KEGGREST

KEGGREST provides programmatic access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server for retrieval and analysis of KEGG pathway, gene, and annotation data.

Key Features:

• Programmatic KEGG access: Client interface to the KEGG REST server for retrieval of pathway, gene, and annotation records.
• Integration with KEGGSOAP and KEGG Python package: Incorporates functionality from KEGGSOAP and the KEGG Python package and interoperates with R packages such as GO.db and KEGG.db for downstream analyses.
• Pathway and GO enrichment: Identifies overrepresented Gene Ontology (GO) categories and KEGG pathways among sets of differentially expressed genes.
• Microarray differential expression support: Supports comparative analysis of microarray gene expression data to identify differentially expressed genes (e.g., 122 genes reported in a cervical cancer study).
• Network construction support: Facilitates construction and visualization of biological networks in Cytoscape using KEGG-derived relationships.
• Biological process and pathway identification: Highlights significant GO biological processes such as cell cycle and cell proliferation and KEGG pathways including oocyte meiosis, cell cycle, and progesterone-mediated oocyte maturation.
• Target gene identification: Enables identification of recurrent genes in enriched processes and pathways, exemplified by CDK1 as a potential therapeutic target.

Scientific Applications:

• Pathway enrichment analysis: Analyze KEGG pathways and GO categories for lists of differentially expressed genes.
• Disease mechanism investigation: Identify biological processes and pathways implicated in diseases such as cervical cancer using microarray-derived differential expression.
• Biomarker and therapeutic target discovery: Support identification of candidate biomarkers and targets, for example CDK1, by linking expression changes to pathway context.
• Network biology: Build and interpret gene and pathway interaction networks in Cytoscape using KEGG-derived relationships.

Methodology:

Comparative gene expression profiling of microarray data from healthy and diseased samples to identify differentially expressed genes, followed by GO category and KEGG pathway enrichment analysis using integrated R packages (GO.db, KEGG.db) and construction of biological networks in Cytoscape.