Overview
Pathway and ontology databases provide structured representations of biological knowledge. They transform the complex, interconnected nature of cellular processes into computable formats that can be queried, analyzed, and visualized. The Gene Ontology (GO) provides a controlled vocabulary to describe gene products across three domains: cellular component, molecular function, and biological process. KEGG (Kyoto Encyclopedia of Genes and Genomes) maps genes to metabolic and signaling pathways. Reactome is an open-source, curated pathway database covering reactions in human biology.
Key Concepts
The Gene Ontology is structured as a directed acyclic graph where terms are connected by parent-child relationships (is-a, part-of, regulates). Annotations associate gene products with GO terms, supported by evidence codes. KEGG Pathway Maps are manually drawn diagrams where nodes represent genes, proteins, or compounds and edges represent interactions or reactions. KEGG Orthology (KO) groups functionally related genes across species. Reactome represents pathways as ordered sets of molecular reactions, each with detailed input-output relationships, and includes tools for pathway analysis of omics data.
Applications
Ontology and pathway databases enable high-level interpretation of experimental data. Enrichment analysis of GO terms or KEGG pathways identifies biological processes overrepresented in gene lists from metabolic pathway studies or differential expression experiments. Enzyme classification and nomenclature cross-references are integrated into KEGG for metabolic reconstruction. Cell signaling and signal transduction pathways in Reactome provide mechanistic context for phosphoproteomics and perturbation screens.
Practical Protocol
To map a list of differentially expressed genes to pathways using KEGG, navigate to kegg.jp and click “KEGG Mapper” > “Search & Color Pathway”. Paste your gene list (using Entrez Gene IDs or UniProt accessions) and select the target organism (e.g., “hsa” for human). KEGG colors the matched genes on each pathway diagram, immediately revealing which pathways are enriched. For a more rigorous statistical analysis, use the web-based KEGG enrichment tool or DAVID (david.ncifcrf.gov): upload your gene list, select the species, and run functional annotation clustering to obtain enrichment p-values and false discovery rates. For GO enrichment analysis, use the Gene Ontology Resource (geneontology.org): paste your gene list, select the evidence code filter (e.g., exclude “IEA” for higher confidence), and view the enriched terms as a hierarchical tree or bar chart. As a concrete example, a researcher performing RNA-seq on liver tissue from a mouse model of non-alcoholic fatty liver disease identifies 500 upregulated genes. Submitting these to KEGG Mapper reveals overrepresentation of “PPAR signaling pathway” (p = 10-8), “Fatty acid metabolism” (p = 10-6), and “Oxidative phosphorylation” (p = 10-4). GO enrichment confirms enrichment of “lipid metabolic process” (GO:0006629) and “mitochondrial electron transport” (GO:0006120), providing a systems-level view of the metabolic reprogramming underlying the disease phenotype. Combined GO and KEGG analyses thus provide complementary views, GO reveals mechanistic detail (e.g., specific transporters or enzymes), while KEGG shows how these components integrate into larger pathway maps.
