Reactome Database — Biological Pathway Queries & Enrichment Analysis

Overview

Reactome is an open-source, curated database of biological pathways and reactions for 16+ species. It provides two REST APIs: the Content Service for querying pathway data, entities, and hierarchy, and the Analysis Service for gene/protein list enrichment and expression data overlay. All endpoints return JSON (default) or other formats and require no authentication.

When to Use

Querying pathway details by stable ID (e.g., R-HSA-69620 for Cell Cycle)
Searching for pathways, reactions, or entities by keyword
Running gene list enrichment analysis (over-representation) against Reactome pathways
Retrieving pathway hierarchy and containment relationships
Mapping identifiers across databases (UniProt, Ensembl, NCBI, ChEBI)
Getting species-specific pathway data (human, mouse, rat, and 13+ other organisms)
Retrieving analysis results by token for sharing or re-filtering
Building pathway context for multi-omics integration workflows
For KEGG metabolic pathways and cross-database ID conversion, use kegg-database instead
For protein-protein interaction networks, use string-database-ppi instead
For a Python wrapper with caching, consider reactome2py (pip install reactome2py)

Prerequisites

pip install requests

API constraints:

No authentication required — all endpoints are public
No documented hard rate limit — add time.sleep(0.5) between batch requests to be respectful
Content Service base URL: https://reactome.org/ContentService
Analysis Service base URL: https://reactome.org/AnalysisService
Identifier input: gene/protein lists accept UniProt IDs, Ensembl gene IDs, NCBI Gene IDs, HGNC symbols, ChEBI IDs, miRBase IDs, KEGG IDs, and more

Quick Start

import requests
import time

CONTENT = "https://reactome.org/ContentService"
ANALYSIS = "https://reactome.org/AnalysisService"

def reactome_get(base, path, params=None):
    """Generic Reactome REST API caller. Returns JSON or raises."""
    resp = requests.get(f"{base}{path}", params=params)
    resp.raise_for_status()
    try:
        return resp.json()
    except ValueError:
        return resp.text

# Check database version
version = reactome_get(CONTENT, "/data/database/version")
print(f"Reactome version: {version}")

# Query a pathway
pathway = reactome_get(CONTENT, "/data/query/R-HSA-69620")
print(f"Pathway: {pathway['displayName']}")
print(f"Species: {pathway['speciesName']}")
time.sleep(0.5)

# Search for pathways
results = reactome_get(CONTENT, "/search/query", params={"query": "apoptosis", "types": "Pathway"})
print(f"Found {results['found']} results for 'apoptosis'")

Core API

1. Pathway & Entity Queries

Retrieve detailed information about pathways, reactions, and biological entities by stable ID. Uses reactome_get helper from Quick Start.

# Query pathway by stable ID
pathway = reactome_get(CONTENT, "/data/query/R-HSA-69620")
print(f"Name: {pathway['displayName']}")
print(f"Stable ID: {pathway['stId']}, Species: {pathway['speciesName']}")
print(f"Schema class: {pathway['schemaClass']}")  # Pathway, TopLevelPathway, etc.
time.sleep(0.5)

# Get participating physical entities in a pathway
entities = reactome_get(CONTENT, f"/data/participants/{pathway['stId']}")
print(f"\nParticipating entities: {len(entities)}")
for e in entities[:3]:
    print(f"  {e['displayName']} ({e['schemaClass']})")
time.sleep(0.5)

# Get participating molecules with reference entities (UniProt, ChEBI, etc.)
refs = reactome_get(CONTENT, f"/data/participants/{pathway['stId']}/referenceEntities")
print(f"\nReference entities: {len(refs)}")
for r in refs[:3]:
    print(f"  {r['displayName']} — {r.get('databaseName', 'N/A')}:{r.get('identifier', 'N/A')}")

2. Search & Discovery

Search across Reactome by keyword with faceted filtering.

