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

Structural variants (SV) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole genome next-generation sequencing (NGS) data. SV genotypers that assume the aligned sequencing data uniformly reflects the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. <br>We introduce NPSV, a machine learning-based approach for genotyping previously discovered SVs that employs NGS 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 consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples and variant types. NPSV can specifically identify putative <i>de novo</i> SVs in a trio context and is robust to offset SV breakpoints. <br>Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a simulate-able black box NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications.