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Reactome Database

  • 1.3k installs
  • 2.6k repo stars
  • Updated July 7, 2026
  • google-deepmind/science-skills

Reactome Database is a Claude Code skill that queries Reactome's AnalysisService REST API for pathway enrichment and identifier analysis for developers building genomics, drug discovery, or bioinformatics features.

About

Reactome Database is a Claude Code skill that documents the Reactome AnalysisService API at https://reactome.org/AnalysisService for programmatic pathway analysis. It covers database name and version GET endpoints, single-identifier analysis with species and disease filters, Homo sapiens projection routes, and POST batch identifier analysis with pagination and sorting parameters. Developers reach for Reactome Database when agents or backend services must map gene or protein IDs to pathways, run enrichment from posted identifier lists, or embed Reactome results in drug-discovery and genomics pipelines without manually browsing the web UI.

  • 13 token-based result retrieval endpoints for full analysis, filtering, and downloading
  • 6 batch identifier analysis endpoints supporting POST with text, file upload, or URL
  • 4 identifier lookup endpoints with optional projection to Homo Sapiens
  • 2 simple database metadata endpoints returning name and version
  • Supports both overrepresentation analysis and expression data formats

Reactome Database by the numbers

  • 1,261 all-time installs (skills.sh)
  • +168 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #358 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/google-deepmind/science-skills --skill reactome-database

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Installs1.3k
repo stars2.6k
Security audit3 / 3 scanners passed
Last updatedJuly 7, 2026
Repositorygoogle-deepmind/science-skills

How do you query Reactome pathways from code?

Query Reactome's biological pathway database directly from agents or scripts for genomics, drug discovery, or bioinformatics features.

Who is it for?

Bioinformatics and ML engineers integrating Reactome pathway enrichment into agents, pipelines, or backend APIs for genomics and drug-discovery features.

Skip if: General web developers building unrelated CRUD apps without biological identifier analysis requirements.

When should I use this skill?

The user asks to query Reactome, analyze gene or protein identifiers, run pathway enrichment, or integrate Reactome AnalysisService into scripts or agents.

What you get

HTTP requests to AnalysisService endpoints, parsed pathway enrichment responses, and batch identifier analysis results with species projection.

  • api integration snippets
  • pathway enrichment response parsing

By the numbers

  • Documents Reactome AnalysisService database, identifier, and batch identifier endpoint groups across the public REST API

Files

SKILL.mdMarkdownGitHub ↗

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.

Related skills

How it compares

Pick Reactome Database over generic REST integration skills when the task is specifically Reactome pathway enrichment and identifier batch analysis.

FAQ

Which Reactome API does the skill document?

Reactome Database documents the Reactome AnalysisService REST API, including database metadata GET routes, single-identifier analysis, Homo sapiens projection, and POST batch identifier analysis with parameters like species, includeDisease, pageSize, and sortBy.

When should developers use Reactome Database?

Developers should use Reactome Database when building genomics, drug-discovery, or bioinformatics features that need programmatic pathway enrichment from gene or protein identifiers instead of manual Reactome web UI lookups.

Is Reactome Database safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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