Accurate genotyping across variant classes and lengths using variant graphs.
Identifieur interne : 000A76 ( Main/Exploration ); précédent : 000A75; suivant : 000A77Accurate genotyping across variant classes and lengths using variant graphs.
Auteurs : Jonas Andreas Sibbesen [Danemark] ; Lasse Maretty [Danemark] ; Anders Krogh [Danemark]Source :
- Nature genetics [ 1546-1718 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
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
Affiliations:
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wicri:level="3"><nlm:affiliation>The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark.</nlm:affiliation><country xml:lang="fr">Danemark</country><wicri:regionArea>The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen</wicri:regionArea><placeName><settlement type="city">Copenhague</settlement><region type="région" nuts="2">Hovedstaden</region></placeName></affiliation></author><author><name sortKey="Krogh, Anders" sort="Krogh, Anders" uniqKey="Krogh A" first="Anders" last="Krogh">Anders Krogh</name><affiliation wicri:level="3"><nlm:affiliation>The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark. krogh@binf.ku.dk.</nlm:affiliation><country xml:lang="fr">Danemark</country><wicri:regionArea>The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen</wicri:regionArea><placeName><settlement type="city">Copenhague</settlement><region type="région" 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qualifier="génétique" xml:lang="fr"><term>Génome humain</term><term>Variation génétique</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>High-Throughput Nucleotide Sequencing</term><term>Sequence Analysis, DNA</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Genotype</term><term>Humans</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de séquence d'ADN</term><term>Génotype</term><term>Humains</term><term>Séquençage nucléotidique à haut débit</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>Danemark</li></country><region><li>Hovedstaden</li></region><settlement><li>Copenhague</li></settlement></list><tree><country name="Danemark"><region name="Hovedstaden"><name sortKey="Sibbesen, Jonas Andreas" sort="Sibbesen, Jonas Andreas" uniqKey="Sibbesen J" first="Jonas Andreas" last="Sibbesen">Jonas Andreas Sibbesen</name></region><name sortKey="Krogh, Anders" sort="Krogh, Anders" uniqKey="Krogh A" first="Anders" last="Krogh">Anders Krogh</name><name sortKey="Maretty, Lasse" sort="Maretty, Lasse" uniqKey="Maretty L" first="Lasse" last="Maretty">Lasse Maretty</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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