A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics

Research output: Contribution to journalJournal articlepeer-review

Documents

  • Fulltext

    Final published version, 7.49 MB, PDF document

  • Xiyang Luo
  • Wout Bittremieux
  • Johannes Griss
  • Eric W Deutsch
  • Timo Sachsenberg
  • Lev I Levitsky
  • Mark V Ivanov
  • Julia A Bubis
  • Ralf Gabriels
  • Webel, Henry
  • Aniel Sanchez
  • Mingze Bai
  • Lukas Käll
  • Yasset Perez-Riverol

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.

Original languageEnglish
JournalJournal of Proteome Research
Volume21
Issue number6
Pages (from-to)1566-1574
Number of pages9
ISSN1535-3893
DOIs
Publication statusPublished - 2022

    Research areas

  • Algorithms, Cluster Analysis, Consensus, Databases, Protein, Proteomics/methods, Software, Tandem Mass Spectrometry/methods

ID: 311609614