CNViz: An R/Shiny Application for Interactive Copy Number Variant Visualization in Cancer

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Copy number variants (CNVs) are crucial genomic alterations implicated in various diseases, including cancer.
• Existing methods for CNV detection often present data in static formats, hindering comprehensive analysis.
• Visualizing complex CNV profiles, including loss of heterozygosity (LOH) and focal variants, remains a challenge.

Purpose of the Study:

• To introduce CNViz, an R/Bioconductor package designed for interactive visualization of copy number data.
• To provide a user-friendly tool for exploring genomic alterations in cancer research and clinical settings.
• To facilitate the integration of diverse genomic data types for enhanced CNV analysis.

Main Methods:

• Developed an interactive R/Shiny visualization tool within the R/Bioconductor package framework.
• Designed the tool to accept genomic locations and copy number ratios at probe, gene, and segment levels.
• Incorporated optional inputs for LOH, single nucleotide variants (SNVs), indels, and specimen metadata (purity, ploidy).

Main Results:

• CNViz enables dynamic visualization and exploration of copy number profiles.
• Users can adjust resolution to examine gene and probe-level copy number changes.
• The tool seamlessly integrates LOH, SNV, and indel data alongside CNV information.

Conclusions:

• CNViz offers an intuitive and powerful platform for visualizing and analyzing copy number variations.
• The package supports both research applications in human cancers and clinical use.
• CNViz enhances the interpretation of complex genomic data for improved understanding of disease.