Duphold: scalable, depth-based annotation and curation of high-confidence structural variant calls.

Abstract

Most structural variant (SV) detection methods use clusters of discordant read-pair and split-read alignments to identify variants yet do not integrate depth of sequence coverage as an additional means to support or refute putative events. Here, we present "duphold," a new method to efficiently annotate SV calls with sequence depth information that can add (or remove) confidence to SVs that are predicted to affect copy number. Duphold indicates not only the change in depth across the event but also the presence of a rapid change in depth relative to the regions surrounding the break-points. It uses a unique algorithm that allows the run time to be nearly independent of the number of variants. This performance is important for large, jointly called projects with many samples, each of which must be evaluated at thousands of sites. We show that filtering on duphold annotations can greatly improve the specificity of SV calls. Duphold can annotate SV predictions made from both short-read and long-read sequencing datasets. It is available under the MIT license at https://github.com/brentp/duphold.