Clair3-trio: high-performance Nanopore long-read variant calling in family trios with trio-to-trio deep neural networks

Abstract Accurate identification of genetic variants from family child–mother–father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing...

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Veröffentlicht in:Briefings in bioinformatics 2022-09, Vol.23 (5)
Hauptverfasser: Su, Junhao, Zheng, Zhenxian, Ahmed, Syed Shakeel, Lam, Tak-Wah, Luo, Ruibang
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Sprache:eng
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Zusammenfassung:Abstract Accurate identification of genetic variants from family child–mother–father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio’s predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac301