alterlab-reactome

---
name: alterlab-reactome
description: Query the Reactome REST API for pathway analysis, over-representation/enrichment, gene-to-pathway mapping, disease pathways, molecular interactions, and expression analysis. Use when running pathway enrichment on a gene list, mapping genes to curated biological pathways, or exploring disease pathways for systems biology studies. Part of the AlterLab Academic Skills suite.
license: MIT
allowed-tools: Read WebFetch Bash(curl:*) Bash(python:*)
compatibility: Keyless Reactome REST API (no authentication required)
metadata:
    skill-author: AlterLab
    version: "1.0.0"
---

# Reactome Database

## Overview

Reactome is a free, open-source, curated pathway database (thousands of 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:**
```bash
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:**
```python
import requests

response = requests.get("https://reactome.org/ContentService/data/database/version")
version = response.text
print(f"Reactome version: {version}")
```

**Query a specific entity:**
```python
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:**
```python
import requests

# NOTE: the path is /data/participants/{id}/... (NOT /data/event/{id}/...);
# the older /data/event/.../participatingPhysicalEntities now returns 404.
pathway_id = "R-HSA-69278"
response = requests.get(
    f"https://reactome.org/ContentService/data/participants/{pathway_id}/participatingPhysicalEntities"
)
molecules = response.json()
```

**Map a gene/protein to the pathways it participates in:**
```python
import requests

# /data/mapping/{resource}/{id}/pathways  — resource = UniProt, Ensembl, etc.
response = requests.get(
    "https://reactome.org/ContentService/data/mapping/UniProt/P04637/pathways",
    params={"species": "9606"},  # taxId; 9606 = Homo sapiens
)
pathways = response.json()  # list of {stId, displayName, ...}
```
Use a UniProt accession (e.g. `P04637`) or an Ensembl gene ID for the mapping
endpoint. To start from a gene symbol (e.g. `TP53`), either resolve it to a
UniProt/Ensembl ID first, or submit it through the Analysis Service
(`/identifiers/`), which auto-detects symbols and returns the matched pathways.

**Search for an entity by name** (when you don't have a stable ID):
```python
response = requests.get(
    "https://reactome.org/ContentService/search/query",
    params={"query": "glycolysis", "species": "Homo sapiens", "types": "Pathway"},
)
hits = response.json()["results"]  # grouped result clusters
```

### Using reactome2py Package

```python
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:**
```python
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:**
```python
# 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:**
```python
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:

```python
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:

```python
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 metrics
- `summary`: Analysis metadata and token
- `entities`: 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:

```bash
# Query pathway information
python scripts/reactome_query.py query R-HSA-69278

# List participating molecules in a pathway
python scripts/reactome_query.py entities R-HSA-69278

# Search for a pathway by name
python scripts/reactome_query.py search "cell cycle"

# Perform overrepresentation analysis
python scripts/reactome_query.py analyze gene_list.txt

# Get database version
python scripts/reactome_query.py version
```

The script depends only on `requests`; run it with `uv run --with requests scripts/reactome_query.py ...`.

## 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.

## Database Version

Reactome ships quarterly releases; the live version (96 as of this writing,
verified via the API) is the source of truth — don't hardcode counts that go
stale. Fetch the current release and per-type statistics at query time:

```bash
curl -s https://reactome.org/ContentService/data/database/version
```

Release-level content statistics are summarised at
https://reactome.org/about/statistics.