@article {1479399, title = {Accurate detection of mosaic variants in sequencing data without matched controls}, journal = {Nature Biotechnology }, volume = {38}, number = {3}, year = {2020}, pages = {314-319}, 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{\textendash}90{\%} of the mosaic single-nucleotide variants and 60{\textendash}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. }, author = {Dou, Yanmei and Kwon, Minseok and Rodin, Rachel E. and Cort{\'e}s-Ciriano, Isidro and Doan, Ryan and J. Luquette, Lovelace and Galor, Alon and Bohrson, Craig and Christopher A. Walsh and Park, P J} }