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Peer-reviewedCC BY-NC-NDNature Biotechnology

Accurate quantification in proteomics with QuantUMS

  • Justus L. Großmann1
  • Franziska Kistner1
  • Ludwig Roman Sinn1
  • Łukasz Szyrwiel1
  • Juri Rappsilber2,3
  • Vadim Petrovich Demichev4
4 affiliations
  1. 1Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
  2. 2Bioanalytics, Institute of Biotechnology, Technical University of Berlin, Berlin, Germany
  3. 3Si-M/"Der Simulierte Mensch", Technical University of Berlin and Charité - Universitätsmedizin Berlin, Berlin, Germany
  4. 4Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. [email protected]
Open at sourceDOICite

Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{großmann2026accurate,
  title = {{Accurate quantification in proteomics with QuantUMS}},
  author = {Justus L. Großmann and Franziska Kistner and Ludwig Roman Sinn and Łukasz Szyrwiel and Juri Rappsilber and Vadim Petrovich Demichev},
  journal = {Nature Biotechnology},
  year = {2026},
  month = may,
  doi = {10.1038/s41587-026-03131-2},
  url = {https://doi.org/10.1038/s41587-026-03131-2},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Accurate quantification in proteomics with QuantUMS
AU  - Justus L. Großmann
AU  - Franziska Kistner
AU  - Ludwig Roman Sinn
AU  - Łukasz Szyrwiel
AU  - Juri Rappsilber
AU  - Vadim Petrovich Demichev
PY  - 2026
DA  - 2026/05/27
JO  - Nature Biotechnology
DO  - 10.1038/s41587-026-03131-2
UR  - https://doi.org/10.1038/s41587-026-03131-2
AB  - 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.
SN  - 0733-222X
SN  - 1087-0156
SN  - 1546-1696
SN  - 2331-3684
ER  -