Graph-based analysis and visualization of experimental results with ONDEX.

Area of Science:

• Bioinformatics
• Systems Biology
• Genomics

Background:

• Interpreting high-throughput gene expression data, like microarrays, is challenging due to the need to connect significant genes with distributed biological information.
• Current methods are labor-intensive, requiring significant informatics and biological expertise to manually integrate data from numerous databases and scientific literature.
• Automated software tools are essential to streamline the process of determining the biological relevance of gene expression findings.

Purpose of the Study:

• To introduce and describe the ONDEX system for interpreting gene expression results.
• To highlight ONDEX's capabilities in semantic database integration, text mining, and graph-based analysis.
• To demonstrate ONDEX's utility in identifying biological relevance and functional annotations for genes.

Main Methods:

• ONDEX system: a database integrating semantic data, text mining, and graph-based analysis.
• Application of ONDEX to interpret gene expression data from microarray experiments.
• Utilizing graph-based analysis and visualization features for relationship discovery.

Main Results:

• ONDEX successfully integrated diverse biological information with gene expression data.
• A case study demonstrated ONDEX's ability to identify causal relationships between stress response genes and metabolic pathways.
• ONDEX discovered functional annotations for previously uncharacterized significant genes from the microarray experiment.

Conclusions:

• The ONDEX system provides an effective solution for interpreting complex gene expression data.
• ONDEX facilitates the identification of biological relevance and functional insights from genomic experiments.
• Automated integration and analysis tools like ONDEX are crucial for advancing biological discovery.