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

Liang Sun1, Sufen Dong1,2, Yinbing Ge1

  • 1Noble Research Institute, Ardmore, OK, United States.

Frontiers in Genetics
|May 28, 2019
PubMed
Summary
This summary is machine-generated.

DiVenn is a new web tool for comparing gene expression data from RNA-Seq experiments. It visualizes gene regulation and integrates pathway and gene ontology (GO) knowledge for deeper biological insights.

Keywords:
KEGGVenn diagramgene ontologypathogen infectiontranscriptome datavisualization

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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.