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<p>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.</p>

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Accurate quantification in proteomics with QuantUMS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Justus L. Grossmann,
  • Franziska Kistner,
  • Ludwig R. Sinn,
  • Lukasz Szyrwiel,
  • Juri Rappsilber,
  • Vadim Demichev

摘要

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.