Target-small decoy search strategy for false discovery rate estimation
DC Field | Value | Language |
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dc.contributor.author | Kim, Hyunwoo | - |
dc.contributor.author | Lee, Sangjeong | - |
dc.contributor.author | Park, Heejin | - |
dc.date.accessioned | 2022-07-09T11:31:43Z | - |
dc.date.available | 2022-07-09T11:31:43Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2019-08 | - |
dc.identifier.issn | 1471-2105 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147386 | - |
dc.description.abstract | Background One of the most important steps in peptide identification is to estimate the false discovery rate (FDR). The most commonly used method for estimating FDR is the target-decoy search strategy (TDS). While this method is simple and effective, it is time/space-inefficient because it searches a database that is twice as large as the original protein database. This inefficiency problem becomes more evident as protein databases get bigger and bigger. We propose a target-small decoy search strategy and present a rigorous verification that it reduces the database size and search time while retaining the accuracy of target-decoy search strategy (TDS). Results We show that peptide spectrum matches (PSMs) obtained at 1% FDR in TDS overlap similar to 99% with those in our method. (Considering that 1% FDR is used, 99% overlap means our method is very accurate.) Moreover, our method is more time/space-efficient than TDS. The search time of our method is reduced to only 1/4 of that of TDS when UniProt and its 1/8 decoy database are used. Conclusions We demonstrate that our method is almost as accurate as TDS and more time/space-efficient than TDS. Since the efficiency of our method is more evident as the database size increases, our method is expected to be useful for identifying peptides in proteogenomics databases constructed from inflated databases using genomic data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | BMC | - |
dc.title | Target-small decoy search strategy for false discovery rate estimation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Heejin | - |
dc.identifier.doi | 10.1186/s12859-019-3034-8 | - |
dc.identifier.scopusid | 2-s2.0-85071415765 | - |
dc.identifier.wosid | 000482926200001 | - |
dc.identifier.bibliographicCitation | BMC BIOINFORMATICS, v.20, no.1, pp.1 - 6 | - |
dc.relation.isPartOf | BMC BIOINFORMATICS | - |
dc.citation.title | BMC BIOINFORMATICS | - |
dc.citation.volume | 20 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Biochemistry & Molecular Biology | - |
dc.relation.journalResearchArea | Biotechnology & Applied Microbiology | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Biochemical Research Methods | - |
dc.relation.journalWebOfScienceCategory | Biotechnology & Applied Microbiology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.subject.keywordPlus | PEPTIDE IDENTIFICATIONS | - |
dc.subject.keywordPlus | PROTEOGENOMIC SEARCH | - |
dc.subject.keywordPlus | DATABASE | - |
dc.subject.keywordPlus | MS/MS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Target-decoy search | - |
dc.subject.keywordAuthor | Target-small decoy search | - |
dc.subject.keywordAuthor | False discovery rate | - |
dc.identifier.url | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3034-8 | - |
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