AI Article Synopsis
- The text discusses the importance of effective methods for analyzing and visualizing copy number variations (CNVs) in genetic research and clinical practice.
- It introduces CNVizard, a Streamlit app designed to enhance the visualization of CNVkit data, and highlights its integration with the CNVand pipeline for better annotation of CNV or SV VCF files.
- The combination of CNVizard and CNVand facilitates a more comprehensive and user-friendly approach to analyzing sequencing data, making it easier for users to visualize CNV data interactively.
Article Abstract
Background: 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.
Results: 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.
Conclusion: 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.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11650836 | PMC |
| http://dx.doi.org/10.1186/s12859-024-06010-2 | DOI Listing |
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