Accurate quantification in proteomics with QuantUMS
In mass-spectrometry-based proteomics it remains challenging to ensure the accuracy of protein quantities. Here we introduce QuantUMS (quantification using an uncertainty-minimizing solution), a machine learning-based method that dynamically tunes the quantification algorithm to minimize quantitative errors. When applied to data-independent acquisition proteomics, QuantUMS increases accuracy and precision, ameliorates ratio compression bias and enhances differential expression analysis. It further reports an uncertainty measure enabling quality control of individual quantities.
Authors
- Justus L. Großmann (ORCID: https://orcid.org/0009-0002-7094-1888)
- Łukasz Szyrwiel (ORCID: https://orcid.org/0000-0003-1983-2950)
- Franziska Kistner (ORCID: https://orcid.org/0009-0009-8080-8643)
- Ludwig R. Sinn (ORCID: https://orcid.org/0000-0003-4692-0681)
- Vadim Demichev (ORCID: https://orcid.org/0000-0002-2424-9412)
- Juri Rappsilber (ORCID: https://orcid.org/0000-0001-5999-1310)
Institutions
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Technische Universität Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Nature Biotechnology
- Published
- 2026-05-27
- DOI
- https://doi.org/10.1038/s41587-026-03131-2
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bundesministerium für Bildung und Forschung