Kegg Database

KEGG Database — Biological Pathway & Molecular Network Queries

Overview

KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis, molecular interaction networks, and cross-database ID conversion. Access is via a direct REST API with no authentication — all operations use simple HTTP GET requests returning tab-delimited text.

When to Use

• Mapping genes to biological pathways (e.g., "which pathways involve TP53?")
• Retrieving metabolic pathway details, gene lists, or compound structures
• Converting identifiers between KEGG, NCBI Gene, UniProt, and PubChem
• Checking drug-drug interactions from KEGG's pharmacological database
• Building pathway enrichment context (all genes per pathway for an organism)
• Cross-referencing compounds, reactions, enzymes, and pathways
• For Python-native multi-database queries (KEGG + UniProt + Ensembl in one script), prefer bioservices instead
• For pathway visualization, use KEGG Mapper (https://www.kegg.jp/kegg/mapper/) directly

Prerequisites

pip install requests

API constraints:

• Academic use only — commercial use requires a separate KEGG license
• Max 10 entries per get/list/conv/link/ddi call (image/kgml/json: 1 entry only)
• No explicit rate limit, but add time.sleep(0.5) between batch requests to avoid server-side throttling
• Base URL: https://rest.kegg.jp/

Quick Start

import requests
import time

BASE = "https://rest.kegg.jp"

def kegg_get(operation, *args):
    """Generic KEGG REST API caller."""
    url = f"{BASE}/{operation}/{'/'.join(args)}"
    resp = requests.get(url)
    resp.raise_for_status()
    return resp.text

# Find pathways linked to human gene TP53
pathways = kegg_get("link", "pathway", "hsa:7157")
print(pathways[:200])
# hsa:7157	path:hsa04010
# hsa:7157	path:hsa04110
# ...

# Get pathway details
detail = kegg_get("get", "hsa04110")
print(detail[:300])

Core API

1. Database Information — kegg_info

Retrieve metadata and statistics about KEGG databases.

import requests

BASE = "https://rest.kegg.jp"

# Database-level info
info = requests.get(f"{BASE}/info/pathway").text
print(info[:200])
# pathway          Pathway
#                  Release 112.0, Dec 2025
#                  Kanehisa Laboratories
#                  ...

# Organism-level info
hsa_info = requests.get(f"{BASE}/info/hsa").text
print(hsa_info[:200])

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 any KEGG database.

import requests

BASE = "https://rest.kegg.jp"

# All human pathways
hsa_pathways = requests.get(f"{BASE}/list/pathway/hsa").text
for line in hsa_pathways.strip().split("\n")[:5]:
    pathway_id, name = line.split("\t")
    print(f"{pathway_id}: {name}")
# path:hsa00010: Glycolysis / Gluconeogenesis - Homo sapiens (human)
# ...

# Specific entries (max 10, joined with +)
genes = requests.get(f"{BASE}/list/hsa:10458+hsa:10459").text
print(genes)

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

3. Keyword Search — kegg_find

Search databases by keywords or molecular properties.

import requests
import time

BASE = "https://rest.kegg.jp"

# Keyword search in genes
results = requests.get(f"{BASE}/find/genes/p53").text
print(f"Found {len(results.strip().split(chr(10)))} entries")
time.sleep(0.5)

# Chemical formula search (exact match)
compounds = requests.get(f"{BASE}/find/compound/C7H10N4O2/formula").text
print(compounds[:200])
time.sleep(0.5)

# Molecular weight range search
drugs = requests.get(f"{BASE}/find/drug/300-310/exact_mass").text
print(drugs[:200])

Search options: append /formula (exact match), /exact_mass (range), /mol_weight (range) to compound/drug queries.

