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CGDV: a webtool for circular visualization of genomics and transcriptomics data.

Vineet Jha¹, Gulzar Singh^1,2, Shiva Kumar¹

¹Labs, Persistent Systems Limited, Pingala - Aryabhata, Erandwane, Pune, 411004, India.

|October 26, 2017

Summary

This summary is machine-generated.

CGDV is a new webtool for automated visualization of genomics and transcriptomics data. It simplifies the generation of complex circular plots, aiding biologists and bioinformaticians in data interpretation.

Keywords:

Circular diagram Genomics and transcriptomics data visualization Visualization Web circos

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Area of Science:

Genomics and Transcriptomics Data Analysis
Bioinformatics Tool Development
Computational Biology

Background:

Interpreting large-scale genomics and transcriptomics data is challenging.
Existing tools for automated data visualization are scarce and often require bioinformatics expertise.
Circular plots offer holistic data visualization but are complex to generate.

Purpose of the Study:

To develop a user-friendly webtool for automated visualization of diverse genomics and transcriptomics data.
To simplify the creation of circular plots for various data types and model organisms.
To provide a seamless visualization solution for biologists and bioinformaticians.

Main Methods:

Development of CGDV (Circos for Genomics and Transcriptomics Data Visualization) webtool.
Utilizes Circos for generating circular plots.
Accepts various data formats (vcf, bed, xls, etc.) and automates intermediate file generation.

Main Results:

CGDV enables seamless and automated visualization of large-scale genomics and transcriptomics data.
The tool generates circular plots from diverse analyzed data outputs.
CGDV is freely accessible online at https://cgdv-upload.persistent.co.in/cgdv/.

Conclusions:

CGDV facilitates biological insights through tailored circular plots for different data types.
The tool visualizes inter-relationships, gene fusions, expression patterns, and variations.
CGDV empowers biologists and bioinformaticians to visualize complex data effectively.