Format

Send to

Choose Destination
iScience. 2019 Aug 30;18:28-36. doi: 10.1016/j.isci.2019.07.032. Epub 2019 Jul 23.

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

Author information

1
Population Health and Reproduction, University of California, Davis, USA. Electronic address: daniel.standage@nbacc.dhs.gov.
2
Population Health and Reproduction, University of California, Davis, USA; Genome Center, University of California, Davis, USA. Electronic address: ctbrown@ucdavis.edu.
3
Genome Center, University of California, Davis, USA; MIND Institute, University of California, Davis, USA; Biochemistry and Molecular Medicine, University of California, Davis, 1 Shields Avenue, Davis, CA 95616, USA. Electronic address: fhormozd@ucdavis.edu.

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.

KEYWORDS:

Bioinformatics; Biological Sciences; Genetics

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center