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Deep convolutional neural networks for accurate somatic mutation detection.

ABSTRACT: Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

SUBMITTER: Sahraeian SME

PROVIDER: S-EPMC6399298 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Deep convolutional neural networks for accurate somatic mutation detection.

Sahraeian Sayed Mohammad Ebrahim SME Liu Ruolin R Lau Bayo B Podesta Karl K Mohiyuddin Marghoob M Lam Hugo Y K HYK

Nature communications 20190304 1

Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a sta ...[more]

PMID: 30833567

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