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STARNET 2: a web-based tool for accelerating discovery of gene regulatory networks using microarray co-expression

Daniel Jupiter¹, Hailin Chen, Vincent VanBuren

¹Department of Systems Biology and Translational Medicine, College of Medicine, Texas A & M Health Science Center, Temple, TX 76504, USA. djupiter@tamu.edu

BMC Bioinformatics

|October 16, 2009

Summary

STARNET 2 is a web tool for analyzing gene expression microarray data to discover gene regulatory networks across 10 species. It visualizes co-expressed genes and known interactions, aiding hypothesis generation for biological research.

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Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

Microarray data analysis presents challenges in cross-platform comparisons and inferring gene relationships.
Developing effective tools for post hoc analysis of gene expression data is crucial for biological discovery.

Purpose of the Study:

To introduce STARNET 2, a web-based tool for visual analysis of gene expression correlations.
To facilitate the discovery of putative gene regulatory networks in multiple species.

Main Methods:

Retrieved raw microarray data from NCBI Gene Expression Omnibus for selected Affymetrix platforms across 10 species.
Computed pairwise Pearson correlation coefficients for gene expression profiles and stored results in a MySQL database.
Developed a web interface for user-specified queries, generating correlation network graphs and known interaction data.

Main Results:

STARNET 2 visualizes gene co-expression networks derived from large-scale microarray datasets.
The tool integrates correlation networks with known gene interactions and provides statistical analyses.
The HEATSEEKER module allows parallel comparison of two networks.

Conclusions:

STARNET 2 aids in generating hypotheses about gene regulatory relationships.
The tool supports network interpretation with interaction databases, Gene Ontology term enrichment, and comparative heat maps.
STARNET 2 is freely accessible and valuable for identifying candidate genes for causal network inference.