Reactome API Reference

Reactome API Reference

This document provides comprehensive reference information for Reactome's REST APIs.

Base URLs

• Content Service: https://reactome.org/ContentService
• Analysis Service: https://reactome.org/AnalysisService

Content Service API

The Content Service provides access to Reactome's curated pathway data through REST endpoints.

Database Information

Get Database Version

GET /data/database/version

Response: Plain text containing the database version number

Example:

import requests
response = requests.get("https://reactome.org/ContentService/data/database/version")
print(response.text)  # e.g., "94"

Get Database Name

GET /data/database/name

Response: Plain text containing the database name

Entity Queries

Query Entity by ID

GET /data/query/{id}

Parameters:

• id (path): Stable identifier or database ID (e.g., "R-HSA-69278")

Response: JSON object containing full entity information including:

• stId: Stable identifier
• displayName: Human-readable name
• schemaClass: Entity type (Pathway, Reaction, Complex, etc.)
• species: Array of species information
• Additional type-specific fields

Example:

import requests
response = requests.get("https://reactome.org/ContentService/data/query/R-HSA-69278")
pathway = response.json()
print(f"Pathway: {pathway['displayName']}")
print(f"Species: {pathway['species'][0]['displayName']}")

Query Entity Attribute

GET /data/query/{id}/{attribute}

Parameters:

• id (path): Entity identifier
• attribute (path): Specific attribute name (e.g., "displayName", "compartment")

Response: JSON or plain text depending on attribute type

Example:

response = requests.get("https://reactome.org/ContentService/data/query/R-HSA-69278/displayName")
name = response.text

Pathway Queries

Get Pathway Entities

GET /data/event/{id}/participatingPhysicalEntities

Parameters:

• id (path): Pathway or reaction stable identifier

Response: JSON array of physical entities (proteins, complexes, small molecules) participating in the pathway

Example:

response = requests.get(
    "https://reactome.org/ContentService/data/event/R-HSA-69278/participatingPhysicalEntities"
)
entities = response.json()
for entity in entities:
    print(f"{entity['stId']}: {entity['displayName']} ({entity['schemaClass']})")

Get Contained Events

GET /data/pathway/{id}/containedEvents

Parameters:

• id (path): Pathway stable identifier

Response: JSON array of events (reactions, subpathways) contained within the pathway

Search Queries

Search by Name

GET /data/query?name={query}

Parameters:

• name (query): Search term

Response: JSON array of matching entities

Example:

response = requests.get(
    "https://reactome.org/ContentService/data/query",
    params={"name": "glycolysis"}
)
results = response.json()

Analysis Service API

The Analysis Service performs pathway enrichment and expression analysis.

Submit Analysis

Submit Identifiers (POST)

POST /identifiers/
POST /identifiers/projection/  # Map to human pathways only

Headers:

• Content-Type: text/plain

Body:

• For overrepresentation: Plain text list of identifiers (one per line)
• For expression analysis: TSV format with header starting with "#"

Expression data format:

#Gene	Sample1	Sample2	Sample3
TP53	2.5	3.1	2.8
BRCA1	1.2	1.5	1.3

Response: JSON object containing:

{
  "summary": {
    "token": "MzUxODM3NTQzMDAwMDA1ODI4MA==",
    "type": "OVERREPRESENTATION",
    "species": "9606",
    "sampleName": null,
    "fileName": null,
    "text": true
  },
  "pathways": [
    {
      "stId": "R-HSA-69278",
      "name": "Cell Cycle, Mitotic",
      "species": {
        "name": "Homo sapiens",
        "taxId": "9606"
      },
      "entities": {
        "found": 15,
        "total": 450,
        "pValue": 0.0000234,
        "fdr": 0.00156
      },
      "reactions": {
        "found": 12,
        "total": 342
      }
    }
  ],
  "resourceSummary": [
    {
      "resource": "TOTAL",
      "pathways": 25
    }
  ]
}

Example:

import requests

# Overrepresentation analysis
identifiers = ["TP53", "BRCA1", "EGFR", "MYC", "CDK1"]
data = "\n".join(identifiers)

response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/",
    headers={"Content-Type": "text/plain"},
    data=data
)

result = response.json()
token = result["summary"]["token"]

# Process pathways
for pathway in result["pathways"]:
    print(f"Pathway: {pathway['name']}")
    print(f"  Found: {pathway['entities']['found']}/{pathway['entities']['total']}")
    print(f"  p-value: {pathway['entities']['pValue']:.6f}")
    print(f"  FDR: {pathway['entities']['fdr']:.6f}")

Submit File (Form Upload)

POST /identifiers/form/

Content-Type: multipart/form-data

Parameters:

• file: File containing identifiers or expression data

Submit URL

POST /identifiers/url/

Parameters:

• url: URL pointing to data file

Retrieve Analysis Results

Get Results by Token

GET /token/{token}
GET /token/{token}/projection/  # With species projection

Parameters:

