duphold: scalalable, depth-based annotation and curation of high-confidence structural variant calls.

Most structural variant detection tools 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, as a new method to efficiently annotate structural variant calls with sequence depth information that can add (or remove) confidence to SV predicted to affect copy number. It 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 breakpoints. 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 deletion calls and that its annotations match visual inspection. Duphold can annotate structural variant predictions made from both short-read and long-read data. It is available under the MIT license at: https://github.com/brentp/duphold.