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HiXCorr: a portable high-speed X-Corr engine for high-resolution tandem mass spectrometry

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dc.contributor.authorKim, Hyunwoo-
dc.contributor.authorJo, Hosung-
dc.contributor.authorPark, Heejin-
dc.contributor.authorPaek, Eunok-
dc.date.accessioned2022-07-15T20:03:11Z-
dc.date.available2022-07-15T20:03:11Z-
dc.date.created2021-05-12-
dc.date.issued2015-12-
dc.identifier.issn1367-4803-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/155761-
dc.description.abstractPeptide identification is an important problem in proteomics. One of the most popular scoring schemes for peptide identification is X-Corr (cross-correlation). Since calculating X-Corr is computationally intensive, a lot of efforts have been made to develop fast X-Corr engines. However, the existing X-Corr engines are not suitable for high-resolution MS/MS spectrometry because they are either slow or require a specific type of CPU. We present a portable high-speed X-Corr engine for high-resolution tandem mass spectrometry by developing a novel algorithm for calculating X-Corr. The algorithm enables X-Corr calculation 1.25-49 times faster than previous algorithms for 0.01 Da fragment tolerance. Furthermore, our engine is easily portable to any machine with different types of CPU because it is developed in C language. Hence, our X-Corr engine will expedite peptide identification by high-resolution tandem mass spectrometry.-
dc.language영어-
dc.language.isoen-
dc.publisherOXFORD UNIV PRESS-
dc.titleHiXCorr: a portable high-speed X-Corr engine for high-resolution tandem mass spectrometry-
dc.typeArticle-
dc.contributor.affiliatedAuthorPark, Heejin-
dc.contributor.affiliatedAuthorPaek, Eunok-
dc.identifier.doi10.1093/bioinformatics/btv490-
dc.identifier.scopusid2-s2.0-84950271001-
dc.identifier.wosid000366630400028-
dc.identifier.bibliographicCitationBIOINFORMATICS, v.31, no.24, pp.4026 - 4028-
dc.relation.isPartOfBIOINFORMATICS-
dc.citation.titleBIOINFORMATICS-
dc.citation.volume31-
dc.citation.number24-
dc.citation.startPage4026-
dc.citation.endPage4028-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusDATABASE-
dc.identifier.urlhttp://dx.doi.org/10.1093/bioinformatics/btv490-
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