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Sharing genetic variants with the NGS pipeline is essential for effective genomic data sharing and reproducibility in health information exchange

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dc.contributor.author박유랑-
dc.date.accessioned2021-09-29T01:28:41Z-
dc.date.available2021-09-29T01:28:41Z-
dc.date.issued2021-01-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/184381-
dc.description.abstractGenetic variants causing underlying pharmacogenetic and disease phenotypes have been used as the basis for clinical decision-making. However, due to the lack of standards for next-generation sequencing (NGS) pipelines, reproducing genetic variants among institutions is still difficult. The aim of this study is to show how many important variants for clinical decisions can be individually detected using different pipelines. Genetic variants were derived from 105 breast cancer patient target DNA sequences via three different variant-calling pipelines. HaplotypeCaller, Mutect2 tumor-only mode in the Genome Analysis ToolKit (GATK), and VarScan were used in variant calling from the sequence read data processed by the same NGS preprocessing tools using Variant Effect Predictor. GATK HaplotypeCaller, VarScan, and MuTect2 found 25,130, 16,972, and 4232 variants, comprising 1491, 1400, and 321 annotated variants with ClinVar significance, respectively. The average number of ClinVar significant variants in the patients was 769.43, 16.50% of the variants were detected by only one variant caller. Despite variants with significant impact on clinical decision-making, the detected variants are different for each algorithm. To utilize genetic variants in the clinical field, a strict standard for NGS pipelines is essential.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleSharing genetic variants with the NGS pipeline is essential for effective genomic data sharing and reproducibility in health information exchange-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Biomedical Systems Informatics (의생명시스템정보학교실)-
dc.contributor.googleauthorJeong Hoon Lee-
dc.contributor.googleauthorSolbi Kweon-
dc.contributor.googleauthorYu Rang Park-
dc.identifier.doi10.1038/s41598-021-82006-9-
dc.contributor.localIdA05624-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid33500538-
dc.contributor.alternativeNamePark, Yu Rang-
dc.contributor.affiliatedAuthor박유랑-
dc.citation.volume11-
dc.citation.number1-
dc.citation.startPage2268-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.11(1) : 2268, 2021-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers

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