Department of Computer Science, The University of Hong Kong, Hong Kong, China.
Brief Bioinform. 2022 Sep 20;23(5). doi: 10.1093/bib/bbac301.
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.
从家系(child-mother-father 三人组)测序数据中准确识别遗传变异对于基因组学很重要。然而，最先进的方法将三人组的变异调用视为三个独立的任务，这限制了它们对 Nanopore 长读测序数据的调用准确性。为了更好地进行三人组变异调用，我们引入了 Clair3-Trio，这是第一个专门针对 Nanopore 长读测序的家系三人组数据的变异调用程序。 Clair3-Trio 采用了 Trio-to-Trio 深度神经网络模型，允许它输入三人组测序信息，并在单个模型中输出三人组的所有预测变异，以提高变异调用的准确性。我们还提出了 MCVLoss，这是一种专门针对三人组变异调用的新型损失函数，利用 Mendelian 遗传的显式编码。Clair3-Trio 在实验中表现出了全面的改进。它预测的孟德尔遗传违反变异比当前最先进的方法要少得多。我们还证明了我们的 Trio-to-Trio 模型比竞争架构更准确。Clair3-Trio 可在 https://github.com/HKU-BAL/Clair3-Trio 上免费获取，是一个开源项目。
