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 Deep

SUBMITTER: Vilov S 

PROVIDER: S-EPMC9843587 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature