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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Published on: August 16, 2017

WebGIVI: a web-based gene enrichment analysis and visualization tool.

Liang Sun^1,2, Yongnan Zhu^3,4, A S M Ashique Mahmood⁵

¹Department of Animal and Food Sciences, University of Delaware, Newark, DE, USA.

BMC Bioinformatics

|May 6, 2017

Summary

High-throughput transcriptome studies present challenges in data interpretation. WebGIVI is an interactive tool that visualizes gene:iTerm pairs, aiding researchers in hypothesis generation from gene lists.

Keywords:

Gene enrichment Gene iTerm Visualization Web development eGIFT

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput transcriptome studies generate large gene lists, posing interpretation challenges.
Existing methods for associating genes with biological concepts (iTerms) from literature are often daunting to analyze.
The eGIFT text-mining system extracts informative terms (iTerms) from biomedical literature to aid gene dataset interpretation.

Purpose of the Study:

To develop an interactive web-based visualization tool for exploring gene:iTerm pairs.
To facilitate the interpretation of large gene datasets by visualizing relationships between genes and biological concepts.

Main Methods:

Developed WebGIVI, an interactive web-based visualization tool.
Utilized Cytoscape and Data Driven Document JavaScript libraries for visualization.
WebGIVI accepts gene lists to retrieve gene symbols and iTerms, visualizing pairs via Concept Maps or Cytoscape Network Maps.
Supports visualization of any two-column tab-separated data.

Main Results:

WebGIVI enables interactive exploration of gene:iTerm relationships.
The tool visualizes gene and iTerm pairs using distinct map types (Concept Map, Cytoscape Network Map).
WebGIVI can handle and visualize hundreds of nodes, generating high-resolution images.

Conclusions:

WebGIVI provides an integrated network graph for gene and iTerms, facilitating hypothesis generation.
Interactive features like filtering, sorting, and grouping enhance biological data interpretation.
The tool is suitable for research publications and its source code is freely available.