Reactome Database
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add google-deepmind/science-skills --skill reactome-databaseCompatible with
Supported agents
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..."
| 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. |
uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.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.
Reference list for common research organisms:
Reference list for commonly used Reactome pathway stable IDs:
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 useR-HSA-69278.
--output: Every subcommand requires --output <file> to
write results to a file. Never rely on stdout for large results.--species to override.--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.species-comparison), use the --summary flag to truncate lists and avoid
exceeding workspace file size limits (1MB).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.
uv run scripts/reactome_analysis.py db-version --output /tmp/version.json
uv run scripts/reactome_analysis.py db-name --output /tmp/name.json
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
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.
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
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
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
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
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
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
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
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
uv run scripts/reactome_analysis.py search --query "TP53 apoptosis" --output /tmp/results.json
uv run scripts/reactome_analysis.py query --id R-HSA-69278 --output /tmp/entry.json
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
A step-by-step workflow for interpreting gene set enrichment results:
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
Inspect top pathways — examine pathwaysFound, top pathway names,
p-values, and FDR values in the output.
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
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
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
Cross-reference — map identifiers to other databases: bash uv run scripts/reactome_analysis.py xref-mapping --id TP53 --output /tmp/xrefs.json
For detailed API endpoint documentation, see references/api_reference.md.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionreactome-databaseExecute the skills CLI command in your project's root directory to begin installation:
epx add google-deepmind/science-skills --skill reactome-databaseFetches reactome-database from google-deepmind/science-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/reactome-database
Restart Cursor to activate reactome-database. Access via /reactome-databasein your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
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💡 Pro Tips
✓ 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.
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reactome-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
reactome-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
reactome-database is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Keeps context tight: reactome-database is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added reactome-database from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
reactome-database reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for reactome-database matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: reactome-database is focused, and the summary matches what you get after install.
I recommend reactome-database for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
reactome-database has been reliable in day-to-day use. Documentation quality is above average for community skills.
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