Main menu

Clair: exploring the limit of using a deep neural network on pileup data for germline variant calling


Single-molecule sequencing technologies have emerged in recent years and revolutionized structural variant calling, complex genome assembly, and epigenetic mark detection. However, the lack of a highly accurate small variant caller has limited the new technologies from being more widely used.

In this study, we present Clair, the successor to Clairvoyante, a program for fast and accurate germline small variant calling, using single molecule sequencing data.

For ONT data, Clair achieves the best precision, recall and speed as compared to several competing programs, including Clairvoyante, Longshot and Medaka. Through studying the missed variants and benchmarking intentionally overfitted models, we found that Clair may be approaching the limit of possible accuracy for germline small variant calling using pileup data and deep neural networks.

Clair requires only a conventional CPU for variant calling and is an open source project available at: https://github.com/HKU-BAL/Clair

Authors: Ruibang Luo, Chak-Lim Wong, Yat-Sing Wong, Chi-Ian Tang, Chi-Man Liu, Chi-Ming Leung, Tak-Wah Lam

入门指南

购买 MinION 启动包 Nanopore 商城 测序服务提供商 全球代理商

纳米孔技术

订阅 Nanopore 更新 资源库及发表刊物 什么是 Nanopore 社区

关于 Oxford Nanopore

新闻 公司历程 可持续发展 领导团队 媒体资源和联系方式 投资者 合作者 在 Oxford Nanopore 工作 职位空缺 商业信息 BSI 27001 accreditationBSI 90001 accreditationBSI mark of trust
Chinese flag