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; non-academic use needs a commercial license (via Pathway Solutions). KEGG asks clients to stay at or below 3 API calls per second and blocks clients that exceed it, so the helpers in scripts/kegg_api.py throttle themselves.

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.

Does NOT Trigger

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 (partial match, atom order ignored)
compounds = kegg_find('compound', 'C7H10N4O2', 'formula')

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

Search options: formula (partial match — finds formulae containing the query, atom order ignored), exact_mass (range, rounded to the query's decimal place), 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 image (PNG) — single entry only
pathway_img = kegg_get('hsa05130', 'image')

# Get pathway KGML (XML structure) — single entry only
pathway_kgml = kegg_get('hsa05130', 'kgml')

# Get a BRITE hierarchy as JSON (json is for BRITE, not pathways)
brite_json = kegg_get('br:br08301', 'json')

Output formats: aaseq (protein FASTA), ntseq (nucleotide FASTA), mol/kcf (compound/glycan/drug structures), image (PNG, pathway maps and compound/glycan/drug), image2x (doubled-size PNG, reference map##### only), kgml (pathway XML), conf (pathway map coordinates), json (BRITE hierarchies only — /get/hsa05130/json returns HTTP 400)

Important: image, image2x, and kgml allow only one entry at a time. Sequence and structure formats accept up to 10 entries.

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: the target is the organism code
# ('genes' as the target returns HTTP 400)
genes = kegg_link('hsa', 'hsa00010')  # Glycolysis genes

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

# Find compounds in a pathway: compounds link to reference maps (map#####);
# the organism-specific hsa00010 returns an empty body
compounds = kegg_link('compound', 'map00010')

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

Common links: genes ↔ pathway (use the organism code, e.g. hsa, as the gene-side database), reference pathway (map) ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology). Link output keeps prefixes (path:hsa04010, cpd:C00022) even though list output dropped them in 2023.

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 collections import defaultdict

from scripts.kegg_api import kegg_list, kegg_link

# Step 1: pathway names (one call)
names = dict(line.split('\t', 1) for line in kegg_list('pathway', 'hsa').splitlines() if line)

# Step 2: every gene-pathway link for the organism in ONE call, instead of one
# request per pathway (~370 calls would take minutes at KEGG's 3 calls/s limit)
gene_sets = defaultdict(set)
for line in kegg_link('pathway', 'hsa').splitlines():
    gene, path = line.split('\t')           # 'hsa:10327', 'path:hsa00010'
    gene_sets[path.removeprefix('path:')].add(gene)

# gene_sets['hsa00010'] -> set of KEGG gene IDs for Glycolysis / Gluconeogenesis

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', 'glucose')

# Step 2: Link compound to reactions
reactions = kegg_link('reaction', 'cpd:C00031')  # Glucose

# Step 3: Link reactions to pathways
pathways = kegg_link('pathway', 'rn:R00299')  # Specific reaction

# Step 4: Get pathway details
pathway_info = kegg_get('map00010')  # Glycolysis

Workflow 4: Cross-Database Integration

Use case: Integrating KEGG data with UniProt, NCBI, or PubChem databases.

from scripts.kegg_api import kegg_conv, kegg_get

# Step 1: Convert KEGG gene IDs to external database IDs
uniprot_map = kegg_conv('uniprot', 'hsa')
ncbi_map = kegg_conv('ncbi-geneid', 'hsa')

# Step 2: Parse conversion results
for line in uniprot_map.split('\n'):
    if line:
        kegg_id, uniprot_id = line.split('\t')
        # Use external IDs for integration

# Step 3: Get sequences using KEGG
sequence = kegg_get('hsa:10458', 'aaseq')

Workflow 5: Organism-Specific Pathway Analysis

Use case: Comparing pathways across different organisms.

from scripts.kegg_api import kegg_list, kegg_get

# Step 1: List pathways for multiple organisms
human_pathways = kegg_list('pathway', 'hsa')
mouse_pathways = kegg_list('pathway', 'mmu')
yeast_pathways = kegg_list('pathway', 'sce')

# Step 2: Get reference pathway for comparison
ref_pathway = kegg_get('map00010')  # Reference glycolysis

# Step 3: Get organism-specific versions
hsa_glycolysis = kegg_get('hsa00010')
mmu_glycolysis = kegg_get('mmu00010')

Pathway Categories

KEGG organizes pathways into seven major categories. When interpreting pathway IDs or recommending pathways to users:

1. Metabolism (e.g., map00010 - Glycolysis, map00190 - Oxidative phosphorylation)
2. Genetic Information Processing (e.g., map03010 - Ribosome, map03040 - Spliceosome)
3. Environmental Information Processing (e.g., map04010 - MAPK signaling, map02010 - ABC transporters)
4. Cellular Processes (e.g., map04140 - Autophagy, map04210 - Apoptosis)
5. Organismal Systems (e.g., map04610 - Complement cascade, map04910 - Insulin signaling)
6. Human Diseases (e.g., map05200 - Pathways in cancer, map05010 - Alzheimer disease)
7. Drug Development (chronological and target-based classifications)

Reference references/kegg_reference.md for detailed pathway lists and classifications.

Important Identifiers and Formats

Pathway IDs

• map##### - Reference pathway (generic, not organism-specific)
• hsa##### - Human pathway
• mmu##### - Mouse pathway

Gene IDs

• Format: organism:gene_number (e.g., hsa:10458)

Compound IDs

• Format: cpd:C##### (e.g., cpd:C00002 for ATP)

Drug IDs

• Format: dr:D##### (e.g., dr:D00001)

Enzyme IDs

• Format: ec:EC_number (e.g., ec:1.1.1.1)

KO (KEGG Orthology) IDs

• Format: ko:K##### (e.g., ko:K00001)

API Limitations

Respect these constraints when using the KEGG API:

1. Entry limits: Maximum 10 entries per operation (except image/image2x/kgml: 1 entry only)
2. Academic use: API is for academic use only; commercial use requires licensing
3. HTTP status codes: Check for 200 (success), 400 (bad request), 404 (not found)
4. Rate limiting: at most 3 calls per second — KEGG blocks clients that exceed it. Prefer bulk calls (/link/pathway/hsa, /conv/ncbi-geneid/hsa) over per-entry loops

Detailed Reference

For comprehensive API documentation, database specifications, organism codes, and advanced usage, refer to references/kegg_reference.md. This includes:

• Complete list of KEGG databases
• Detailed API operation syntax
• All organism codes
• HTTP status codes and error handling
• Integration with Biopython and R/Bioconductor
• Best practices for API usage

Troubleshooting

404 Not Found: Entry or database doesn't exist; verify IDs and organism codes 400 Bad Request: Syntax error in API call; check parameter formatting Empty results: Search term may not match entries; try broader keywords Image/KGML errors: image/image2x/kgml only work with single entries; remove batch processing `json` on a pathway returns 400: json is only valid for BRITE hierarchies (e.g. br:br08301); use kgml for pathway structure

Additional Tools

For interactive pathway visualization and annotation:

• KEGG Mapper: https://www.kegg.jp/kegg/mapper/
• BlastKOALA: Automated genome annotation
• GhostKOALA: Metagenome/metatranscriptome annotation