Accurate detection of mosaic variants in sequencing data without matched controls
Author: ["Yanmei Dou","Minseok Kwon","Rachel E. Rodin","Isidro Cortés-Ciriano","Ryan Doan","Lovelace J. Luquette","Alon Galor","Craig Bohrson","Christopher A. Walsh","Peter J. Park"]
Publication: Nature Biotechnology
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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. MosaicForecast detects mosaic single-nucleotide variants and indels in human samples.
Cite this article
Dou, Y., Kwon, M., Rodin, R.E. et al. Accurate detection of mosaic variants in sequencing data without matched controls. Nat Biotechnol 38, 314–319 (2020). https://doi.org/10.1038/s41587-019-0368-8
