org/0009-0004-8120-9339)
- Tom David Müller (ORCID: https://orcid.org/0000-0003-0118-9434)
- Chengxin Dai
- Jinqiu Xiao
- Wout Bittremieux
- Yasset Perez-Riverol
- Fengchao Yu
- Juergen Cox
Institutions
- Chongqing University of Posts and Telecommunications (CN)
- European Bioinformatics Institute (GB)
- University of Antwerp (BE)
- University of Michigan (US)
- University Hospitals of the Ruhr-University of Bochum (DE)
- Novo Nordisk Foundation (DK)
- Agbar (Spain) (ES)
- Bernstein Center for Computational Neuroscience Tübingen (DE)
- Beijing Proteome Research Center (CN)
- Max Planck Institute of Biochemistry (DE)
- Freie Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
- University of Tübingen (DE)
- Wellcome Trust (GB)
- Ruhr University Bochum (DE)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Molecular & Cellular Proteomics
- Published
- 2026-02-01
- DOI
- https://doi.org/10.1016/j.mcpro.2026.101530
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00