Back to Search Start Over

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

Authors :
Ramesh RG
Bigdeli A
Rushton C
Rosenbaum JN
Source :
Journal of pathology informatics [J Pathol Inform] 2022 Feb 15; Vol. 13, pp. 100089. Date of Electronic Publication: 2022 Feb 15 (Print Publication: 2022).
Publication Year :
2022

Abstract

Copy number variants (CNVs) comprise a class of mutation which includes deletion, duplication, or amplification events that range in size from smaller than a single-gene or exon, to the size of a full chromosome. These changes can affect gene expression levels and are thus implicated in disease, including cancer. Although a variety of tools and methodologies exist to detect CNVs using data from massively parallel sequencing (also referred to as next-generation sequencing), it can be difficult to appreciate the copy number profile in a list format or as a static image. CNViz is a freely accessible R/Bioconductor package that launches an interactive R/Shiny visualization tool to facilitate review of copy number data. As inputs, it requires genomic locations and corresponding copy number ratios for probe, gene, and/or segment-level data. If supplied, loss of heterozygosity (LOH), focal variant data [single nucleotide variants (SNVs) and small insertions and deletions (indels)], and metadata (e.g., specimen purity and ploidy) can also be incorporated into the visualization. The CNViz R/Bioconductor package is an easy-to-use tool built with the intent of encouraging visualization and exploration of copy number variation. CNViz can be used in a clinical setting as well as for research to study patterns in human cancers more broadly. The intuitive interface allows users to visualize the copy number profile of a specimen, dynamically change resolution to explore gene and probe-level copy number changes, and simultaneously integrate LOH, SNV, and indel findings. CNViz is available for download as an R package via Bioconductor. An example of the application is available at rebeccagreenblatt.shinyapps.io/cnviz_example.<br /> (© 2022 The Authors.)

Details

Language :
English
ISSN :
2229-5089
Volume :
13
Database :
MEDLINE
Journal :
Journal of pathology informatics
Publication Type :
Academic Journal
Accession number :
35251754
Full Text :
https://doi.org/10.1016/j.jpi.2022.100089