A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spect...

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Published inJournal of proteome research Vol. 21; no. 6; pp. 1566 - 1574
Main Authors Luo, Xiyang, Bittremieux, Wout, Griss, Johannes, Deutsch, Eric W., Sachsenberg, Timo, Levitsky, Lev I., Ivanov, Mark V., Bubis, Julia A., Gabriels, Ralf, Webel, Henry, Sanchez, Aniel, Bai, Mingze, Käll, Lukas, Perez-Riverol, Yasset
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 03.06.2022
Subjects
Annan naturvetenskap
benchmark
big data
clustering
consensus spectra
mass spectrometry
Natural Sciences
Naturvetenskap
Other Natural Sciences
Other Natural Sciences not elsewhere specified
pride database
ProteomeXchange
spectral libraries
Technical Note
Övrig annan naturvetenskap
ProteomeXchange
consensus spectra
pride database
mass spectrometry
clustering
big data
spectral libraries
benchmark
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Summary:Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.
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ISSN:1535-3893
1535-3907
1535-3907
DOI:10.1021/acs.jproteome.2c00069