SENSV: Detecting Structural Variations with Precise Breakpoints using Low-Depth WGS Data from a Single Oxford Nanopore MinION Flowcell

Abstract

Structural variation (SV) is a major cause of genetic disorders. In this paper, we show that low-depth (specifically, 4x) whole-genome sequencing using a single Oxford Nanopore MinION flow cell suffices to support sensitive detection of SV, in particular, pathogenic SV for supporting clinical diagnosis. Existing SV calling software, when using 4x ONT WGS data, often fails to detect pathogenic SV especially in the form of long deletion, terminal deletion, duplication, and unbalanced translocation. Our new SV calling software SENSV is able to achieve high sensitivity for all types of SV and a breakpoint precision typically {+/-}100 bp, both features are important for clinical concerns. The improvement achieved by SENSV stems from several new algorithms. We evaluated SENSV and other software using both real and simulated data. The former was based on 24 patient samples, each diagnosed with a genetic disorder. SENSV found the pathogenic SV in 22 out of 24 cases (all heterozygous, size from hundreds of Kbp to a few Mbp), reporting breakpoints within 100 bp of the true answers. No existing software can detect the pathogenic SV in more than 10 out of 24 cases, even when the breakpoint requirement is relaxed to {+/-}2,000 bp.