dv-trio: a family-based variant calling pipeline using DeepVariant
Abstract Motivation In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outper...
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Published in | Bioinformatics Vol. 36; no. 11; pp. 3549 - 3551 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
01.06.2020
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Online Access | Get full text |
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Summary: | Abstract
Motivation
In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery.
Results
To further improve DeepVariant’s variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant.
Availability and implementation
dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/).
Contact
e.giannoulatou@victorchang.edu.au
Supplementary information
Supplementary data are available at Bioinformatics online. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1367-4803 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btaa116 |