Deep convolutional neural networks for accurate somatic mutation detection

Sayed Mohammad Ebrahim Sahraeian, Ruolin Liu, Bayo Lau, Karl Podesta, Marghoob Mohiyuddin and Hugo Y. K. Lam ()
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Sayed Mohammad Ebrahim Sahraeian: Roche Sequencing Solutions
Ruolin Liu: Roche Sequencing Solutions
Bayo Lau: Roche Sequencing Solutions
Karl Podesta: Microsoft Azure
Marghoob Mohiyuddin: Roche Sequencing Solutions
Hugo Y. K. Lam: Roche Sequencing Solutions

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-09027-x

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