Krause Jeremias, Classen Carlos, Dey Daniela, Lausberg Eva, Kessler Luise, Eggermann Thomas, Kurth Ingo, Begemann Matthias, Kraft Florian
Medical Faculty, Institute for Human Genetics and Genomic Medicine, Uniklinik RWTH Aachen, Pauwelsstrasse 30, 52074, Aachen, North-Rhine-Westphalia, Germany.
BMC Bioinformatics. 2024 Dec 17;25(1):376. doi: 10.1186/s12859-024-06010-2.
Methods to call, analyze and visualize copy number variations (CNVs) from massive parallel sequencing data have been widely adopted in clinical practice and genetic research. To enable a streamlined analysis of CNV data, comprehensive annotations and good visualizations are indispensable. The ability to detect single exon CNVs is another important feature for genetic testing. Nonetheless, most available open-source tools come with limitations in at least one of these areas. One additional drawback is that available tools deliver data in an unstructured and static format which requires subsequent visualization and formatting efforts.
Here we present CNVizard, an interactive Streamlit app allowing a comprehensive visualization of CNVkit data. Furthermore, combining CNVizard with the CNVand pipeline allows the annotation and visualization of CNV or SV VCF files from any CNV caller.
CNVizard, in combination with CNVand, enables the comprehensive and streamlined analysis of short- and long-read sequencing data and provide an intuitive webapp-like experience enabling an interactive visualization of CNV data.
从大规模平行测序数据中调用、分析和可视化拷贝数变异（CNV）的方法已在临床实践和基因研究中广泛应用。为了实现对CNV数据的简化分析，全面的注释和良好的可视化是必不可少的。检测单个外显子CNV的能力是基因检测的另一个重要特征。然而，大多数现有的开源工具在这些领域中至少存在一个局限性。另一个缺点是，现有工具以非结构化和静态格式提供数据，这需要后续的可视化和格式化工作。
我们在此展示CNVizard，这是一个交互式的Streamlit应用程序，可对CNVkit数据进行全面可视化。此外，将CNVizard与CNVand管道相结合，可以对来自任何CNV调用程序的CNV或SV VCF文件进行注释和可视化。
CNVizard与CNVand相结合，能够对短读长和长读长测序数据进行全面且简化的分析，并提供类似直观网络应用程序的体验，实现CNV数据的交互式可视化。
