The CO-Regulation Database (CORD): a tool to identify coordinately expressed genes

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Gene expression meta-analysis can reveal gene function but is often limited by microarray platform.
• Identifying gene-gene interactions from expression data requires cross-platform analysis.
• Existing methods struggle to integrate diverse gene expression datasets.

Purpose of the Study:

• To develop a gene-centered approach for analyzing differential gene expression across multiple microarray platforms.
• To create the CO-Regulation Database (CORD) for identifying gene-gene correlations.
• To facilitate the understanding of gene function through co-expression analysis.

Main Methods:

• Developed a gene-centered approach to analyze differential gene expression.
• Created the CO-Regulation Database (CORD) to store and query gene correlations.
• Utilized data from GEO and ArrayExpress databases, analyzing over 120,000 experiments.

Main Results:

• Analyzed over 120,000 gene expression experiments across platforms.
• Identified correlating genes for over 30,000 genes.
• Demonstrated CORD's utility with known genes like CDKN2A, VIM, and MYOD1, showing consistent correlations.

Conclusions:

• Developed a web-enabled program (CORD) for cross-platform gene-gene correlation analysis.
• CORD aids in understanding gene function by identifying co-expressed genes.
• The database is accessible at http://cord-db.org for researchers.