Pathway Processor: a tool for integrating whole-genome expression results into metabolic networks

Area of Science:

• Bioinformatics
• Systems Biology
• Genomics

Background:

• Analyzing whole-genome expression data is crucial for understanding cellular processes.
• Identifying affected metabolic pathways provides insights into cellular responses.
• Existing methods may not efficiently link transcriptional changes to specific metabolic pathways.

Purpose of the Study:

• To develop a novel tool for visualizing and analyzing gene expression data on metabolic pathways.
• To evaluate the impact of transcriptional changes on metabolic pathways in genome-wide expression studies.
• To provide a method for comparing results across multiple experiments, focusing on key pathways.

Main Methods:

• Utilized the Fisher Exact Test to score biochemical pathways based on the probability of significant gene alterations by chance.
• Developed Pathway Analyzer, a statistical tool within the Pathway Processor package, for analyzing whole-genome expression data.
• Employed OpenDX for pathway visualization and Expression Mapper for detailed examination of gene relationships and expression differences.

Main Results:

• Validated the method on diauxic shift experiments, accurately reproducing known effects of carbon sources on yeast metabolism.
• Demonstrated the ability to reduce complex analyses from individual genes to a manageable set of significantly affected pathways.
• Generated graphical outputs displaying expression differences on metabolic charts, linking open reading frames to their assigned pathways.

Conclusions:

• Pathway Analyzer offers an effective approach to visualize and interpret gene expression data in the context of metabolic pathways.
• The tool facilitates the identification of key metabolic pathways impacted by transcriptional changes, simplifying complex genomic data.
• This method enhances the understanding of cellular metabolism and response to various experimental conditions.