MCPInstall

reactome-database

by google-deepmindData & Databases

Query the Reactome database (Analysis and Content Services). Use when the user asks about pathway analysis, gene list enrichment, retrieving results by token, finding unmapped or not-found identifiers, mapping identifiers, reaction participants (inputs, outputs), pathway hierarchy (including top-level pathways), diagram export, cross-reference mapping, or searching the knowledgebase.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/google-deepmind/science-skills --skill reactome-database
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads reactome-database automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Reactome Analysis & Content Service

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If .licenses/reactome_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://reactome.org/license and https://reactome.org/cite, then (2) create the file recording the notification text and timestamp.

Overview

Reactome is a free, open-source, curated pathway database. This skill wraps both the Analysis Service (https://reactome.org/AnalysisService/) and the Content Service (https://reactome.org/ContentService/) providing pathway enrichment analysis, identifier mapping, reaction details, pathway hierarchy navigation, diagram export, cross-reference mapping, and search.

When to Use This Skill

  • Performing pathway enrichment (overrepresentation) analysis on gene/protein lists
  • Retrieving analysis results using a token from previous enrichment
  • Identifying which genes or proteins were not found in a pathway analysis
  • Analyzing gene expression data against pathway annotations
  • Mapping identifiers to Reactome entities across species
  • Retrieving reaction participants (inputs, outputs, catalysts, regulators)
  • Navigating pathway hierarchy and listing top-level pathways
  • Finding which complexes or sets contain a protein
  • Exporting pathway/reaction diagrams (PNG/SVG) with gene highlighting
  • Cross-referencing identifiers across databases (UniProt, Ensembl, etc.)
  • Searching the Reactome knowledgebase
  • Downloading analysis reports (PDF, CSV, JSON)
  • Comparing pathways across species

Common Species IDs

Reference list for common research organisms:

  • Homo sapiens
    • ID: 9606
  • Mus musculus (Mouse)
    • ID: 48892
  • Rattus norvegicus (Rat)
    • ID: 48895

Common Pathway IDs

Reference list for commonly used Reactome pathway stable IDs:

  • Cell Cycle
    • Stable ID: R-HSA-1640170
    • Notes: Top-level pathway (broad)
  • Cell Cycle, Mitotic
    • Stable ID: R-HSA-69278
    • Notes: Specific sub-pathway — use this for diagrams and drill-downs
  • Immune System
    • Stable ID: R-HSA-168256
    • Notes: Top-level pathway
  • Signal Transduction
    • Stable ID: R-HSA-162582
    • Notes: Top-level pathway
  • Gene Expression
    • Stable ID: R-HSA-74160
    • Notes: Top-level pathway
  • Programmed Cell Death
    • Stable ID: R-HSA-5357801
    • Notes: Top-level pathway

Important: When the user asks for a "Cell Cycle" diagram or analysis, prefer the specific Cell Cycle, Mitotic pathway (R-HSA-69278) unless the user explicitly requests the top-level overview. The examples throughout this document use R-HSA-69278.

Core Rules

  1. Always use --output: Every subcommand requires --output <file> to write results to a file. Never rely on stdout for large results.
  2. Default species is Homo sapiens: Use --species to override.
  3. Tokens expire after 7 days: Store tokens from analysis results to retrieve them later without re-submitting data.
  4. Use --fdr and --pvalue to filter: Enrichment results can be overwhelming. Filter with --fdr 0.05 or --pvalue 0.01 to focus on statistically significant pathways.
  5. Identifier formats: Reactome auto-detects identifiers including gene symbols (TP53), UniProt (P04637), Ensembl (ENSG00000141510), ChEBI, OMIM, EntrezGene, and many more.
  6. Handle large outputs: For commands that return large data (like species-comparison), use the --summary flag to truncate lists and avoid exceeding workspace file size limits (1MB).
  7. Notification: If this skill is used, ensure this is mentioned in the output.

Tool Execution

The CLI tool is at scripts/reactome_analysis.py. Run with uv:

uv run scripts/reactome_analysis.py <command> [options] --output /tmp/out.json

To list all available subcommands and flags, run:

uv run scripts/reactome_analysis.py --help

Use --help to verify available subcommands or flags before executing an unfamiliar command.

Feature Domains

1. Database Info

uv run scripts/reactome_analysis.py db-version --output /tmp/version.json
uv run scripts/reactome_analysis.py db-name --output /tmp/name.json

2. Single Identifier Analysis

uv run scripts/reactome_analysis.py identifier --id TP53 --output /tmp/tp53.json
uv run scripts/reactome_analysis.py identifier-projection --id TP53 --output /tmp/tp53_proj.json

3. Batch Analysis (Enrichment)

Submit a list of identifiers for overrepresentation or expression analysis:

uv run scripts/reactome_analysis.py analyze --data "TP53,BRCA1,EGFR" --output /tmp/enrich.json
uv run scripts/reactome_analysis.py analyze --file genes.txt --output /tmp/enrich.json
uv run scripts/reactome_analysis.py analyze-projection --data "TP53,BRCA1" --output /tmp/proj.json
uv run scripts/reactome_analysis.py analyze --data "TP53,BRCA1" --fdr 0.05 --output /tmp/sig.json

Common options: --page-size (alias --limit), --page (alias --offset), --sort-by, --order, --resource, --species, --fdr, --pvalue.

