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.

• 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

• Homo sapiens
  • ID: 9606
• Mus musculus (Mouse)
  • ID: 48892
• Rattus norvegicus (Rat)
  • ID: 48895

• 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

.

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.

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