158 190

Cited 2 times in

Comprehensive benchmarking and guidelines of mosaic variant calling strategies

DC Field Value Language
dc.contributor.author김상우-
dc.date.accessioned2024-01-16T01:39:14Z-
dc.date.available2024-01-16T01:39:14Z-
dc.date.issued2023-12-
dc.identifier.issn1548-7091-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/197714-
dc.description.abstractRapid advances in sequencing and analysis technologies have enabled the accurate detection of diverse forms of genomic variants represented as heterozygous, homozygous and mosaic mutations. However, the best practices for mosaic variant calling remain disorganized owing to the technical and conceptual difficulties faced in evaluation. Here we present our benchmark of 11 feasible mosaic variant detection approaches based on a systematically designed whole-exome-level reference standard that mimics mosaic samples, supported by 354,258 control positive mosaic single-nucleotide variants and insertion-deletion mutations and 33,111,725 control negatives. We identified not only the best practice for mosaic variant detection but also the condition-dependent strengths and weaknesses of the current methods. Furthermore, feature-level evaluation and their combinatorial usage across multiple algorithms direct the way for immediate to prolonged improvements in mosaic variant detection. Our results will guide researchers in selecting suitable calling algorithms and suggest future strategies for developers.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherNature Pub. Group-
dc.relation.isPartOfNATURE METHODS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAlgorithms-
dc.subject.MESHBenchmarking*-
dc.subject.MESHHigh-Throughput Nucleotide Sequencing* / methods-
dc.subject.MESHHumans-
dc.subject.MESHMutation-
dc.subject.MESHPolymorphism, Single Nucleotide-
dc.subject.MESHSoftware-
dc.titleComprehensive benchmarking and guidelines of mosaic variant calling strategies-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Biomedical Systems Informatics (의생명시스템정보학교실)-
dc.contributor.googleauthorYoo-Jin Ha-
dc.contributor.googleauthorSeungseok Kang-
dc.contributor.googleauthorJisoo Kim-
dc.contributor.googleauthorJunhan Kim-
dc.contributor.googleauthorSe-Young Jo-
dc.contributor.googleauthorSangwoo Kim-
dc.identifier.doi10.1038/s41592-023-02043-2-
dc.contributor.localIdA00524-
dc.contributor.localIdA00969-
dc.relation.journalcodeJ02297-
dc.identifier.eissn1548-7105-
dc.identifier.pmid37828153-
dc.contributor.alternativeNameKim, Sang Woo-
dc.contributor.affiliatedAuthor김상우-
dc.citation.volume20-
dc.citation.number12-
dc.citation.startPage2058-
dc.citation.endPage2067-
dc.identifier.bibliographicCitationNATURE METHODS, Vol.20(12) : 2058-2067, 2023-12-
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.