reactome-database

# 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:** ```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 event_id = "R-HSA-69278" response = requests.get( f"https://reactome.org/ContentService/data/event/{event_id}/participatingPhysicalEntities" ) molecules = response.json() ``` ### 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 # 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 --- > Converted and distributed by [TomeVault](https://tomevault.io) | [Claim this content](https://tomevault.io/claim/davila7/claude-code-templates) <!-- tomevault:2.0:skill_md:2026-04-05 -->