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AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing

DC FieldValueLanguage
dc.contributor.author김상우-
dc.contributor.author백순명-
dc.contributor.author이한나-
dc.date.accessioned2018-08-28T17:02:22Z-
dc.date.available2018-08-28T17:02:22Z-
dc.date.issued2018-
dc.identifier.issn1367-4803-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/162185-
dc.description.abstractSummary: Ion Torrent sequencing is one of the most frequently used platforms in healthcare research and industry. Despite many advantages, platform-specific artifacts complicate efficient separation of true variants from errors, especially in variants with lower allele frequencies (<15%). Here, we developed a multi-step filtering toolbox AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to approximately 98% reduction of false variants when combined to conventional public pipelines and approximately 48% to the in-house commercial solution, with a minimal loss of sensitivity. Availability and implementation: The program with a detailed manual is available at https://sourceforge.net/projects/airvf/. Contact: swkim@yuhs.ac. Supplementary information: Supplementary data are available at Bioinformatics online.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherOxford University Press-
dc.relation.isPartOfBIOINFORMATICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleAIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Life Science-
dc.contributor.googleauthorSunguk Shin-
dc.contributor.googleauthorHanna Lee-
dc.contributor.googleauthorHyeonju Son-
dc.contributor.googleauthorSoonmyung Paik-
dc.contributor.googleauthorSangwoo Kim-
dc.identifier.doi10.1093/bioinformatics/btx719-
dc.contributor.localIdA00524-
dc.contributor.localIdA01823-
dc.contributor.localIdA03275-
dc.relation.journalcodeJ00299-
dc.identifier.eissn1367-4811-
dc.identifier.pmid29126106-
dc.identifier.urlhttps://academic.oup.com/bioinformatics/article/34/7/1232/4596945-
dc.contributor.alternativeNameKim, Sang Woo-
dc.contributor.alternativeNamePaik, Soon Myung-
dc.contributor.alternativeNameLee, Hanna-
dc.contributor.affiliatedAuthorKim, Sang Woo-
dc.contributor.affiliatedAuthorPaik, Soon Myung-
dc.contributor.affiliatedAuthorLee, Hanna-
dc.citation.volume34-
dc.citation.number7-
dc.citation.startPage1232-
dc.citation.endPage1234-
dc.identifier.bibliographicCitationBIOINFORMATICS, Vol.34(7) : 1232-1234, 2018-
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers

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