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• W2922140724 abstract "Label-free quantification has become a common-practice in many mass spectrometry-based proteomics experiments. In recent years, we and others have shown that spectral clustering can considerably improve the analysis of (primarily large-scale) proteomics data sets. Here we show that spectral clustering can be used to infer additional peptide-spectrum matches and improve the quality of label-free quantitative proteomics data in data sets also containing only tens of MS runs. We analyzed four well-known public benchmark data sets that represent different experimental settings using spectral counting and peak intensity based label-free quantification. In both approaches, the additionally inferred peptide-spectrum matches through our spectra-cluster algorithm improved the detectability of low abundant proteins while increasing the accuracy of the derived quantitative data, without increasing the data sets’ noise. Additionally, we developed a Proteome Discoverer node for our spectra-cluster algorithm which allows anyone to rebuild our proposed pipeline using the free version of Proteome Discoverer." @default.
• W2922140724 created "2019-03-22" @default.
• W2922140724 creator A5001448895 @default.
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• W2922140724 date "2019-03-12" @default.
• W2922140724 modified "2023-10-17" @default.
• W2922140724 title "Spectral Clustering Improves Label-Free Quantification of Low-Abundant Proteins" @default.
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• W2922140724 doi "https://doi.org/10.1021/acs.jproteome.8b00377" @default.
• W2922140724 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6456873" @default.
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