Duphold: scalable, depth-based annotation and curation of high-confidence structural variant calls
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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.
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Now published in GigaScience doi: 10.1093/gigascience/giz040
Brent S. Pedersen 1Department of Human Genetics, University of Utah. Salt Lake City, UT3USTAR Center for Genetic Discovery, University of Utah. Salt Lake City, UTFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Brent S. PedersenAaron R. Quinlan 1Department of Human Genetics, University of Utah. Salt Lake City, UT2Department of Biomedical Informatics, University of Utah. Salt Lake City, UT3USTAR Center for Genetic Discovery, University of Utah. Salt Lake City, UTFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Aaron R. Quinlan
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascie…
Now published in GigaScience doi: 10.1093/gigascience/giz040
Brent S. Pedersen 1Department of Human Genetics, University of Utah. Salt Lake City, UT3USTAR Center for Genetic Discovery, University of Utah. Salt Lake City, UTFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Brent S. PedersenAaron R. Quinlan 1Department of Human Genetics, University of Utah. Salt Lake City, UT2Department of Biomedical Informatics, University of Utah. Salt Lake City, UT3USTAR Center for Genetic Discovery, University of Utah. Salt Lake City, UTFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Aaron R. Quinlan
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giz040 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.101641 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.101642
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