A hierarchical statistical model to assess the confidence of peptides and proteins inferred from tandem mass spectrometry

Changyu Shen, Zhiping Wang, Ganesh Shankar, Xiang Zhang, Lang Li

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

Motivation: Statistical evaluation of the confidence of peptide and protein identifications made by tandem mass spectrometry is a critical component for appropriately interpreting the experimental data and conducting downstream analysis. Although many approaches have been developed to assign confidence measure from different perspectives, a unified statistical framework that integrates the uncertainty of peptides and proteins is still missing. Results: We developed a hierarchical statistical model (HSM) that jointly models the uncertainty of the identified peptides and proteins and can be applied to any scoring system. With data sets of a standard mixture and the yeast proteome, we demonstrate that the HSM offers a reliable or at least conservative false discovery rate (FDR) estimate for peptide and protein identifications. The probability measure of HSM also offers a powerful discriminating score for peptide identification.

Original languageEnglish (US)
Pages (from-to)202-208
Number of pages7
JournalBioinformatics
Volume24
Issue number2
DOIs
StatePublished - Jan 15 2008

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ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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