NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

ABSTRACT:

Motivation

Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, particularly for the many SVs first detected with long-read sequencing, will improve our understanding of genetic variation.

Results

NPSV-deep is a deep learning-based approach for genotyping previously reported insertion and deletion SVs that recasts this task as an image similarity problem. NPSV-deep predicts the SV genotype based on the similarity between pileup images generated from the actual SRS data and matching SRS simulations. We show that NPSV-deep consistently matches or improves upon the state-of-the-art for SV genotyping

SUBMITTER: Linderman MD 

PROVIDER: S-EPMC10955255 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature