A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

详细信息    查看全文

• 作者：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
• 刊名：Analytical Chemistry
• 出版年：2011
• 出版时间：August 15, 2011
• 年：2011
• 卷：83
• 期：16
• 页码：6135-6140
• 全文大小：885K
• 年卷期：v.83,no.16(August 15, 2011)
• ISSN：1520-6882

文摘

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.