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Accurate quantification in proteomics with QuantUMS
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DOI：10.1038/s41587-026-03131-2.png)
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Abstract
En 中文
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. QuantUMS implements uncertainty estimation for protein quantification in mass spectrometry.
Keywords:
QuantUMS
proteomics
protein quantification
machine learning
uncertainty estimation
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