DiVenn: An Interactive and Integrated Web-Based Visualization Tool for Comparing Gene Lists

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Venn diagrams are commonly used to compare gene expression data across multiple samples.
• Understanding overlapping genes and their associated pathways or gene ontology (GO) terms is crucial.
• Existing tools lack integrated visualization of gene expression levels with pathway and GO information.

Purpose of the Study:

• To develop DiVenn, a novel web-based tool for comparative analysis of gene lists from RNA-Seq experiments.
• To provide an interactive force-directed graph visualizing gene regulation and biological annotations.
• To integrate KEGG pathway and GO knowledge for enhanced data visualization and analysis.

Main Methods:

• Development of a web-based tool named DiVenn.
• Implementation of a force-directed graph for visualizing gene expression levels.
• Integration of KEGG pathway and Gene Ontology (GO) databases for annotation.
• Incorporation of interactive features for subsetting and highlighting gene nodes.
• Inclusion of pathway and GO enrichment analysis functionalities.
• Provision for exporting high-resolution images and gene-associated information.

Main Results:

• DiVenn offers an informative force-directed graph displaying gene expression levels across multiple datasets.
• The tool provides interactive visualization, allowing users to explore biological annotations, pathways, and GO terms.
• DiVenn facilitates pathway and GO enrichment analysis for selected or all genes within the graph.
• Users can export high-resolution images and detailed gene information.

Conclusions:

• DiVenn enhances the analysis of comparative gene expression data by integrating visualization, annotation, and enrichment analysis.
• The tool facilitates a deeper understanding of gene regulation patterns and their biological significance.
• DiVenn is a freely accessible resource for researchers working with RNA-Seq data.