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Accurate detection of mosaic variants in sequencing data without matched controls.

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.

SUBMITTER: Dou Y

PROVIDER: S-EPMC7065972 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Accurate detection of mosaic variants in sequencing data without matched controls.

Dou Yanmei Y Kwon Minseok M Rodin Rachel E RE Cortés-Ciriano Isidro I Doan Ryan R Luquette Lovelace J LJ Galor Alon A Bohrson Craig C Walsh Christopher A CA Park Peter J PJ

Nature biotechnology 20200106 3

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 algo ...[more]

PMID: 31907404

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