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Instance Based Algorithm for Posterior Probability Calculation by Target-Decoy Strategy to Improve Protein Identifications
Jiang, Xinning1,2; Dong, Xiaoli1,3; Ye, Mingliang1; Zou, Hanfa1; Zou HF(邹汉法); Zou HF(邹汉法)
Source PublicationANALYTICAL CHEMISTRY
2008-12-01
ISSN0003-2700
DOI10.1021/ac8017229
Volume80Issue:23Pages:9326-9335
Indexed BySCI
SubtypeArticle
Department18
Funding Project1809
Contribution Rank1;1
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectChemistry, Analytical
WOS Research AreaChemistry
WOS KeywordTANDEM MASS-SPECTROMETRY ; INDUCED DISSOCIATION SPECTRA ; PEPTIDE MS/MS SPECTRA ; NEAREST-NEIGHBOR RULE ; DATABASE SEARCH ; STATISTICAL-MODEL ; SHOTGUN PROTEOMICS ; YEAST PROTEOME ; SEQUEST ; VALIDATION
AbstractThe target-decoy database search strategy is often applied to determine the global false-discovery rate (FDR) of peptide identifications in proteome research. However, the confidence of individual peptide identification is typically not determined. In this study, we introduced an approach for the calculation of posterior probability of individual peptide identification from the "local false-discovery rate" (local FDR), which is also determined based on a target-decoy database search. The peptide identification scores output by the database search algorithm were weighted by their discriminating power using a Shannon information entropy based strategy. Then the local FDR of a peptide identification was calculated based on the fraction of decoy identifications among its nearest neighbors within a small space defined by these weighted scores. It was demonstrated that the calculated probability matched the actual probability precisely, and it provided powerful discriminating performance between true positive and false positive identifications. Hence, the sensitivity for peptide identification as well as protein identification was significantly improved when the calculated probability was used to process different proteome data sets. As an instance based strategy, this algorithm provides a safe way for the posterior probability calculation and should work well for datasets with different characteristics.
Language英语
URL查看原文
WOS IDWOS:000261335600061
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/100923
Collection中国科学院大连化学物理研究所
Corresponding AuthorZou HF(邹汉法); Zou HF(邹汉法)
Affiliation1.Chinese Acad Sci, Dalian Inst Chem Phys, Natl Chromatog R&A Ctr, Dalian 116023, Peoples R China
2.Chinese Acad Sci, Grad Sch, Beijing 100049, Peoples R China
3.Zhejiang Univ, Dept Chem, Hangzhou 310028, Peoples R China
Recommended Citation
GB/T 7714
Jiang, Xinning,Dong, Xiaoli,Ye, Mingliang,et al. Instance Based Algorithm for Posterior Probability Calculation by Target-Decoy Strategy to Improve Protein Identifications[J]. ANALYTICAL CHEMISTRY,2008,80(23):9326-9335.
APA Jiang, Xinning,Dong, Xiaoli,Ye, Mingliang,Zou, Hanfa,邹汉法,&邹汉法.(2008).Instance Based Algorithm for Posterior Probability Calculation by Target-Decoy Strategy to Improve Protein Identifications.ANALYTICAL CHEMISTRY,80(23),9326-9335.
MLA Jiang, Xinning,et al."Instance Based Algorithm for Posterior Probability Calculation by Target-Decoy Strategy to Improve Protein Identifications".ANALYTICAL CHEMISTRY 80.23(2008):9326-9335.
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