kegg-database

What problem does it solve?

Manually extracting and integrating biological pathway, gene, and compound data from KEGG is tedious and prone to inconsistencies. This Skill automates access to the KEGG REST API, simplifying complex systems biology and enrichment analyses, allowing researchers to quickly uncover biological insights.

Core Features & Use Cases

• Pathway & Gene Querying: Retrieve detailed information on metabolic pathways, genetic information, genes, and compounds across various organisms.
• ID Conversion & Linking: Seamlessly convert identifiers between KEGG and external databases (e.g., UniProt, NCBI) and link related entries within KEGG.
• Drug Interaction Analysis: Directly check for drug-drug interactions, aiding in pharmacological research and drug combination analysis.
• Use Case: Given a list of differentially expressed genes, automatically map them to KEGG pathways to identify enriched biological processes, saving hours of manual lookup and cross-referencing.

Quick Start

List all human-specific pathways

from scripts.kegg_api import kegg_list hsa_pathways = kegg_list('pathway', 'hsa') print("First 3 human pathways:") print(' '.join(hsa_pathways.split(' ')[:3]))

Find genes by keyword (e.g., 'p53')

from scripts.kegg_api import kegg_find gene_results = kegg_find('genes', 'p53') print(" Genes matching 'p53':") print(' '.join(gene_results.split(' ')[:3]))