4. Token-Based Result Retrieval

uv run scripts/reactome_analysis.py token-result --token TOKEN --output /tmp/result.json
uv run scripts/reactome_analysis.py token-not-found --token TOKEN --output /tmp/notfound.json
uv run scripts/reactome_analysis.py token-resources --token TOKEN --output /tmp/resources.json
uv run scripts/reactome_analysis.py token-found-entities --token TOKEN --pathway R-HSA-69278 --output /tmp/found.json
uv run scripts/reactome_analysis.py token-filter-species --token TOKEN --species-filter 9606 --output /tmp/filtered.json
uv run scripts/reactome_analysis.py token-reactions-pathway --token TOKEN --pathway R-HSA-69278 --output /tmp/rxns.json

5. Download Results

uv run scripts/reactome_analysis.py download-result --token TOKEN --output /tmp/full.json
uv run scripts/reactome_analysis.py download-pathways --token TOKEN --output /tmp/pathways.csv
uv run scripts/reactome_analysis.py download-found --token TOKEN --output /tmp/found.csv
uv run scripts/reactome_analysis.py download-not-found --token TOKEN --output /tmp/notfound.csv

6. Identifier Mapping

uv run scripts/reactome_analysis.py mapping --data "TP53,BRCA1" --output /tmp/mapped.json
uv run scripts/reactome_analysis.py mapping-projection --data "TP53" --output /tmp/mapped_proj.json

7. Reaction Participants & Mechanism of Action

Retrieve the molecular participants of a reaction (inputs, outputs, catalysts):

uv run scripts/reactome_analysis.py participants --id R-HSA-6804194 --output /tmp/participants.json
uv run scripts/reactome_analysis.py participating-entities --id R-HSA-6804194 --output /tmp/entities.json

8. Complex & Set Membership

Find which complexes or sets contain a given entity:

uv run scripts/reactome_analysis.py component-of --id R-HSA-69488 --output /tmp/complexes.json

9. Pathway Hierarchy Navigation

Move up (ancestors) or down (contained events) the pathway hierarchy:

uv run scripts/reactome_analysis.py event-ancestors --id R-HSA-69278 --output /tmp/ancestors.json
uv run scripts/reactome_analysis.py contained-events --id R-HSA-69278 --output /tmp/steps.json
uv run scripts/reactome_analysis.py top-pathways --output /tmp/top.json
uv run scripts/reactome_analysis.py low-pathways --id R-HSA-69488 --output /tmp/low.json

10. Diagram Export

Export pathway or reaction diagrams as PNG/SVG, with optional gene highlighting:

uv run scripts/reactome_analysis.py diagram --id R-HSA-69278 --output /tmp/diagram.png
uv run scripts/reactome_analysis.py diagram --id R-HSA-69278 --highlight TP53 --output /tmp/highlighted.png
uv run scripts/reactome_analysis.py diagram --id R-HSA-69278 --format svg --output /tmp/diagram.svg
uv run scripts/reactome_analysis.py reaction-diagram --id R-HSA-6804194 --output /tmp/rxn.png

11. Cross-Reference Mapping

Resolve identifiers to Reactome internal IDs and cross-references:

uv run scripts/reactome_analysis.py xref-mapping --id TP53 --output /tmp/xref.json
uv run scripts/reactome_analysis.py xref-mapping-batch --data "TP53,BRCA1" --output /tmp/xrefs.json

12. Search

uv run scripts/reactome_analysis.py search --query "TP53 apoptosis" --output /tmp/results.json

13. Query Entry by ID

uv run scripts/reactome_analysis.py query --id R-HSA-69278 --output /tmp/entry.json

14. Report & Species Comparison

uv run scripts/reactome_analysis.py report --token TOKEN --output /tmp/report.pdf
uv run scripts/reactome_analysis.py species-comparison --species-id 48892 --output /tmp/species.json
# Use --summary to truncate large output and avoid workspace file size limits
uv run scripts/reactome_analysis.py species-comparison --species-id 48892 --summary --output /tmp/species.json

Recipe: Interpreting Gene Set Enrichment

A step-by-step workflow for interpreting gene set enrichment results:

  1. Submit gene list with projection to human pathways: bash uv run scripts/reactome_analysis.py analyze-projection \ --data "TP53,BRCA1,EGFR,MYC,PTEN" --fdr 0.05 --output /tmp/enrichment.json

  2. Inspect top pathways — examine pathwaysFound, top pathway names, p-values, and FDR values in the output.

  3. Drill into a pathway — get its sub-events and reaction details: bash uv run scripts/reactome_analysis.py contained-events --id R-HSA-69278 --output /tmp/steps.json uv run scripts/reactome_analysis.py participants --id <reaction_id> --output /tmp/parts.json

  4. Visualise — export a diagram with your genes highlighted: bash uv run scripts/reactome_analysis.py diagram --id R-HSA-69278 \ --highlight "TP53,BRCA1" --output /tmp/diagram.png

  5. Check hierarchy — navigate up to see broader biological context: bash uv run scripts/reactome_analysis.py event-ancestors --id R-HSA-69278 --output /tmp/ancestors.json

  6. Cross-reference — map identifiers to other databases: bash uv run scripts/reactome_analysis.py xref-mapping --id TP53 --output /tmp/xrefs.json

Reference

For detailed API endpoint documentation, see references/api_reference.md.

FAQ

What does the reactome-database skill do?

Query the Reactome database (Analysis and Content Services). Use when the user asks about pathway analysis, gene list enrichment, retrieving results by token, finding unmapped or not-found identifiers, mapping identifiers, reaction participants (inputs, outputs), pathway hierarchy (including top-level pathways), diagram export, cross-reference mapping, or searching the knowledgebase.

How do I install reactome-database?

Run npx skills add https://github.com/google-deepmind/science-skills --skill reactome-database in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/reactome-database/ (personal) or .claude/skills/reactome-database/ (project).

Which AI agents can use reactome-database?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is reactome-database safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains reactome-database?

It is published by google-deepmind in google-deepmind/science-skills. It has 2,158 recorded installs.

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