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Article: Clair3-Trio: high-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks
Title | Clair3-Trio: high-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks |
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Authors | |
Issue Date | 2022 |
Citation | Briefing in Bioinformatics, 2022 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/316297 |
DC Field | Value | Language |
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dc.contributor.author | SU, J | - |
dc.contributor.author | ZHENG, Z | - |
dc.contributor.author | Ahmed, SS | - |
dc.contributor.author | Lam, TW | - |
dc.contributor.author | Luo, R | - |
dc.date.accessioned | 2022-09-02T06:09:00Z | - |
dc.date.available | 2022-09-02T06:09:00Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Briefing in Bioinformatics, 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10722/316297 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | Briefing in Bioinformatics | - |
dc.title | Clair3-Trio: high-performance Nanopore long-read variant calling in family trios with Trio-to-Trio deep neural networks | - |
dc.type | Article | - |
dc.identifier.email | Ahmed, SS: ssahmed@hku.hk | - |
dc.identifier.email | Lam, TW: twlam@cs.hku.hk | - |
dc.identifier.email | Luo, R: rbluo@cs.hku.hk | - |
dc.identifier.authority | Lam, TW=rp00135 | - |
dc.identifier.authority | Luo, R=rp02360 | - |
dc.identifier.hkuros | 336224 | - |