Verification of single-peptide protein identifications by the application of complementary database search algorithms

James G. Rohrbough, Linda Breci, Nirav Merchant, Susan Miller, Paul A. Haynes

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

Data produced from the MudPIT analysis of yeast (S. cerevisiae) and rice (O. sativa) were used to develop a technique to validate single-peptide protein identifications using complementary database search algorithms. This results in a considerable reduction of overall false-positive rates for protein identifications; the overall false discovery rates in yeast are reduced from near 25% to less than 1%, and the false discovery rate of yeast single-peptide protein identifications becomes negligible. This technique can be employed by laboratories utilizing a SEQUEST-based proteomic analysis platform, incorporating the XTandem algorithm as a complementary tool for verification of single-peptide protein identifications. We have achieved this using open-source software, including several data-manipulation software tools developed in our laboratory, which are freely available to download.

Original languageEnglish (US)
Pages (from-to)327-332
Number of pages6
JournalJournal of Biomolecular Techniques
Volume17
Issue number5
StatePublished - Dec 2006

Keywords

  • Database search algorithms
  • Protein identification
  • SEQUEST criteria
  • Single-peptide verification
  • Tandem mass spectrometry
  • Xtandem

ASJC Scopus subject areas

  • Molecular Biology

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