Svirlpool: structural variant detection from long read sequencing by local assembly

MotivationLong-Read Sequencing (LRS) promises great improvements in the detection of structural genome variants (SVs). However, existing methods are lacking in key areas such as the reliable detection of inserted sequence, precise genotyping of variants, and reproducible calling of variants across multiple samples. Here, we present our method Svirlpool, that is aimed at the analysis of Oxford Nanopore Technologies (ONT) sequencing data. Svirlpool uses local assembly of candidate SV regions to obtain high-quality consensus sequences. ResultsSvirlpool obtains competitive results to the leading method Sniffles on the widely used Genome in a Bottle benchmark data sets. On trio data, however, Svirlpools shows a clear favorable performance in terms of mendelian consistency. This indicates that Svirlpool shows great promise in clinical applications and beyond benchmark datasets. Availability and ImplementationSource code, container images, and documentation available at https://github.com/bihealth/svirlpool