### Reactome Database
Works with
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..."
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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:
Fetches 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:
Restart Cursor to activate reactome-database. Access via /reactome-database in your agent's command palette.
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.
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| 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.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 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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