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 inherit

SUBMITTER: Su J 

PROVIDER: S-EPMC9487642 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature