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SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

ABSTRACT: Motivation:Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease. Results:Here, we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations. Availability and implementation:SV2 is freely available on GitHub (https://github.com/dantaki/SV2). Contact:jsebat@ucsd.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Antaki D

PROVIDER: S-EPMC5946924 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

Antaki Danny D Brandler William M WM Sebat Jonathan J

Bioinformatics (Oxford, England) 20180501 10

<h4>Motivation</h4>Structural variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease.<h4>Results</h4>Here, we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability ...[more]

PMID: 29300834

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Project description:BackgroundPrevious studies exploring sequence variation in the model legume, Medicago truncatula, relied on mapping short reads to a single reference. However, read-mapping approaches are inadequate to examine large, diverse gene families or to probe variation in repeat-rich or highly divergent genome regions. De novo sequencing and assembly of M. truncatula genomes enables near-comprehensive discovery of structural variants (SVs), analysis of rapidly evolving gene families, and ultimately, construction of a pan-genome.ResultsGenome-wide synteny based on 15 de novo M. truncatula assemblies effectively detected different types of SVs indicating that as much as 22% of the genome is involved in large structural changes, altogether affecting 28% of gene models. A total of 63 million base pairs (Mbp) of novel sequence was discovered, expanding the reference genome space for Medicago by 16%. Pan-genome analysis revealed that 42% (180 Mbp) of genomic sequences is missing in one or more accession, while examination of de novo annotated genes identified 67% (50,700) of all ortholog groups as dispensable - estimates comparable to recent studies in rice, maize and soybean. Rapidly evolving gene families typically associated with biotic interactions and stress response were found to be enriched in the accession-specific gene pool. The nucleotide-binding site leucine-rich repeat (NBS-LRR) family, in particular, harbors the highest level of nucleotide diversity, large effect single nucleotide change, protein diversity, and presence/absence variation. However, the leucine-rich repeat (LRR) and heat shock gene families are disproportionately affected by large effect single nucleotide changes and even higher levels of copy number variation.ConclusionsAnalysis of multiple M. truncatula genomes illustrates the value of de novo assemblies to discover and describe structural variation, something that is often under-estimated when using read-mapping approaches. Comparisons among the de novo assemblies also indicate that different large gene families differ in the architecture of their structural variation.

Dataset Information

SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

Publications

SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

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