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SKILL.md
npx skills add google-deepmind/science-skills --skill reactome-database
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.
npx skills add google-deepmind/science-skills --skill reactome-database
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:
To list all available subcommands and flags, run:
Use --help to verify available subcommands or flags before executing an
unfamiliar command.
Submit a list of identifiers for overrepresentation or expression analysis:
Common options: --page-size (alias --limit), --page (alias --offset),
--sort-by, --order, --resource, --species, --fdr, --pvalue.
Retrieve the molecular participants of a reaction (inputs, outputs, catalysts):
Find which complexes or sets contain a given entity:
Move up (ancestors) or down (contained events) the pathway hierarchy:
Export pathway or reaction diagrams as PNG/SVG, with optional gene highlighting:
Resolve identifiers to Reactome internal IDs and cross-references:
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.