한빛사논문
- 조회3,846
Junho Kim1, Dachan Kim1, Jae Seok Lim2, Ju Heon Maeng1, Hyeonju Son1, Hoon-Chul Kang3, Hojung Nam4, Jeong Ho Lee2,* & Sangwoo Kim1,*
1 Department of Biomedical Systems Informatics and Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, South Korea. 2 Graduate School of Medical Science and Engineering, KAIST, Daejeon 34141, South Korea. 3 Department of Pediatrics, Division of Pediatric Neurology, Pediatric Epilepsy Clinics, Severance Children’s Hospital, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul 03722, South Korea. 4 School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, South Korea.
*Correspondence and requests for materials should be addressed to J.H.L. or to S.K.
Abstract
Accurate genome-wide detection of somatic mutations with low variant allele frequency (VAF, <1%) has proven difficult, for which generalized, scalable methods are lacking. Herein, we describe a new computational method, called RePlow, that we developed to detect low-VAF somatic mutations based on simple, library-level replicates for next-generation sequencing on any platform. Through joint analysis of replicates, RePlow is able to remove prevailing background errors in next-generation sequencing analysis, facilitating remarkable improvement in the detection accuracy for low-VAF somatic mutations (up to ~99% reduction in false positives). The method is validated in independent cancer panel and brain tissue sequencing data. Our study suggests a new paradigm with which to exploit an overwhelming abundance of sequencing data for accurate variant detection.
논문정보
TOP52019년 선정
- Research article
- 2019년 03월 (BRIC 등록일 2019-03-12)
- 국내 연구진
- 의약학 > 유전 및 유전체의학
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