Skip to content

VizCNV is an interactive tool designed to analyze and visualize CNVs from short read WGS data in rare disease research. Built on R Studio Shiny, it streamlines the identification of complex genomic rearrangements and facilitates advancements in understanding and diagnosing rare genetic conditions.

License

Notifications You must be signed in to change notification settings

BCM-Lupskilab/VizCNV

 
 

Repository files navigation

VizCNV

This is a shiny app for chromosomal copy number variant analysis. It can parse the vcf file with SV calls, visualize CNV and B-allele frequency and genetic phasing information interactively.

Prerequisites

R version >= 4.2 Following R libraries are required:

Shifting level models based segmentation is performed using SLMSuite.

Launch app on local with the main branch:

shiny::runGitHub(repo = "BCM-Lupskilab/VizCNV")

or Launch app on local with the dev branch:

shiny::runGitHub(repo = "cluhaowie/VizCNV",ref="dev")

A docker image is public available and can be pulled from docker hub

docker pull duclare123/vizcnv_dev

and run docker image with

docker run -d -p 3838:3838 -v /path/to/local:/root/input/ duclare123/vizcnv_dev

then accessing the app with http://localhost:3838/

Upload the required file from local file systerm: gif1

If launch the app on cloud or on server, input file need to be upload due to access restriction.

Visualize the CNV calls in table format, read depth plot and B-allele frequency together: gif1

The app require read depth file as the input: A output from mosedepth can be used example of generate the read depth file for 1Kb window size would be:

mosdepth -n --fast-mode --by 1000 sample.wgs $sample.wgs.cram

About

VizCNV is an interactive tool designed to analyze and visualize CNVs from short read WGS data in rare disease research. Built on R Studio Shiny, it streamlines the identification of complex genomic rearrangements and facilitates advancements in understanding and diagnosing rare genetic conditions.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 94.6%
  • Fortran 5.4%