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Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants

Identifieur interne : 000509 ( Main/Exploration ); précédent : 000508; suivant : 000510

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants

Auteurs : Daniel S. Standage [États-Unis] ; C. Titus Brown [États-Unis] ; Fereydoun Hormozdiari [États-Unis]

Source :

RBID : PMC:6682328

Abstract

Summary

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy.


Url:
DOI: 10.1016/j.isci.2019.07.032
PubMed: 31377530
PubMed Central: 6682328


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<italic>De novo</italic>
genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and
<italic>de novo</italic>
variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for
<italic>de novo</italic>
variant prediction. Kevlar is a mapping-free method for
<italic>de novo</italic>
variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance
<italic>k</italic>
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<italic>k</italic>
-mers are partitioned into disjoint sets by shared
<italic>k</italic>
-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both
<italic>de novo</italic>
single-nucleotide variants and indels with high accuracy.</p>
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