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

| Type | Format | Example | |------|--------|---------| | Reference pathway | map##### | map00010 (Glycolysis, generic) | | Organism pathway | {org}##### | hsa00010 (Glycolysis, human) | | Gene | {org}:{number} | hsa:7157 (TP53) | | Compound | cpd:C##### | cpd:C00002 (ATP) | | Drug | dr:D##### | dr:D00001 | | Enzyme | ec:{EC_number} | ec:1.1.1.1 | | KO (orthology) | ko:K##### | ko:K00001 |

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, DN