kegg-database

KEGG Database

Overview

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.

Important: KEGG API is made available only for academic use by academic users.

When to Use This Skill

This skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.

Quick Start

The skill provides:

1. Python helper functions (scripts/kegg_api.py) for all KEGG REST API operations
2. Comprehensive reference documentation (references/kegg_reference.md) with detailed API specifications

When users request KEGG data, determine which operation is needed and use the appropriate function from scripts/kegg_api.py.

Core Operations

1. Database Information (kegg_info)

Retrieve metadata and statistics about KEGG databases.

When to use: Understanding database structure, checking available data, getting release information.

Usage:

from scripts.kegg_api import kegg_info

# Get pathway database info
info = kegg_info('pathway')

# Get organism-specific info
hsa_info = kegg_info('hsa')  # Human genome

Common databases: kegg, pathway, module, brite, genes, genome, compound, glycan, reaction, enzyme, disease, drug

2. Listing Entries (kegg_list)

List entry identifiers and names from KEGG databases.

When to use: Getting all pathways for an organism, listing genes, retrieving compound catalogs.

Usage:

from scripts.kegg_api import kegg_list

# List all reference pathways
pathways = kegg_list('pathway')

# List human-specific pathways
hsa_pathways = kegg_list('pathway', 'hsa')

# List specific genes (max 10)
genes = kegg_list('hsa:10458+hsa:10459')

Common organism codes: hsa (human), mmu (mouse), dme (fruit fly), sce (yeast), eco (E. coli)

3. Searching (kegg_find)

Search KEGG databases by keywords or molecular properties.