Name
Title | ||
|---|---|---|
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 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/BIB/BBAC301 | Briefings in Bioinformatics |
Keywords | DocType | Volume |
Mendelian inheritance,deep neural networks,family trios,nanopore long-read,variant calling | Journal | 23 |
Issue | ISSN | Citations |
5 | 1477-4054 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhao Su | 1 | 0 | 0.34 |
| Zhenxian Zheng | 2 | 0 | 0.34 |
| Syed Shakeel Ahmed | 3 | 0 | 0.34 |
| Tak-Wah Lam | 4 | 1860 | 164.96 |
| Ruibang Luo | 5 | 113 | 9.92 |