KegArray

KegArray maps transcriptome and metabolome datasets onto Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, BRITE hierarchical classifications, and genome maps to enable interpretation of gene expression and compound profiles in a pathway context.

Key Features:

• Implementation: Implemented in Java.
• KEGG mapping: Maps gene expression profiles and compound/metabolite profiles to KEGG pathways, BRITE hierarchies, and genome maps.
• Supported data types: Accepts transcriptome and metabolome datasets, including data derived from microarray, metagenome, and metabolome analyses.
• Data integration: Integrates experimental omics data with KEGG knowledgebase resources.
• Visualization and interpretation: Visualizes and facilitates interpretation of large-scale omics datasets in pathway and network contexts.
• Domain-specific resources: Provides access to KEGG resources relevant to human disease research, drug discovery, and plant science.

Scientific Applications:

• Human disease research: Maps omics data onto KEGG pathways and resources to support disease-related analyses.
• Drug discovery: Connects omics profiles to KEGG pathway and related resources to inform target and pathway-level interpretations.
• Plant science: Enables mapping of plant transcriptome and metabolome data to KEGG pathways and BRITE classifications relevant to plant biology.
• Pathway and network analysis: Facilitates elucidation of complex biological processes and interactions via pathway-level mapping.

Methodology:

Maps gene expression and compound profiles onto KEGG pathways, BRITE hierarchical classifications, and genome maps and visualizes these mappings for interpretation.