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A universal SNP and small-indel variant caller using deep neural networks

Nature biotechnology, 2018-11, Vol.36 (10), p.983-987 [Peer Reviewed Journal]

COPYRIGHT 2018 Nature Publishing Group ;COPYRIGHT 2018 Nature Publishing Group ;Copyright Nature Publishing Group Oct 2018 ;ISSN: 1087-0156 ;EISSN: 1546-1696 ;DOI: 10.1038/nbt.4235 ;PMID: 30247488

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Title:
A universal SNP and small-indel variant caller using deep neural networks
Author: Poplin, Ryan ; Chang, Pi-Chuan ; Alexander, David ; Schwartz, Scott ; Colthurst, Thomas ; Ku, Alexander ; Newburger, Dan ; Dijamco, Jojo ; Nguyen, Nam ; Afshar, Pegah T ; Gross, Sam S ; Dorfman, Lizzie ; McLean, Cory Y ; DePristo, Mark A
Subjects: Analysis ; Artificial neural networks ; Gene sequencing ; Genetic diversity ; Genetic variance ; Genomes ; Genomic structural variations ; Genomics ; Genotypes ; Neural networks ; Next-generation sequencing ; Nucleotide sequence ; Single-nucleotide polymorphism ; State of the art
Is Part Of: Nature biotechnology, 2018-11, Vol.36 (10), p.983-987
Description: Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.
Publisher: United States: Nature Publishing Group
Language: English
Identifier: ISSN: 1087-0156
EISSN: 1546-1696
DOI: 10.1038/nbt.4235
PMID: 30247488
Source: GFMER Free Medical Journals
ProQuest Central

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A universal SNP and small-indel variant caller using deep neural networks

COPYRIGHT 2018 Nature Publishing Group ;COPYRIGHT 2018 Nature Publishing Group ;Copyright Nature Publishing Group Oct 2018 ;ISSN: 1087-0156 ;EISSN: 1546-1696 ;DOI: 10.1038/nbt.4235 ;PMID: 30247488

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