The detection of copy number variations (CNVs) on whole-exome sequencing (WES) represents a cost-effective technique for the study of genetic variants. This approach, however, has encountered an obstacle with high false-positive rates due to biases from exome sequencing capture kits and GC contents. Although plenty of CNV detection tools have been developed, they do not perform well with all types of CNVs. In addition, most tools lack features of genetic annotation, CNV visualization, and flexible installation, requiring users to put much effort into CNV interpretation. Here, we present “inCNV,” a web-based application that can accept multiple CNV-tool results, then integrate and prioritize them with user-friendly interfaces. This application helps users analyze the importance of called CNVs by generating CNV annotations from Ensembl, Database of Genomic Variants (DGV), ClinVar, and Online Mendelian Inheritance in Man (OMIM). Moreover, users can select and export CNVs of interest including their flanking sequences for primer design and experimental verification. We demonstrated how inCNV could help users filter and narrow down the called CNVs to a potentially novel CNV, a common CNV within a group of samples of the same disease, or a de novo CNV of a sample within the same family. Besides, we have provided in CNV as a docker image for ease of installation (https://github.com/saowwapark/inCNV).

全外显子组测序 (WES) 检测拷贝数变异 (CNV) 是一种经济有效的遗传变异研究技术。然而，由于外显子组测序捕获试剂盒和 GC 内容的偏差，这种方法遇到了假阳性率较高的障碍。尽管已经开发了很多 CNV 检测工具，但它们并不能很好地检测所有类型的 CNV。此外，大多数工具缺乏基因注释、CNV可视化和灵活安装等功能，需要用户在CNV解读上投入大量精力。在这里，我们推出了“inCNV”，这是一个基于 Web 的应用程序，它可以接受多个 CNV 工具结果，然后通过用户友好的界面将它们集成并确定优先级。该应用程序通过从 Ensembl、基因组变异数据库 (DGV)、ClinVar 和在线人类孟德尔遗传 (OMIM) 生成 CNV 注释，帮助用户分析调用的 CNV 的重要性。此外，用户可以选择并导出感兴趣的 CNV，包括其侧翼序列，用于引物设计和实验验证。我们演示了 inCNV 如何帮助用户过滤和缩小所调用的 CNV 范围，使其成为潜在的新颖 CNV、同一疾病的一组样本中的常见 CNV 或同一家族中样本的从头CNV。此外，我们在 CNV 中提供了一个 docker 镜像，以便于安装（https://github.com/saowwapark/inCNV）。