reactome-database
Query Reactome pathways via REST: pathway queries, entity lookup, keyword search, gene list enrichment, hierarchy, cross-refs. Content + Analysis services. Python wrapper: reactome2py. For KEGG use kegg-database; for PPIs use string-database-ppi.
npx skills add BioTender-max/awesome-bio-agent-skills --skill reactome-database --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Describes a toolset that lets an agent query Reactome pathways via REST, perform pathway and entity lookups, keyword searches, and gene list enrichment analyses. It also covers retrieving pathway hierarchy, cross-references, and species-specific data, with guidance for using a Python wrapper reactome2py and alternate databases for KEGG and PPIs. Includes example code and workflows for enrichment, exploration, and expression data overlays.
How it works
- Uses two Reactome REST endpoints: Content Service for querying pathways, entities, and hierarchy; Analysis Service for gene/protein list enrichment and expression data overlays.
- Provides concrete Python snippets and HTTP requests to:
- Query by stable ID and fetch participating entities and reference entities.
- Perform keyword searches with faceted filtering (types, species).
- Submit gene/protein lists to /identifiers/ on the Analysis Service to obtain an enrichment token, then retrieve results and details by token.
- Retrieve pathway hierarchy, contained events, and orthology across species.
- Map identifiers across databases and retrieve species data.
- Specifies input formats (e.g., newline-separated IDs for enrichment; tab-delimited expression data for overlays) and includes example tokens and paths.
- Recommends using time.sleep(0.5) between requests and handling tokens that persist for hours.
When to use it
- When you need to query a pathway by stable ID and inspect its details, species, and components.
- When you want to search Reactome for pathways, reactions, or entities by keyword.
- When you need to run gene list enrichment against Reactome pathways and retrieve an analysis token.
- When you want to explore pathway hierarchy, contained events, and ancestor chains.
- When you need cross-references to external databases and orthologous pathways across species.
What it can touch
- Endpoints: CONTENT (ContentService) at https://reactome.org/ContentService and ANALYSIS (AnalysisService) at https://reactome.org/AnalysisService.
- Identifiers accepted for enrichment include UniProt, HGNC, Ensembl, NCBI Gene, ChEBI, miRBase, KEGG, Ensembl Protein, and more.
- Tools referenced: Python snippets, requests library, and quick-start helper function reactome_get (implied in examples).
Caveats
- Requires no authentication but advises rate-limiting via time.sleep(0.5).
- Tokens from enrichment persist for several hours and can be shared via URL; tokens expire after several hours.
- Documentation emphasizes usage across multiple species and cross-database mapping; no guarantees about specific results beyond examples.
# 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 `stri
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- 1. Pathway & Entity Queries
- 2. Search & Discovery
- 3. Enrichment Analysis
- 4. Analysis Results & Filtering
- 5. Pathway Hierarchy & Events
- 6. Cross-References & Species
- Key Concepts
- Pathway Hierarchy
- Supported Identifiers
pip install requests
What does the reactome-database skill do?
Query Reactome pathways via REST: pathway queries, entity lookup, keyword search, gene list enrichment, hierarchy, cross-refs. Content + Analysis services. Python wrapper: reactome2py. For KEGG use kegg-database; for PPIs use string-database-ppi.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill reactome-database --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.