Score regularization for peptide identification

BMC Bioinformatics

Research

Hongyu Zhao¹  Weichuan Yu²  Zengyou He³ 

[1] Department of Epidemiology and Public Health, Yale University, 06520, New Haven, Connecticut, USA;Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China;School of Software, Dalian University of Technology, Dalian, China;

关键词: Ranking Score;    Peptide Identification;    Similar Peptide;    Spectral Graph Theory;    Area Under Receiver Operating Characteristic Curve;   

DOI  :  10.1186/1471-2105-12-S1-S2

来源: Springer

【 摘 要 】

BackgroundPeptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.ResultsIn this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.ConclusionsThe score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: http://bioinformatics.ust.hk/SRPI.rar.

【 授权许可】

CC BY   
© He et al; licensee BioMed Central Ltd. 2011