口译(哲学)
工作流程
医学遗传学
计算生物学
一致性(知识库)
一致性
计算机科学
基因组学
空(SQL)
可解释性
生物信息学
人工智能
基因组
自然语言处理
遗传学
数据挖掘
生物
基因
程序设计语言
数据库
作者
Jiale Xiang,Jiguang Peng,Zhiyu Peng
摘要
Abstract Null variants are prevalent within human genome, and their accurate interpretation is critical for clinical management. In 2018, the ClinGen Sequence Variant Interpretation (SVI) Working Group refined the only criterion (PVS1) for pathogenicity in the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines. The refinement may improve interpretation consistency, but it also brings hurdles to biocurators because of the complicated workflows and multiple bioinformatics sources required. To address these issues, we developed an automatic classification tool called AutoPVS1 to streamline PVS1 interpretation. We assessed the performance of AutoPVS1 using 56 variants manually curated by ClinGen’s SVI Working Group and achieved an interpretation concordance of 95% (53/56). A further analysis of 28,586 putative loss-of-function variants by AutoPVS1 demonstrated that at least 27.6% of them do not reach a very strong strength level, with 17.4% based on variant-specific issues and 10.2% on disease mechanism considerations. Moreover, 40.7% (1,918/4,717) of splicing variants were assigned a decreased PVS1 strength level, significantly higher than frameshift and nonsense variants. Our results reinforce the necessity of considering variant-specific issues and disease mechanisms in variant interpretation, and demonstrate that AutoPVS1 is an accurate, reproducible, and reliable tool which facilitates PVS1 interpretation and will thus be of great importance to curators.
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