Journal
NATURE BIOTECHNOLOGY
Volume 38, Issue 3, Pages 314-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41587-019-0368-8
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Funding
- National Institutes of Health [U01MH106883, R01NS032457, T32HG002295, T32GM007753]
- Harvard Ludwig Center
- Horizon 2020 grant [703543]
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MosaicForecast detects mosaic single-nucleotide variants and indels in human samples. 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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