4. Entry Retrieval — kegg_get

Retrieve complete database entries or specific data formats.

import requests
import time

BASE = "https://rest.kegg.jp"

# Full pathway entry (text format)
pathway = requests.get(f"{BASE}/get/hsa00010").text
print(pathway[:500])
time.sleep(0.5)

# Multiple entries (max 10, joined with +)
genes = requests.get(f"{BASE}/get/hsa:10458+hsa:10459").text

# Protein sequence (FASTA)
fasta = requests.get(f"{BASE}/get/hsa:10458/aaseq").text
print(fasta[:200])
time.sleep(0.5)

# Compound structure (MOL format)
mol = requests.get(f"{BASE}/get/cpd:C00002/mol").text  # ATP

# Pathway image (PNG, single entry only)
img_resp = requests.get(f"{BASE}/get/hsa05130/image")
with open("pathway.png", "wb") as f:
    f.write(img_resp.content)
print(f"Saved pathway image: {len(img_resp.content)} bytes")

Output formats: aaseq (protein FASTA), ntseq (nucleotide FASTA), mol (MOL), kcf (KCF), image (PNG), kgml (XML), json (pathway JSON). Image/KGML/JSON accept one entry only.

5. ID Conversion — kegg_conv

Convert identifiers between KEGG and external databases.

import requests
import time

BASE = "https://rest.kegg.jp"

# KEGG gene → NCBI Gene ID (specific gene)
ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/hsa:10458").text
print(ncbi.strip())
# hsa:10458	ncbi-geneid:10458
time.sleep(0.5)

# KEGG gene → UniProt
uniprot = requests.get(f"{BASE}/conv/uniprot/hsa:10458").text
print(uniprot.strip())
time.sleep(0.5)

# Bulk conversion: all human genes → NCBI Gene IDs
all_conv = requests.get(f"{BASE}/conv/ncbi-geneid/hsa").text
lines = all_conv.strip().split("\n")
print(f"Total conversions: {len(lines)}")

# Reverse: NCBI Gene ID → KEGG
reverse = requests.get(f"{BASE}/conv/hsa/ncbi-geneid:7157").text
print(reverse.strip())  # TP53

Supported external databases: ncbi-geneid, ncbi-proteinid, uniprot, pubchem, chebi

6. Cross-Referencing — kegg_link

Find related entries within and between KEGG databases.

import requests
import time

BASE = "https://rest.kegg.jp"

# Genes in glycolysis pathway
genes = requests.get(f"{BASE}/link/genes/hsa00010").text
gene_list = [line.split("\t")[1] for line in genes.strip().split("\n") if line]
print(f"Glycolysis genes: {len(gene_list)}")
time.sleep(0.5)

# Pathways containing a specific gene
pathways = requests.get(f"{BASE}/link/pathway/hsa:7157").text  # TP53
print(pathways[:300])
time.sleep(0.5)

# Compounds in a pathway
compounds = requests.get(f"{BASE}/link/compound/hsa00010").text
print(f"Compounds in glycolysis: {len(compounds.strip().split(chr(10)))}")

# Map genes to KO (orthology) groups
ko = requests.get(f"{BASE}/link/ko/hsa:10458").text
print(ko.strip())

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

7. Drug-Drug Interactions — kegg_ddi

Check pharmacological interactions between drugs.

import requests

BASE = "https://rest.kegg.jp"

# Single drug — all known interactions
interactions = requests.get(f"{BASE}/ddi/D00001").text
print(f"Interactions: {len(interactions.strip().split(chr(10)))}")

# Pairwise check (max 10 drugs, joined with +)
pair = requests.get(f"{BASE}/ddi/D00001+D00002+D00003").text
print(pair[:300])

Key Concepts

Identifier Formats

Pathway Categories

KEGG organizes pathways into seven major categories:

1. Metabolism — map001xx (Glycolysis, TCA cycle, amino acid metabolism)
2. Genetic Information Processing — map030xx (Ribosome, Spliceosome, DNA repair)
3. Environmental Information Processing — map040xx (MAPK signaling, ABC transporters)
4. Cellular Processes — map041xx (Autophagy, Apoptosis, Cell cycle)
5. Organismal Systems — map046xx (Immune, Endocrine, Nervous)
6. Human Diseases — map052xx (Cancer, Neurodegenerative, Infectious)
7. Drug Development — Chronological and target-based classifications

Common Workflows

Workflow: Gene to Pathway Mapping

Find all pathways associated with a gene of interest.

import requests
import time

BASE = "https://rest.kegg.jp"

