Using GenMAPP and MAPPFinder to view microarray data on biological pathways and identify global trends in the data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Analyzing gene expression data is crucial for understanding biological processes.
• Visualizing gene relationships within pathways aids interpretation.
• Identifying global trends in gene expression requires robust analytical tools.

Purpose of the Study:

• To introduce GenMAPP (Gene MicroArray Pathway Profiler), a software for gene expression data analysis.
• To present MAPPFinder, an accessory program for identifying biological trends in gene expression datasets.
• To enable dynamic visualization and analysis of gene expression data mapped to biological pathways.

Main Methods:

• GenMAPP utilizes a MAPP (MAPPs) file format to depict gene relationships.
• GenMAPP dynamically color-codes genes on MAPPs based on user-defined expression criteria.
• MAPPFinder integrates GenMAPP with Gene Ontology (GO) annotations to analyze expression data trends.

Main Results:

• GenMAPP provides a platform for interactive viewing and analysis of gene expression data.
• MAPPFinder relates microarray datasets to the GO hierarchy, identifying significant biological trends.
• Statistical scores and percentages are calculated for GO terms based on user-defined gene expression changes.

Conclusions:

• GenMAPP and MAPPFinder offer a comprehensive, free solution for gene expression data analysis and visualization.
• These tools facilitate the identification of global biological trends within complex gene expression datasets.
• The integration with Gene Ontology enhances the biological interpretability of microarray data.