Pathway and ontology analysis: emerging approaches connecting transcriptome data and clinical endpoints

Area of Science:

• Genomics
• Bioinformatics
• Systems Biology

Background:

• Gene expression profiling is increasingly used in clinical research for disease biology and treatment.
• Analyzing and interpreting large gene expression datasets presents a significant challenge.
• Understanding the biological context of gene expression changes is crucial for clinical applications.

Purpose of the Study:

• To discuss approaches for interpreting gene expression data.
• To highlight web-based resources and tools for pathway and ontology analysis.
• To facilitate the understanding and validation of biological pathways in human disease and therapeutics.

Main Methods:

• Associating genes with known biochemical or signaling pathways.
• Mapping genes to standardized biological categories using ontology resources.
• Utilizing web-based tools for data analysis and interpretation.

Main Results:

• Development of analytical tools to manage large gene expression datasets.
• Methods for linking gene expression changes to biological pathways and categories.
• Identification of resources to aid in the interpretation of gene expression data.

Conclusions:

• Interpreting gene expression data through pathway and ontology analysis enhances biological understanding.
• Web-based tools are essential for facilitating these complex analyses.
• This approach aids in designing focused experiments for disease and therapeutic research.