Grossmann, J. L. | Search bioRxiv

bioRxiv2025

Large-scale quantitative benchmark reveals accuracy limits of proteomics

Quantitative proteomics has become a streamlined method for high-throughput interrogation of biological samples, with a wide range of applications in both basic and clinical research. We present a benchmark data set and methodology for evaluating the accuracy of proteomics in a high-throughput setting. We build upon previous works by incorporating the variation of sample amounts and the background proteome into the experiment design. This approach allows us to comprehensively map the factors that determine the accuracy of proteomic measurements, revealing significant systematic errors missed by previous works. We envision these data to assist in future method optimisation as well as provide a comprehensive benchmark for data analysis software algorithms.

biochemistry↗

bioRxiv2023

QuantUMS: uncertainty minimisation enables confident quantification in proteomics

Mass spectrometry-based proteomics has been rapidly gaining traction as a powerful analytical method both in basic research and translation. While the problem of error control in peptide and protein identification has been addressed extensively, the quality of the resulting quantities remains challenging to evaluate. Here we introduce QuantUMS (Quantification using an Uncertainty Minimising Solution), a machine learning-based method which minimises errors and eliminates bias in peptide and protein quantification by integrating multiple sources of quantitative information. In combination with data-independent acquisition proteomics, QuantUMS boosts accuracy and precision of quantities, as well as reports an uncertainty metric, enabling effective filtering of data for downstream analysis. The algorithm has linear complexity with respect to the number of mass spectrometry acquisitions in the experiment and is thus scalable to infinitely large proteomic experiments. For an easy implementation in a proteomics laboratory, we integrate QuantUMS in our automated DIA-NN software suite.

bioinformatics↗