Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV’s performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https://github.com/Genotek/ClassifyCNV.

拷贝数变异（CNV）是人类遗传变异的重要组成部分。它们可能是良性的，也可能通过造成剂量失衡并破坏基因和调控元件而在人类疾病中发挥作用。CNV的准确识别和临床注释是必不可少的，但是，临床医生对单个CNV进行人工评估在很大程度上具有挑战性。在这里，我们介绍了ClassifyCNV，这是一种易于使用的工具，可实施2019 ACMG分类指南以评估CNV的致病性。ClassifyCNV使用基因组坐标和CNV类型作为输入，并报告每个变体的临床分类，分类评分细分以及对变体解释潜在重要的基因列表。我们使用一组已知的临床CNV和一组手动评估的变体来验证ClassifyCNV的性能。ClassifyCNV将81％的手动评估变体的致病性类别与自动确定为不确定的剩余病原性和良性变体的重要性相匹配，需要临床医生进行进一步评估。ClassifyCNV有助于在高通量CNV分析中实施最新的ACMG指南，适合集成到NGS分析管道中，并可以缩短诊断时间。该工具位于https://github.com/Genotek/ClassifyCNV。适用于集成到NGS分析管道中，并可以减少诊断时间。该工具位于https://github.com/Genotek/ClassifyCNV。适用于集成到NGS分析管道中，并可以减少诊断时间。该工具位于https://github.com/Genotek/ClassifyCNV。