RT Journal Article
SR Electronic
T1 Creating a universal SNP and small indel variant caller with deep neural networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 092890
DO 10.1101/092890
A1 Poplin, Ryan
A1 Newburger, Dan
A1 Dijamco, Jojo
A1 Nguyen, Nam
A1 Loy, Dion
A1 Gross, Sam
A1 McLean, Cory Y.
A1 DePristo, Mark A.
YR 2016
UL http://biorxiv.org/content/early/2016/12/14/092890.abstract
AB Next-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome1 by calling genetic variants present in an individual using billions of short, errorful sequence reads2. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome3,4. Here we show that a deep convolutional neural network5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.