@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}
}