한빛사 논문
Yanmei Dou1, Minseok Kwon1, Rachel E. Rodin2,3,4,5, Isidro Cortés-Ciriano1,8, Ryan Doan2,3,4, Lovelace J. Luquette1,6, Alon Galor1, Craig Bohrson1,6, Christopher A. Walsh2,3,4 and Peter J. Park1,7,*
1 Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. 2 Division of Genetics and Genomics, Manton Center for Orphan Disease, and Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA, USA. 3 Departments of Neurology and Pediatrics, Harvard Medical School, Boston, MA, USA. 4 Broad Institute of MIT and Harvard, Cambridge, MA, USA. 5 Harvard/MIT MD–PhD Program, Harvard Medical School, Boston, MA, USA. 6 Bioinformatics and Integrative Genomics PhD program, Harvard Medical School, Boston, MA, USA. 7 Ludwig Center at Harvard, Boston, MA, USA. 8 Present address: European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
*Correspondence to Peter J. Park
Abstract
Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants and indels, achieving a multifold increase in specificity compared with existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80–90% of the mosaic single-nucleotide variants and 60–80% of indels detected in human brain whole-genome sequencing data. Our method should help elucidate the contribution of mosaic somatic mutations to the origin and development of disease.
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