Reactome Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The Reactome database skill provides a command-line interface to the Reactome Analysis and Content Services. It enables researchers to perform pathway enrichment analysis on gene or protein lists, map identifiers across species, navigate pathway hierarchies, and retrieve detailed information about reactions and participants. The tool supports advanced features like diagram generation, cross-reference mapping, and token-based result management for large datasets.
What it does
- Performs overrepresentation and expression analysis on gene or protein lists.
- Maps biological identifiers across different species and databases.
- Navigates pathway hierarchies and retrieves reaction-level details.
- Exports pathway and reaction diagrams in PNG or SVG formats.
- Manages analysis results using persistent tokens for later retrieval.
- Searches the Reactome knowledgebase for specific biological entities.
When to use it
- When you need to identify statistically significant biological pathways for a list of genes.
- When you need to visualize gene involvement in specific cellular processes.
- When you need to map gene symbols or protein IDs to Reactome stable identifiers.
- When you are exploring the hierarchical structure of biological pathways.
- When you need to retrieve detailed participant lists for a specific reaction.
Advantages
- Provides direct access to curated, open-source biological pathway data.
- Supports automated analysis workflows for large-scale gene lists.
- Allows for visual pathway exploration through diagram exports.
- Facilitates cross-species comparisons and identifier mapping.
- Enables efficient data handling through token-based result retrieval.
Usage examples
- “Perform a pathway enrichment analysis on the following gene list: TP53, BRCA1, EGFR.”
- The agent runs the analysis service and saves the enrichment results, including p-values and FDR, to a JSON file.
- “Export a diagram for the Mitotic Cell Cycle pathway and highlight TP53.”
- The agent generates a PNG diagram of the specified pathway with the TP53 protein highlighted.
- “Find all reaction participants for the reaction R-HSA-6804194.”
- The agent retrieves and saves the list of inputs, outputs, and catalysts for the specified reaction.
- “Map the gene symbol TP53 to its Reactome cross-references.”
- The agent queries the database and returns a JSON file containing the cross-reference mapping for the identifier.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google-deepmind/reactome-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip reactome-database.zip -d .agents/skills
Documentation by Google DeepMind
Reactome Analysis & Content Service
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.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 useR-HSA-69278.
Core Rules
Always use
--output: Every subcommand requires--output <file>to write results to a file. Never rely on stdout for large results.Default species is Homo sapiens: Use
--speciesto override.Tokens expire after 7 days: Store tokens from analysis results to retrieve them later without re-submitting data.
Use
--fdrand--pvalueto filter: Enrichment results can be overwhelming. Filter with--fdr 0.05or--pvalue 0.01to focus on statistically significant pathways.Identifier formats: Reactome auto-detects identifiers including gene symbols (TP53), UniProt (P04637), Ensembl (ENSG00000141510), ChEBI, OMIM, EntrezGene, and many more.
Handle large outputs: For commands that return large data (like
species-comparison), use the--summaryflag to truncate lists and avoid exceeding workspace file size limits (1MB).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.jsonTo list all available subcommands and flags, run:
uv run scripts/reactome_analysis.py --helpUse --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.json2. 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.json3. 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.jsonCommon 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.json5. 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.csv6. 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.json7. 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.json8. 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.json9. 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.json10. 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.png11. 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.json12. Search
uv run scripts/reactome_analysis.py search --query "TP53 apoptosis" --output /tmp/results.json13. Query Entry by ID
uv run scripts/reactome_analysis.py query --id R-HSA-69278 --output /tmp/entry.json14. 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.jsonRecipe: Interpreting Gene Set Enrichment
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.jsonInspect 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.jsonVisualise — 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.pngCheck hierarchy — navigate up to see broader biological context:
bash uv run scripts/reactome_analysis.py event-ancestors --id R-HSA-69278 --output /tmp/ancestors.jsonCross-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.
Files
- ATTRIBUTION.mdAdded by AutoGineer916 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md10.8 KiB
- references/api_reference.md4.1 KiB
- references/citation.bib2.2 KiB
- scripts/reactome_analysis.pyScript30.5 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
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Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/reactome_database · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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