reactome-database
GitHub用于查询Reactome数据库的REST API，支持通路分析、富集分析、基因-通路映射及分子交互探索。适用于系统生物学研究中的基因列表过表达分析和表达数据解析。
触发场景
安装
npx skills add synthetic-sciences/openscience --skill reactome-database -g -y
SKILL.md
Frontmatter
{
"name": "reactome-database",
"license": "Unknown",
"category": "databases",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies."
}
Reactome Database
Overview
Reactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.
When to Use This Skill
This skill should be used when:
- Performing pathway enrichment analysis on gene or protein lists
- Analyzing gene expression data to identify relevant biological pathways
- Querying specific pathway information, reactions, or molecular interactions
- Mapping genes or proteins to biological pathways and processes
- Exploring disease-related pathways and mechanisms
- Visualizing analysis results in the Reactome Pathway Browser
- Conducting comparative pathway analysis across species
Core Capabilities
Reactome provides two main API services and a Python client library:
1. Content Service - Data Retrieval
Query and retrieve biological pathway data, molecular interactions, and entity information.
Common operations:
- Retrieve pathway information and hierarchies
- Query specific entities (proteins, reactions, complexes)
- Get participating molecules in pathways
- Access database version and metadata
- Explore pathway compartments and locations
API Base URL: https://reactome.org/ContentService
2. Analysis Service - Pathway Analysis
Perform computational analysis on gene lists and expression data.
Analysis types:
- Overrepresentation Analysis: Identify statistically significant pathways from gene/protein lists
- Expression Data Analysis: Analyze gene expression datasets to find relevant pathways
- Species Comparison: Compare pathway data across different organisms
API Base URL: https://reactome.org/AnalysisService
3. reactome2py Python Package
Python client library that wraps Reactome API calls for easier programmatic access.
Installation:
uv pip install reactome2py
Note: The reactome2py package (version 3.0.0, released January 2021) is functional but not actively maintained. For the most up-to-date functionality, consider using direct REST API calls.
Querying Pathway Data
Using Content Service REST API
The Content Service uses REST protocol and returns data in JSON or plain text formats.
Get database version:
import requests
response = requests.get("https://reactome.org/ContentService/data/database/version")
version = response.text
print(f"Reactome version: {version}")
Query a specific entity:
import requests
entity_id = "R-HSA-69278" # Example pathway ID
response = requests.get(f"https://reactome.org/ContentService/data/query/{entity_id}")
data = response.json()
Get participating molecules in a pathway:
import requests
event_id = "R-HSA-69278"
response = requests.get(
f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities"
)
molecules = response.json()
Using reactome2py Package
import reactome2py
from reactome2py import content
# Query pathway information
pathway_info = content.query_by_id("R-HSA-69278")
# Get database version
version = content.get_database_version()
For detailed API endpoints and parameters, refer to references/api_reference.md in this skill.
Performing Pathway Analysis
Overrepresentation Analysis
Submit a list of gene/protein identifiers to find enriched pathways.
Using REST API:
import requests
# Prepare identifier list
identifiers = ["TP53", "BRCA1", "EGFR", "MYC"]
data = "\n".join(identifiers)
# Submit analysis
response = requests.post(
"https://reactome.org/AnalysisService/identifiers/",
headers={"Content-Type": "text/plain"},
data=data
)
result = response.json()
token = result["summary"]["token"] # Save token to retrieve results later
# Access pathways
for pathway in result["pathways"]:
print(f"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})")
Retrieve analysis by token:
# Token is valid for 7 days
response = requests.get(f"https://reactome.org/AnalysisService/token/{token}")
results = response.json()
Expression Data Analysis
Analyze gene expression datasets with quantitative values.
Input format (TSV with header starting with #):
#Gene Sample1 Sample2 Sample3
TP53 2.5 3.1 2.8
BRCA1 1.2 1.5 1.3
EGFR 4.5 4.2 4.8
Submit expression data:
import requests
# Read TSV file
with open("expression_data.tsv", "r") as f:
data = f.read()
response = requests.post(
"https://reactome.org/AnalysisService/identifiers/",
headers={"Content-Type": "text/plain"},
data=data
)
result = response.json()
Species Projection
Map identifiers to human pathways exclusively using the /projection/ endpoint:
response = requests.post(
"https://reactome.org/AnalysisService/identifiers/projection/",
headers={"Content-Type": "text/plain"},
data=data
)
Visualizing Results
Analysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:
token = result["summary"]["token"]
pathway_id = "R-HSA-69278"
url = f"https://reactome.org/PathwayBrowser/#{pathway_id}&DTAB=AN&ANALYSIS={token}"
print(f"View results: {url}")
Working with Analysis Tokens
- Analysis tokens are valid for 7 days
- Tokens allow retrieval of previously computed results without re-submission
- Store tokens to access results across sessions
- Use
GET /token/{TOKEN}endpoint to retrieve results
Data Formats and Identifiers
Supported Identifier Types
Reactome accepts various identifier formats:
- UniProt accessions (e.g., P04637)
- Gene symbols (e.g., TP53)
- Ensembl IDs (e.g., ENSG00000141510)
- EntrezGene IDs (e.g., 7157)
- ChEBI IDs for small molecules
The system automatically detects identifier types.
Input Format Requirements
For overrepresentation analysis:
- Plain text list of identifiers (one per line)
- OR single column in TSV format
For expression analysis:
- TSV format with mandatory header row starting with "#"
- Column 1: identifiers
- Columns 2+: numeric expression values
- Use period (.) as decimal separator
Output Format
All API responses return JSON containing:
pathways: Array of enriched pathways with statistical metricssummary: Analysis metadata and tokenentities: Matched and unmapped identifiers- Statistical values: pValue, FDR (false discovery rate)
Helper Scripts
This skill includes scripts/reactome_query.py, a helper script for common Reactome operations:
# Query pathway information
python scripts/reactome_query.py query R-HSA-69278
# Perform overrepresentation analysis
python scripts/reactome_query.py analyze gene_list.txt
# Get database version
python scripts/reactome_query.py version
Additional Resources
- API Documentation: https://reactome.org/dev
- User Guide: https://reactome.org/userguide
- Documentation Portal: https://reactome.org/documentation
- Data Downloads: https://reactome.org/download-data
- reactome2py Docs: https://reactome.github.io/reactome2py/
For comprehensive API endpoint documentation, see references/api_reference.md in this skill.
Current Database Statistics (Version 94, September 2025)
- 2,825 human pathways
- 16,002 reactions
- 11,630 proteins
- 2,176 small molecules
- 1,070 drugs
- 41,373 literature references
版本历史
- e9844a4 当前 2026-07-11 17:25
