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Creating a universal SNP and small indel variant caller with deep neural networks

Ryan Poplin, Dan Newburger, Jojo Dijamco, Nam Nguyen, Dion Loy, Sam S. Gross, Cory Y. McLean, Mark A. DePristo
doi: https://doi.org/10.1101/092890
Ryan Poplin
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
2Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Dan Newburger
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Jojo Dijamco
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Nam Nguyen
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Dion Loy
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Sam S. Gross
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Cory Y. McLean
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Mark A. DePristo
1Verily Life Sciences, 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
2Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043, (650) 253-0000
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Abstract

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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 21, 2016.
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Creating a universal SNP and small indel variant caller with deep neural networks
Ryan Poplin, Dan Newburger, Jojo Dijamco, Nam Nguyen, Dion Loy, Sam S. Gross, Cory Y. McLean, Mark A. DePristo
bioRxiv 092890; doi: https://doi.org/10.1101/092890
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Creating a universal SNP and small indel variant caller with deep neural networks
Ryan Poplin, Dan Newburger, Jojo Dijamco, Nam Nguyen, Dion Loy, Sam S. Gross, Cory Y. McLean, Mark A. DePristo
bioRxiv 092890; doi: https://doi.org/10.1101/092890

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