Dispec: A Novel Peptide Scoring Algorithm Based on Peptide Matching Discriminability

Chuan-Le Xiao, Xiao-Zhou Chen, Yang-Li Du, Zhe-Fu Li, Li Wei, Gong Zhang and Qing-Yu He

PLOS ONE, 2013, vol. 8, issue 5, 1-7

Abstract: Identifying peptides from the fragmentation spectra is a fundamental step in mass spectrometry (MS) data processing. The significance (discriminability) of every peak varies, providing additional information for potentially enhancing the identification sensitivity and the correct match rate. However this important information was not considered in previous algorithms. Here we presented a novel method based on Peptide Matching Discriminability (PMD), in which the PMD information of every peak reflects the discriminability of candidate peptides. In addition, we developed a novel peptide scoring algorithm Dispec based on PMD, by taking three aspects of discriminability into consideration: PMD, intensity discriminability and m/z error discriminability. Compared with Mascot and Sequest, Dispec identified remarkably more peptides from three experimental datasets with the same confidence at 1% PSM-level FDR. Dispec is also robust and versatile for various datasets obtained on different instruments. The concept of discriminability enhances the peptide identification and thus may contribute largely to the proteome studies. As an open-source program, Dispec is freely available at http://bioinformatics.jnu.edu.cn/software/dispec/.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0062724

DOI: 10.1371/journal.pone.0062724

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