Dataset Information

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.

SUBMITTER: Pedersen BS

PROVIDER: S-EPMC6479422 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Pedersen Brent S BS Quinlan Aaron R AR

GigaScience 20190401 4

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 a ...[more]

PMID: 31222198

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Project description:The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives.We first used pedigree analysis to develop a set of high-confidence breakpoint-resolved large deletions. We then used svclassify to cluster and classify these deletions as well as a set of high-confidence deletions from the 1000 Genomes Project and a set of breakpoint-resolved complex insertions from Spiral Genetics. We find that likely SVs cluster separately from likely non-SVs based on our annotations, and that the SVs cluster into different types of deletions. We then developed a supervised one-class classification method that uses a training set of random non-SV regions to determine whether candidate SVs have abnormal annotations different from most of the genome. To test this classification method, we use our pedigree-based breakpoint-resolved SVs, SVs validated by the 1000 Genomes Project, and assembly-based breakpoint-resolved insertions, along with semi-automated visualization using svviz.We find that candidate SVs with high scores from multiple technologies have high concordance with PCR validation and an orthogonal consensus method MetaSV (99.7 % concordant), and candidate SVs with low scores are questionable. We distribute a set of 2676 high-confidence deletions and 68 high-confidence insertions with high svclassify scores from these call sets for benchmarking SV callers. We expect these methods to be particularly useful for establishing high-confidence SV calls for benchmark samples that have been characterized by multiple technologies.

Project description:BackgroundBrucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of four Brucella genomes have been published, and various Brucella gene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitate Brucella research. Brucella genome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-based Brucella gene annotation would increase understanding of complicated Brucella pathogenesis mechanisms.ResultsThe Brucella Bioinformatics Portal (BBP) is developed to integrate existing Brucella genome data and analysis tools with literature mining and curation. The BBP InterBru database and Brucella Genome Browser allow users to search and analyze genes of 4 currently available Brucella genomes and link to more than 20 existing databases and analysis programs. Brucella literature publications in PubMed are extracted and can be searched by a TextPresso-powered natural language processing method, a MeSH browser, a keywords search, and an automatic literature update service. To efficiently annotate Brucella genes using the large amount of literature publications, a literature mining and curation system coined Limix is developed to integrate computational literature mining methods with a PubSearch-powered manual curation and management system. The Limix system is used to quickly find and confirm 107 Brucella gene mutations including 75 genes shown to be essential for Brucella virulence. The 75 genes are further clustered using COG. In addition, 62 Brucella genetic interactions are extracted from literature publications. These results make possible more comprehensive investigation of Brucella pathogenesis. Other BBP features include publication email alert service, Brucella researchers' contact database, and discussion forum.ConclusionBBP is a gateway for Brucella researchers to search, analyze, and curate Brucella genome data originated from public databases and literature. Brucella gene mutations and genetic interactions are annotated using Limix leading to better understanding of Brucella pathogenesis.

Dataset Information

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

Publications

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

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