# Step 1: Find gene by keyword
results = requests.get(f"{BASE}/find/genes/BRCA1+homo+sapiens").text
print("Gene search results:")
for line in results.strip().split("\n")[:5]:
    print(f"  {line}")
time.sleep(0.5)

# Step 2: Get pathways linked to BRCA1
pathways = requests.get(f"{BASE}/link/pathway/hsa:672").text
pathway_ids = [line.split("\t")[1].replace("path:", "") for line in pathways.strip().split("\n") if line]
print(f"\nBRCA1 is in {len(pathway_ids)} pathways:")
time.sleep(0.5)

# Step 3: Get pathway names
for pid in pathway_ids[:5]:
    info = requests.get(f"{BASE}/get/{pid}").text
    # Extract NAME field
    for line in info.split("\n"):
        if line.startswith("NAME"):
            print(f"  {pid}: {line.replace('NAME', '').strip()}")
            break
    time.sleep(0.5)

Workflow: Pathway Enrichment Context

Build a gene-set collection for all pathways of an organism.

import requests
import time

BASE = "https://rest.kegg.jp"

# Step 1: List all human pathways
pathways_text = requests.get(f"{BASE}/list/pathway/hsa").text
pathways = {}
for line in pathways_text.strip().split("\n"):
    pid, name = line.split("\t", 1)
    pathways[pid.replace("path:", "")] = name
print(f"Total human pathways: {len(pathways)}")
time.sleep(0.5)

# Step 2: Get genes for each pathway (sample first 3 for demo)
gene_sets = {}
for pid in list(pathways.keys())[:3]:
    genes_text = requests.get(f"{BASE}/link/genes/{pid}").text
    gene_ids = [line.split("\t")[1] for line in genes_text.strip().split("\n") if line]
    gene_sets[pid] = gene_ids
    print(f"  {pid}: {len(gene_ids)} genes")
    time.sleep(0.5)

# Step 3: Convert to NCBI Gene IDs for enrichment tools
# (use kegg_conv for bulk conversion)

Workflow: Compound-Pathway-Reaction Analysis

Trace a compound through metabolic reactions and pathways.

import requests
import time

BASE = "https://rest.kegg.jp"

# Step 1: Search for compound
results = requests.get(f"{BASE}/find/compound/glucose").text
print("Compound search:")
for line in results.strip().split("\n")[:3]:
    print(f"  {line}")
time.sleep(0.5)

# Step 2: Find reactions involving glucose (C00031)
reactions = requests.get(f"{BASE}/link/reaction/cpd:C00031").text
rxn_ids = [line.split("\t")[1] for line in reactions.strip().split("\n") if line]
print(f"\nReactions involving glucose: {len(rxn_ids)}")
time.sleep(0.5)

# Step 3: Find pathways for a specific reaction
pathways = requests.get(f"{BASE}/link/pathway/rn:R00299").text
print(f"\nPathways for R00299:")
print(pathways[:300])
time.sleep(0.5)

# Step 4: Get pathway detail
detail = requests.get(f"{BASE}/get/map00010").text
print(f"\nGlycolysis pathway detail (first 500 chars):")
print(detail[:500])

Workflow: Cross-Database ID Integration

Map KEGG identifiers to UniProt, NCBI, and PubChem for multi-database workflows.

import requests
import time

BASE = "https://rest.kegg.jp"

# Step 1: Convert gene to multiple external IDs
gene = "hsa:7157"  # TP53

uniprot = requests.get(f"{BASE}/conv/uniprot/{gene}").text.strip()
print(f"UniProt: {uniprot}")
time.sleep(0.5)

ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/{gene}").text.strip()
print(f"NCBI Gene: {ncbi}")
time.sleep(0.5)

# Step 2: Get protein sequence from KEGG
fasta = requests.get(f"{BASE}/get/{gene}/aaseq").text
print(f"\nProtein sequence (first 200 chars):\n{fasta[:200]}")
time.sleep(0.5)

# Step 3: Convert compounds to PubChem CIDs
cpd_conv = requests.get(f"{BASE}/conv/pubchem/cpd:C00002").text.strip()  # ATP
print(f"\nATP PubChem: {cpd_conv}")

Key Parameters

Best Practices

1. Add delays between batch requests: No explicit rate limit, but time.sleep(0.5) between requests prevents throttling and is courteous to the shared academic resource.

