DeepSom: a CNN-based approach to somatic variant calling in WGS samples without a matched normal

Abstract

Motivation: Somatic mutations are usually called by analyzing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole-genome sequencing (WGS) samples.

Results: We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism and short insertion and deletion variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on five different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling.

Availability and implementation: DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom.

Supplementary information: Supplementary data are available at Bioinformatics online.