A universal SNP and small-indel variant caller using deep neural networks
DeepVariant uses convolutional neural networks to improve the accuracy of variant calling. 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 d...
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| Published in | Nature biotechnology Vol. 36; no. 10; pp. 983 - 987 |
|---|---|
| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer New York
01.11.2018
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1087-0156 1546-1696 1546-1696 |
| DOI | 10.1038/nbt.4235 |
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| Abstract | DeepVariant uses convolutional neural networks to improve the accuracy of variant calling.
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. |
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| AbstractList | DeepVariant uses convolutional neural networks to improve the accuracy of variant calling.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. 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. 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.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. DeepVariant uses convolutional neural networks to improve the accuracy of variant calling. 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. |
| Audience | Academic |
| Author | Schwartz, Scott Newburger, Dan McLean, Cory Y Afshar, Pegah T Dijamco, Jojo Colthurst, Thomas Alexander, David Dorfman, Lizzie Nguyen, Nam Chang, Pi-Chuan DePristo, Mark A Ku, Alexander Gross, Sam S Poplin, Ryan |
| Author_xml | – sequence: 1 givenname: Ryan surname: Poplin fullname: Poplin, Ryan organization: Verily Life Sciences, Google Inc – sequence: 2 givenname: Pi-Chuan surname: Chang fullname: Chang, Pi-Chuan organization: Google Inc – sequence: 3 givenname: David surname: Alexander fullname: Alexander, David organization: Google Inc – sequence: 4 givenname: Scott surname: Schwartz fullname: Schwartz, Scott organization: Google Inc – sequence: 5 givenname: Thomas surname: Colthurst fullname: Colthurst, Thomas organization: Google Inc – sequence: 6 givenname: Alexander surname: Ku fullname: Ku, Alexander organization: Google Inc – sequence: 7 givenname: Dan surname: Newburger fullname: Newburger, Dan organization: Verily Life Sciences – sequence: 8 givenname: Jojo surname: Dijamco fullname: Dijamco, Jojo organization: Verily Life Sciences – sequence: 9 givenname: Nam surname: Nguyen fullname: Nguyen, Nam organization: Verily Life Sciences – sequence: 10 givenname: Pegah T surname: Afshar fullname: Afshar, Pegah T organization: Verily Life Sciences – sequence: 11 givenname: Sam S surname: Gross fullname: Gross, Sam S organization: Verily Life Sciences – sequence: 12 givenname: Lizzie surname: Dorfman fullname: Dorfman, Lizzie organization: Verily Life Sciences, Google Inc – sequence: 13 givenname: Cory Y surname: McLean fullname: McLean, Cory Y organization: Verily Life Sciences, Google Inc – sequence: 14 givenname: Mark A surname: DePristo fullname: DePristo, Mark A email: mdepristo@google.com organization: Verily Life Sciences, Google Inc |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30247488$$D View this record in MEDLINE/PubMed |
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| Snippet | DeepVariant uses convolutional neural networks to improve the accuracy of variant calling.
Despite rapid advances in sequencing technologies, accurately... Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful... DeepVariant uses convolutional neural networks to improve the accuracy of variant calling.Despite rapid advances in sequencing technologies, accurately calling... |
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| Title | A universal SNP and small-indel variant caller using deep neural networks |
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