Accurate genotyping across variant classes and lengths using variant graphs.
Identifieur interne : 000863 ( PubMed/Corpus ); précédent : 000862; suivant : 000864Accurate genotyping across variant classes and lengths using variant graphs.
Auteurs : Jonas Andreas Sibbesen ; Lasse Maretty ; Anders KroghSource :
- Nature genetics [ 1546-1718 ] ; 2018.
English descriptors
- KwdEn :
- MESH :
- genetics : Genetic Variation, Genome, Human.
- methods : High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA.
- Genotype, Humans.
Abstract
Genotype estimates from short-read sequencing data are typically based on the alignment of reads to a linear reference, but reads originating from more complex variants (for example, structural variants) often align poorly, resulting in biased genotype estimates. This bias can be mitigated by first collecting a set of candidate variants across discovery methods, individuals and databases, and then realigning the reads to the variants and reference simultaneously. However, this realignment problem has proved computationally difficult. Here, we present a new method (BayesTyper) that uses exact alignment of read k-mers to a graph representation of the reference and variants to efficiently perform unbiased, probabilistic genotyping across the variation spectrum. We demonstrate that BayesTyper generally provides superior variant sensitivity and genotyping accuracy relative to existing methods when used to integrate variants across discovery approaches and individuals. Finally, we demonstrate that including a 'variation-prior' database containing already known variants significantly improves sensitivity.
DOI: 10.1038/s41588-018-0145-5
PubMed: 29915429
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This bias can be mitigated by first collecting a set of candidate variants across discovery methods, individuals and databases, and then realigning the reads to the variants and reference simultaneously. However, this realignment problem has proved computationally difficult. Here, we present a new method (BayesTyper) that uses exact alignment of read k-mers to a graph representation of the reference and variants to efficiently perform unbiased, probabilistic genotyping across the variation spectrum. We demonstrate that BayesTyper generally provides superior variant sensitivity and genotyping accuracy relative to existing methods when used to integrate variants across discovery approaches and individuals. Finally, we demonstrate that including a 'variation-prior' database containing already known variants significantly improves sensitivity.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29915429</PMID><DateCompleted><Year>2019</Year><Month>04</Month><Day>24</Day></DateCompleted><DateRevised><Year>2019</Year><Month>05</Month><Day>28</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1546-1718</ISSN><JournalIssue CitedMedium="Internet"><Volume>50</Volume><Issue>7</Issue><PubDate><Year>2018</Year><Month>07</Month></PubDate></JournalIssue><Title>Nature genetics</Title><ISOAbbreviation>Nat. 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Here, we present a new method (BayesTyper) that uses exact alignment of read k-mers to a graph representation of the reference and variants to efficiently perform unbiased, probabilistic genotyping across the variation spectrum. We demonstrate that BayesTyper generally provides superior variant sensitivity and genotyping accuracy relative to existing methods when used to integrate variants across discovery approaches and individuals. 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