EXPath: a database of comparative expression analysis inferring metabolic pathways for plants.

Area of Science:

• Plant biology
• Genomics
• Bioinformatics

Background:

• Gene expression changes are crucial for metabolic pathways.
• Microarray data is abundant but underutilized for plant pathway analysis.
• Existing resources lack comprehensive plant gene expression and pathway enrichment tools.

Purpose of the Study:

• To develop a centralized database for plant gene expression data.
• To provide tools for analyzing gene expression patterns and inferring metabolic pathways.
• To facilitate research in plant biology by integrating diverse datasets.

Main Methods:

• Collected and curated over 1000 plant microarray samples.
• Developed EXPath database with functions for gene and pathway searches.
• Implemented coexpression analysis, differential gene expression identification, and pathway/GO enrichment.

Main Results:

• EXPath integrates diverse microarray data for three model plants: Arabidopsis thaliana, Oryza sativa, and Zea mays.
• The database supports coexpression analysis and identification of differentially expressed genes (DEGs).
• Enriched KEGG pathways and Gene Ontology (GO) terms can be inferred.

Conclusions:

• EXPath is a valuable, freely accessible resource for plant gene expression and metabolic pathway analysis.
• The database aids researchers in understanding gene function under biotic, abiotic, and hormone-related conditions.
• EXPath enhances plant research by providing a user-friendly interface for complex data exploration.