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.

When to use: Finding genes by name/description, searching compounds by formula or mass, discovering entries by keywords.

Usage:

from scripts.kegg_api import kegg_find

# Keyword search
results = kegg_find('genes', 'p53')
shiga_toxin = kegg_find('genes', 'shiga toxin')

# Chemical formula search (exact match)
compounds = kegg_find('compound', 'C7H10N4O2', 'formula')

# Molecular weight range search
drugs = kegg_find('drug', '300-310', 'exact_mass')

Search options: formula (exact match), exact_mass (range), mol_weight (range)

4. Retrieving Entries (kegg_get)

Get complete database entries or specific data formats.

When to use: Retrieving pathway details, getting gene/protein sequences, downloading pathway maps, accessing compound structures.

Usage:

from scripts.kegg_api import kegg_get

# Get pathway entry
pathway = kegg_get('hsa00010')  # Glycolysis pathway

# Get multiple entries (max 10)
genes = kegg_get(['hsa:10458', 'hsa:10459'])

# Get protein sequence (FASTA)
sequence = kegg_get('hsa:10458', 'aaseq')

# Get nucleotide sequence
nt_seq = kegg_get('hsa:10458', 'ntseq')

# Get compound structure
mol_file = kegg_get('cpd:C00002', 'mol')  # ATP in MOL format

# Get pathway as JSON (single entry only)
pathway_json = kegg_get('hsa05130', 'json')

# Get pathway image (single entry only)
pathway_img = kegg_get('hsa05130', 'image')

Output formats: aaseq (protein FASTA), ntseq (nucleotide FASTA), mol (MOL format), kcf (KCF format), image (PNG), kgml (XML), json (pathway JSON)

Important: Image, KGML, and JSON formats allow only one entry at a time.

5. ID Conversion (kegg_conv)

Convert identifiers between KEGG and external databases.

When to use: Integrating KEGG data with other databases, mapping gene IDs, converting compound identifiers.

Usage:

from scripts.kegg_api import kegg_conv

# Convert all human genes to NCBI Gene IDs
conversions = kegg_conv('ncbi-geneid', 'hsa')

# Convert specific gene
gene_id = kegg_conv('ncbi-geneid', 'hsa:10458')

# Convert to UniProt
uniprot_id = kegg_conv('uniprot', 'hsa:10458')

# Convert compounds to PubChem
pubchem_ids = kegg_conv('pubchem', 'compound')

# Reverse conversion (NCBI Gene ID to KEGG)
kegg_id = kegg_conv('hsa', 'ncbi-geneid')

Supported conversions: ncbi-geneid, ncbi-proteinid, uniprot, pubchem, chebi

6. Cross-Referencing (kegg_link)

Find related entries within and between KEGG databases.

When to use: Finding pathways containing genes, getting genes in a pathway, mapping genes to KO groups, finding compounds in pathways.

Usage:

from scripts.kegg_api import kegg_link

# Find pathways linked to human genes
pathways = kegg_link('pathway', 'hsa')

# Get genes in a specific pathway
genes = kegg_link('genes', 'hsa00010')  # Glycolysis genes

# Find pathways containing a specific gene
gene_pathways = kegg_link('pathway', 'hsa:10458')

# Find compounds in a pathway
compounds = kegg_link('compound', 'hsa00010')

# Map genes to KO (orthology) groups
ko_groups = kegg_link('ko', 'hsa:10458')

Common links: genes ↔ pathway, pathway ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology)

7. Drug-Drug Interactions (kegg_ddi)

Check for drug-drug interactions.

When to use: Analyzing drug combinations, checking for contraindications, pharmacological research.

Usage:

from scripts.kegg_api import kegg_ddi

# Check single drug
interactions = kegg_ddi('D00001')

# Check multiple drugs (max 10)
interactions = kegg_ddi(['D00001', 'D00002', 'D00003'])

Common Analysis Workflows

Workflow 1: Gene to Pathway Mapping

Use case: Finding pathways associated with genes of interest (e.g., for pathway enrichment analysis).

from scripts.kegg_api import kegg_find, kegg_link, kegg_get

# Step 1: Find gene ID by name
gene_results = kegg_find('genes', 'p53')

# Step 2: Link gene to pathways
pathways = kegg_link('pathway', 'hsa:7157')  # TP53 gene

# Step 3: Get detailed pathway information
for pathway_line in pathways.split('\n'):
    if pathway_line:
        pathway_id = pathway_line.split('\t')[1].replace('path:', '')
        pathway_info = kegg_get(pathway_id)
        # Process pathway information

Workflow 2: Pathway Enrichment Context

Use case: Getting all genes in organism pathways for enrichment analysis.

from scripts.kegg_api import kegg_list, kegg_link

# Step 1: List all human pathways
pathways = kegg_list('pathway', 'hsa')

# Step 2: For each pathway, get associated genes
for pathway_line in pathways.split('\n'):
    if pathway_line:
        pathway_id = pathway_line.split('\t')[0]
        genes = kegg_link('genes', pathway_id)
        # Process genes for enrichment analysis

Workflow 3: Compound to Pathway Analysis

Use case: Finding metabolic pathways containing compounds of interest.

from scripts.kegg_api import kegg_find, kegg_link, kegg_get

# Step 1: Search for compound
compound_results = kegg_find('compound'
how to use kegg-database
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use kegg-database on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
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›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add kegg-database
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/davila7/claude-code-templates --skill kegg-databasecopy
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Verify installation
Confirm successful installation by checking the skill directory location:
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Reload or restart Cursor to activate kegg-database. Access the skill through slash commands (e.g., /kegg-database) or your agent's skill management interface.
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