2. Anti-pattern — fetching all entries without filtering: Use kegg_list to enumerate IDs first, then kegg_get for specific entries. Avoid downloading entire databases when you need a subset.

3. Parse tab-delimited output consistently: All KEGG responses use \t as field separator and \n as record separator. Always .strip() before splitting.

4. Respect the 10-entry batch limit: kegg_get, kegg_list, kegg_conv, kegg_link, kegg_ddi accept max 10 entries (joined with +). Image/KGML/JSON formats accept only 1.

5. Use organism-specific pathway IDs: hsa00010 (human glycolysis) returns organism-specific gene mappings; map00010 (reference) returns generic entries. Always prefer organism-specific when analyzing a known organism.

6. Cache frequently-used conversions: Full organism ID conversions (kegg_conv('ncbi-geneid', 'hsa')) return large results. Cache locally rather than repeating.

Common Recipes

Recipe: Parse KEGG Flat-File Entry

def parse_kegg_entry(text):
    """Parse a KEGG flat-file entry into a dictionary."""
    entry = {}
    current_key = None
    for line in text.split("\n"):
        if line.startswith("///"):
            break
        if line[:12].strip():  # New field
            current_key = line[:12].strip()
            entry[current_key] = line[12:].strip()
        elif current_key:  # Continuation
            entry[current_key] += "\n" + line[12:].strip()
    return entry

import requests
pathway = requests.get("https://rest.kegg.jp/get/hsa00010").text
parsed = parse_kegg_entry(pathway)
print(f"Name: {parsed.get('NAME', 'N/A')}")
print(f"Description: {parsed.get('DESCRIPTION', 'N/A')[:200]}")

Recipe: Organism Comparison

import requests
import time

BASE = "https://rest.kegg.jp"

organisms = {"hsa": "Human", "mmu": "Mouse", "sce": "Yeast"}
pathway = "00010"  # Glycolysis

for org, name in organisms.items():
    genes = requests.get(f"{BASE}/link/genes/{org}{pathway}").text
    count = len([l for l in genes.strip().split("\n") if l])
    print(f"{name} ({org}): {count} genes in Glycolysis")
    time.sleep(0.5)
# Human (hsa): 68 genes in Glycolysis
# Mouse (mmu): 67 genes in Glycolysis
# Yeast (sce): 31 genes in Glycolysis

Recipe: Build Gene-to-Pathway Mapping Table

import requests
import time

BASE = "https://rest.kegg.jp"

# Get all human gene-pathway links
links = requests.get(f"{BASE}/link/pathway/hsa").text
gene_pathways = {}
for line in links.strip().split("\n"):
    if not line:
        continue
    gene, pathway = line.split("\t")
    gene_pathways.setdefault(gene, []).append(pathway.replace("path:", ""))

print(f"Genes with pathway annotations: {len(gene_pathways)}")
# Show top genes by pathway count
top = sorted(gene_pathways.items(), key=lambda x: -len(x[1]))[:5]
for gene, paths in top:
    print(f"  {gene}: {len(paths)} pathways")

Troubleshooting

Related Skills

• gget-genomic-databases — unified Python interface to Ensembl, NCBI, UniProt; use for gene-level queries when KEGG pathway context isn't needed
• biopython-molecular-biology — BioPython's Bio.KEGG module provides an alternative Python API for KEGG parsing
• pubchem-compound-search — for compound property lookups beyond KEGG's structural data; use kegg_conv('pubchem', ...) to bridge IDs

References

• KEGG REST API documentation — official API specification
• KEGG website — pathway browser, KEGG Mapper, BlastKOALA
• KEGG organism codes — full list of 3-4 letter organism codes
• Kanehisa, M. et al. (2023) "KEGG for taxonomy-based analysis of pathways and genomes" Nucleic Acids Research 51:D483-D489