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

IScience
Daniel S StandageFereydoun Hormozdiari

Abstract

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.

Citations

Sep 29, 2020·Frontiers in Microbiology·Ying WangFengzhu Sun
Jan 28, 2021·Nucleic Acids Research·Parsoa Khorsand, Fereydoun Hormozdiari
Mar 15, 2021·Human Mutation·Lauris KaplinskiMaido Remm
Apr 4, 2021·Algorithms for Molecular Biology : AMB·Jens Zentgraf, Sven Rahmann
Jun 23, 2021·Algorithms for Molecular Biology : AMB·Amatur RahmanPaul Medvedev
Oct 21, 2021·Genome Research·Kristoffer Sahlin

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Methods Mentioned

BETA
genotyping
whole-genome shotgun sequencing

Software Mentioned

NovoBreak
db
MALVA
TrioDenovo
VarGeno
Kevlar
denovo
PhaseByTransmission
Kallisto
LAVA

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