A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

Jeffrey R. Stanley, Joshua N. Adkins, Gordon W. Slysz, Matthew E. Monroe, Samuel O. Purvine, Yuliya V. Karpievitch, Gordon A. Anderson, Richard D. Smith, Alan R. Dabney

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

Figure Presented Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

Original languageEnglish
Pages (from-to)6135-6140
Number of pages6
JournalAnalytical Chemistry
Volume83
Issue number16
DOIs
Publication statusPublished - 15 Aug 2011
Externally publishedYes

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