# Keyword search filtered to Pathways
results = reactome_get(CONTENT, "/search/query", params={
    "query": "cell cycle",
    "types": "Pathway",
    "species": "Homo sapiens",
    "cluster": "true"
})
print(f"Total found: {results['found']}")
for entry in results.get("results", [])[:1]:
    for e in entry.get("entries", [])[:5]:
        print(f"  {e['stId']}: {e['name']}")
time.sleep(0.5)

# Search for proteins/complexes
proteins = reactome_get(CONTENT, "/search/query", params={
    "query": "TP53", "types": "Protein", "species": "Homo sapiens"
})
print(f"\nTP53 protein entries: {proteins['found']}")
time.sleep(0.5)

# Suggest (autocomplete)
suggestions = reactome_get(CONTENT, "/search/suggest", params={"query": "apopt"})
print(f"Suggestions: {suggestions}")

Searchable types: Pathway, Reaction, Protein, Complex, SmallMolecule, Gene, DNA, RNA, Drug, ReferenceEntity

3. Enrichment Analysis

Submit a gene/protein list for over-representation analysis against Reactome pathways.

import requests
import time

ANALYSIS = "https://reactome.org/AnalysisService"

# Gene list (newline-separated identifiers — UniProt, HGNC symbols, Ensembl, etc.)
gene_list = "TP53\nBRCA1\nBRCA2\nATM\nCHEK2\nCDK2\nRB1\nMDM2\nCDKN1A\nBAX"

# Submit for enrichment (POST with text body)
resp = requests.post(
    f"{ANALYSIS}/identifiers/",
    headers={"Content-Type": "text/plain"},
    data=gene_list,
    params={"pageSize": 10, "page": 1, "sortBy": "ENTITIES_FDR", "order": "ASC"}
)
resp.raise_for_status()
result = resp.json()

print(f"Analysis token: {result['summary']['token']}")
print(f"Pathways found: {result['pathwaysFound']}")
print(f"Identifiers found: {result['identifiersNotFound']}")
print(f"\nTop enriched pathways:")
for p in result["pathways"][:5]:
    print(f"  {p['stId']}: {p['name']}")
    print(f"    FDR: {p['entities']['fdr']:.2e}, "
          f"Found: {p['entities']['found']}/{p['entities']['total']}")
time.sleep(0.5)

Analysis accepts: newline-separated identifiers, or tab-separated with expression values (for expression overlay). Supported IDs include UniProt, HGNC symbols, Ensembl, NCBI Gene, ChEBI, miRBase, KEGG, and more.

4. Analysis Results & Filtering

Retrieve previously computed analysis results by token and apply filters.

import requests
import time

ANALYSIS = "https://reactome.org/AnalysisService"

# Re-fetch results by token (from a previous analysis)
token = "MjAyNTA2MTcxMDA3MzRfMQ%3D%3D"  # example — use token from Module 3

# Get results with filtering
results = requests.get(f"{ANALYSIS}/token/{token}", params={
    "pageSize": 20,
    "page": 1,
    "sortBy": "ENTITIES_FDR",
    "species": "Homo sapiens",
    "resource": "TOTAL"  # TOTAL, UNIPROT, ENSEMBL, CHEBI, etc.
})
results.raise_for_status()
data = results.json()
print(f"Token: {data['summary']['token']}")
print(f"Pathways: {data['pathwaysFound']}")
time.sleep(0.5)

# Get identifiers found in a specific pathway
pathway_detail = requests.get(
    f"{ANALYSIS}/token/{token}/found/all/{data['pathways'][0]['stId']}"
)
pathway_detail.raise_for_status()
found = pathway_detail.json()
print(f"\nIdentifiers found in {data['pathways'][0]['name']}:")
for entity in found.get("entities", [])[:5]:
    mapsTo = [m["identifier"] for m in entity.get("mapsTo", [])]
    print(f"  {entity['id']} -> {mapsTo}")

Token persistence: analysis tokens are valid for several hours. Share tokens to let collaborators view the same results without re-running. Filter by resource (TOTAL, UNIPROT, ENSEMBL, CHEBI, etc.) and species.

5. Pathway Hierarchy & Events

Navigate the Reactome pathway hierarchy from top-level pathways down to reactions.

# Top-level pathways for human (9606 = NCBI taxonomy ID)
top = reactome_get(CONTENT, "/data/pathways/top/9606")
print(f"Top-level human pathways: {len(top)}")
for p in top[:5]:
    print(f"  {p['stId']}: {p['displayName']}")
time.sleep(0.5)

# Get contained events (sub-pathways and reactions)
e

reactome-database

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