• token (path): Analysis token returned from submission

Response: Same structure as initial analysis response

Example:

token = "MzUxODM3NTQzMDAwMDA1ODI4MA=="
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}")
results = response.json()

Note: Tokens are valid for 7 days

Filter Results

GET /token/{token}/filter/pathways?resource={resource}

Parameters:

• token (path): Analysis token
• resource (query): Resource filter (e.g., "TOTAL", "UNIPROT", "ENSEMBL")

Download Results

Download as CSV

GET /download/{token}/pathways/{resource}/result.csv

Download Mapping

GET /download/{token}/entities/found/{resource}/mapping.tsv

Supported Identifiers

Reactome automatically detects and processes various identifier types:

Proteins and Genes

• UniProt: P04637
• Gene Symbol: TP53
• Ensembl: ENSG00000141510
• EntrezGene: 7157
• RefSeq: NM_000546
• OMIM: 191170

Small Molecules

• ChEBI: CHEBI:15377
• KEGG Compound: C00031
• PubChem: 702

Other

• miRBase: hsa-miR-21
• InterPro: IPR011616

Response Formats

JSON Objects

Entity objects contain standardized fields:

{
  "stId": "R-HSA-69278",
  "displayName": "Cell Cycle, Mitotic",
  "schemaClass": "Pathway",
  "species": [
    {
      "dbId": 48887,
      "displayName": "Homo sapiens",
      "taxId": "9606"
    }
  ],
  "isInDisease": false
}

TSV Format

For bulk queries, TSV returns:

stId	displayName	schemaClass
R-HSA-69278	Cell Cycle, Mitotic	Pathway
R-HSA-69306	DNA Replication	Pathway

Error Responses

HTTP Status Codes

• 200: Success
• 400: Bad Request (invalid parameters)
• 404: Not Found (invalid ID)
• 415: Unsupported Media Type
• 500: Internal Server Error

Error JSON Structure

{
  "code": 404,
  "reason": "NOT_FOUND",
  "messages": ["Pathway R-HSA-INVALID not found"]
}

Rate Limiting

Reactome does not currently enforce strict rate limits, but consider:

• Implementing reasonable delays between requests
• Using batch operations when available
• Caching results when appropriate
• Respecting the 7-day token validity period

Best Practices

1. Use Analysis Tokens

Store and reuse analysis tokens to avoid redundant computation:

# Store token after analysis
token = result["summary"]["token"]
save_token(token)  # Save to file or database

# Retrieve results later
result = requests.get(f"https://reactome.org/AnalysisService/token/{token}")

2. Batch Queries

Submit multiple identifiers in a single request rather than individual queries:

# Good: Single batch request
identifiers = ["TP53", "BRCA1", "EGFR"]
result = analyze_batch(identifiers)

# Avoid: Multiple individual requests
# for gene in genes:
#     result = analyze_single(gene)  # Don't do this

3. Handle Species Appropriately

Use /projection/ endpoints to map non-human identifiers to human pathways:

# For mouse genes, project to human pathways
response = requests.post(
    "https://reactome.org/AnalysisService/identifiers/projection/",
    headers={"Content-Type": "text/plain"},
    data=mouse_genes
)

4. Process Large Result Sets

For analyses returning many pathways, filter by significance:

significant_pathways = [
    p for p in result["pathways"]
    if p["entities"]["fdr"] < 0.05
]

Integration Examples

Complete Analysis Workflow

import requests
import json

def analyze_gene_list(genes, output_file="analysis_results.json"):
    """
    Perform pathway enrichment analysis on a list of genes
    """
    # Submit analysis
    data = "\n".join(genes)
    response = requests.post(
        "https://reactome.org/AnalysisService/identifiers/",
        headers={"Content-Type": "text/plain"},
        data=data
    )

    if response.status_code != 200:
        raise Exception(f"Analysis failed: {response.text}")

    result = response.json()
    token = result["summary"]["token"]

    # Filter significant pathways (FDR < 0.05)
    significant = [
        p for p in result["pathways"]
        if p["entities"]["fdr"] < 0.05
    ]

    # Save results
    with open(output_file, "w") as f:
        json.dump({
            "token": token,
            "total_pathways": len(result["pathways"]),
            "significant_pathways": len(significant),
            "pathways": significant
        }, f, indent=2)

    # Generate browser URL for top pathway
    if significant:
        top_pathway = significant[0]
        url = f"https://reactome.org/PathwayBrowser/#{top_pathway['stId']}&DTAB=AN&ANALYSIS={token}"
        print(f"View top result: {url}")

    return result

# Usage
genes = ["TP53", "BRCA1", "BRCA2", "CDK1", "CDK2"]
result = analyze_gene_list(genes)

Additional Resources

• Interactive API Documentation: https://reactome.org/dev/content-service
• Analysis Service Docs: https://reactome.org/dev/analysis
• User Guide: https://reactome.org/userguide
• Data Downloads: https://reactome.org/download-data