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reactome-database▌

google-deepmind/science-skills · updated Jun 4, 2026

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

### Reactome Database

  • ›name: "reactome-database"
  • ›description: "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..."
skill.md
name
reactome-database
description
> 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.

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 LICENSE_NOTIFICATION.txt does not already exist in this skill 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.

how to use reactome-database

How to use reactome-database on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add reactome-database
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

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

The skills CLI fetches reactome-database from GitHub repository google-deepmind/science-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/reactome-database

Reload or restart Cursor to activate reactome-database. Access the skill through slash commands (e.g., /reactome-database) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

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Use Cases▌

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

✓

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

✓

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

✓

Improve work quality by 30-40% with less effort

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client with skill support
  • ›Clear understanding of task or problem to solve
  • ›Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • ⚠Expecting perfect results without iteration
  • ⚠Not providing enough context in prompts
  • ⚠Using skill for tasks outside its intended scope
  • ⚠Accepting outputs without review and validation

Best Practices▌

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • −Don't use without understanding skill limitations
  • −Don't skip validation of outputs
  • −Don't share sensitive information in prompts
  • −Don't expect skill to replace human judgment

💡 Pro Tips

  • ★Be specific about desired format and style
  • ★Ask for multiple options to choose from
  • ★Request explanations to understand reasoning
  • ★Combine AI efficiency with human expertise

When to Use This▌

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path▌

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.5★★★★★35 reviews
  • ★★★★★Dev Chawla· Dec 20, 2024

    reactome-database has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Soo Smith· Dec 12, 2024

    reactome-database reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Pratham Ware· Dec 4, 2024

    reactome-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • ★★★★★Sakshi Patil· Nov 23, 2024

    Keeps context tight: reactome-database is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Hana Liu· Nov 15, 2024

    We added reactome-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Carlos Sanchez· Nov 11, 2024

    reactome-database reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Chaitanya Patil· Oct 14, 2024

    Registry listing for reactome-database matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Aisha Kim· Oct 6, 2024

    Solid pick for teams standardizing on skills: reactome-database is focused, and the summary matches what you get after install.

  • ★★★★★Dev Farah· Oct 2, 2024

    I recommend reactome-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Aditi Ramirez· Sep 9, 2024

    reactome-database has been reliable in day-to-day use. Documentation quality is above average for community skills.

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