Publication date: Jun 02, 2025

Understanding gene function and pathways is a key goal in genomics. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) provide frameworks to annotate genes based on their roles, cellular locations, and functions. These classifications help researchers analyze complex systems, identify drug targets, and investigate gene-disease connections. GO and KEGG annotations are central to enrichment analysis, which tests for overrepresented pathways in gene sets, often comparing disease to healthy states. Enrichment analysis methods include overrepresentation analysis (ORA) for gene lists and gene set enrichment analysis (GSEA) for ranked gene lists. In R/Bioconductor, tools like clusterProfiler, topGO, and DOSE make this process accessible. Each tool offers unique features, supporting insights into pathways, regulatory functions, and disease mechanisms. This chapter provides step-by-step instructions for using these tools to analyze differentially expressed genes in SARS-CoV-2-infected patients compared to healthy controls. By following this protocol, researchers can efficiently interpret gene set enrichment results and export them in publication-ready tables and figures. This analysis reveals valuable biological insights, facilitating a deeper understanding of gene regulation and pathway interactions in disease contexts.

Semantics

Original Article