NPSV: A simulation-driven approach to genotyping structural variants in whole-genome sequencing data.

ABSTRACT:

Background

Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases.

Results

We introduce NPSV, a machine learning-based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistentl

SUBMITTER: Linderman MD 

PROVIDER: S-EPMC8246072 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature