[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-reactome-database":3,"mdc--u4jtcu-key":36,"related-repo-google-deepmind-reactome-database":2685,"related-org-google-deepmind-reactome-database":2792},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"reactome-database","query Reactome pathway and gene data","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.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"google-deepmind","Google DeepMind","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fgoogle-deepmind.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":17,"slug":18,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},"Life Sciences","life-sciences",{"name":23,"slug":24,"type":15},"Bioinformatics","bioinformatics",2333,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills","2026-07-12T07:51:34.288753",null,234,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"GDM Science Skills to speed up agentic scientific workflows with better grounding and higher token efficiency. Integrate insights from AlphaGenome, AFDB, UniProt and 30+ other databases and tools.","https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills\u002Ftree\u002FHEAD\u002Fskills\u002Freactome_database","---\nname: reactome-database\ndescription: >\n  Query the Reactome database (Analysis and Content Services). Use when the user\n  asks about pathway analysis, gene list enrichment, retrieving results by\n  token, finding unmapped or not-found identifiers, mapping identifiers,\n  reaction participants (inputs, outputs), pathway hierarchy (including\n  top-level pathways), diagram export, cross-reference mapping, or searching the\n  knowledgebase.\n---\n\n# Reactome Analysis & Content Service\n\n## Prerequisites\n\n1.  **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure\n    `uv` is installed and on PATH.\n2.  **User Notification**: If .licenses\u002Freactome_database_LICENSE.txt does not\n    already exist in the workspace root directory then (1) prominently notify\n    the user to check the terms at https:\u002F\u002Freactome.org\u002Flicense and\n    https:\u002F\u002Freactome.org\u002Fcite, then (2) create the file recording the\n    notification text and timestamp.\n\n## Overview\n\nReactome is a free, open-source, curated pathway database. This skill wraps both\nthe **Analysis Service** (`https:\u002F\u002Freactome.org\u002FAnalysisService\u002F`) and the\n**Content Service** (`https:\u002F\u002Freactome.org\u002FContentService\u002F`) providing pathway\nenrichment analysis, identifier mapping, reaction details, pathway hierarchy\nnavigation, diagram export, cross-reference mapping, and search.\n\n## When to Use This Skill\n\n-   Performing pathway enrichment (overrepresentation) analysis on gene\u002Fprotein\n    lists\n-   Retrieving analysis results using a token from previous enrichment\n-   Identifying which genes or proteins were not found in a pathway analysis\n-   Analyzing gene expression data against pathway annotations\n-   Mapping identifiers to Reactome entities across species\n-   Retrieving reaction participants (inputs, outputs, catalysts, regulators)\n-   Navigating pathway hierarchy and listing top-level pathways\n-   Finding which complexes or sets contain a protein\n-   Exporting pathway\u002Freaction diagrams (PNG\u002FSVG) with gene highlighting\n-   Cross-referencing identifiers across databases (UniProt, Ensembl, etc.)\n-   Searching the Reactome knowledgebase\n-   Downloading analysis reports (PDF, CSV, JSON)\n-   Comparing pathways across species\n\n## Common Species IDs\n\nReference list for common research organisms:\n\n-   Homo sapiens\n    -   ID: 9606\n-   Mus musculus (Mouse)\n    -   ID: 48892\n-   Rattus norvegicus (Rat)\n    -   ID: 48895\n\n## Common Pathway IDs\n\nReference list for commonly used Reactome pathway stable IDs:\n\n-   Cell Cycle\n    -   Stable ID: R-HSA-1640170\n    -   Notes: Top-level pathway (broad)\n-   Cell Cycle, Mitotic\n    -   Stable ID: R-HSA-69278\n    -   Notes: Specific sub-pathway — use this for diagrams and drill-downs\n-   Immune System\n    -   Stable ID: R-HSA-168256\n    -   Notes: Top-level pathway\n-   Signal Transduction\n    -   Stable ID: R-HSA-162582\n    -   Notes: Top-level pathway\n-   Gene Expression\n    -   Stable ID: R-HSA-74160\n    -   Notes: Top-level pathway\n-   Programmed Cell Death\n    -   Stable ID: R-HSA-5357801\n    -   Notes: Top-level pathway\n\n> **Important**: When the user asks for a \"Cell Cycle\" diagram or analysis,\n> prefer the specific **Cell Cycle, Mitotic** pathway (`R-HSA-69278`) unless the\n> user explicitly requests the top-level overview. The examples throughout this\n> document use `R-HSA-69278`.\n\n## Core Rules\n\n1.  **Always use `--output`**: Every subcommand requires `--output \u003Cfile>` to\n    write results to a file. Never rely on stdout for large results.\n2.  **Default species is Homo sapiens**: Use `--species` to override.\n3.  **Tokens expire after 7 days**: Store tokens from analysis results to\n    retrieve them later without re-submitting data.\n4.  **Use `--fdr` and `--pvalue` to filter**: Enrichment results can be\n    overwhelming. Filter with `--fdr 0.05` or `--pvalue 0.01` to focus on\n    statistically significant pathways.\n5.  **Identifier formats**: Reactome auto-detects identifiers including gene\n    symbols (TP53), UniProt (P04637), Ensembl (ENSG00000141510), ChEBI, OMIM,\n    EntrezGene, and many more.\n6.  **Handle large outputs**: For commands that return large data (like\n    `species-comparison`), use the `--summary` flag to truncate lists and avoid\n    exceeding workspace file size limits (1MB).\n7.  **Notification**: If this skill is used, ensure this is mentioned in the\n    output.\n\n## Tool Execution\n\nThe CLI tool is at `scripts\u002Freactome_analysis.py`. Run with `uv`:\n\n```bash\nuv run scripts\u002Freactome_analysis.py \u003Ccommand> [options] --output \u002Ftmp\u002Fout.json\n```\n\n**To list all available subcommands and flags**, run:\n\n```bash\nuv run scripts\u002Freactome_analysis.py --help\n```\n\nUse `--help` to verify available subcommands or flags before executing an\nunfamiliar command.\n\n## Feature Domains\n\n### 1. Database Info\n\n```bash\nuv run scripts\u002Freactome_analysis.py db-version --output \u002Ftmp\u002Fversion.json\nuv run scripts\u002Freactome_analysis.py db-name --output \u002Ftmp\u002Fname.json\n```\n\n### 2. Single Identifier Analysis\n\n```bash\nuv run scripts\u002Freactome_analysis.py identifier --id TP53 --output \u002Ftmp\u002Ftp53.json\nuv run scripts\u002Freactome_analysis.py identifier-projection --id TP53 --output \u002Ftmp\u002Ftp53_proj.json\n```\n\n### 3. Batch Analysis (Enrichment)\n\nSubmit a list of identifiers for overrepresentation or expression analysis:\n\n```bash\nuv run scripts\u002Freactome_analysis.py analyze --data \"TP53,BRCA1,EGFR\" --output \u002Ftmp\u002Fenrich.json\nuv run scripts\u002Freactome_analysis.py analyze --file genes.txt --output \u002Ftmp\u002Fenrich.json\nuv run scripts\u002Freactome_analysis.py analyze-projection --data \"TP53,BRCA1\" --output \u002Ftmp\u002Fproj.json\nuv run scripts\u002Freactome_analysis.py analyze --data \"TP53,BRCA1\" --fdr 0.05 --output \u002Ftmp\u002Fsig.json\n```\n\nCommon options: `--page-size` (alias `--limit`), `--page` (alias `--offset`),\n`--sort-by`, `--order`, `--resource`, `--species`, `--fdr`, `--pvalue`.\n\n### 4. Token-Based Result Retrieval\n\n```bash\nuv run scripts\u002Freactome_analysis.py token-result --token TOKEN --output \u002Ftmp\u002Fresult.json\nuv run scripts\u002Freactome_analysis.py token-not-found --token TOKEN --output \u002Ftmp\u002Fnotfound.json\nuv run scripts\u002Freactome_analysis.py token-resources --token TOKEN --output \u002Ftmp\u002Fresources.json\nuv run scripts\u002Freactome_analysis.py token-found-entities --token TOKEN --pathway R-HSA-69278 --output \u002Ftmp\u002Ffound.json\nuv run scripts\u002Freactome_analysis.py token-filter-species --token TOKEN --species-filter 9606 --output \u002Ftmp\u002Ffiltered.json\nuv run scripts\u002Freactome_analysis.py token-reactions-pathway --token TOKEN --pathway R-HSA-69278 --output \u002Ftmp\u002Frxns.json\n```\n\n### 5. Download Results\n\n```bash\nuv run scripts\u002Freactome_analysis.py download-result --token TOKEN --output \u002Ftmp\u002Ffull.json\nuv run scripts\u002Freactome_analysis.py download-pathways --token TOKEN --output \u002Ftmp\u002Fpathways.csv\nuv run scripts\u002Freactome_analysis.py download-found --token TOKEN --output \u002Ftmp\u002Ffound.csv\nuv run scripts\u002Freactome_analysis.py download-not-found --token TOKEN --output \u002Ftmp\u002Fnotfound.csv\n```\n\n### 6. Identifier Mapping\n\n```bash\nuv run scripts\u002Freactome_analysis.py mapping --data \"TP53,BRCA1\" --output \u002Ftmp\u002Fmapped.json\nuv run scripts\u002Freactome_analysis.py mapping-projection --data \"TP53\" --output \u002Ftmp\u002Fmapped_proj.json\n```\n\n### 7. Reaction Participants & Mechanism of Action\n\nRetrieve the molecular participants of a reaction (inputs, outputs, catalysts):\n\n```bash\nuv run scripts\u002Freactome_analysis.py participants --id R-HSA-6804194 --output \u002Ftmp\u002Fparticipants.json\nuv run scripts\u002Freactome_analysis.py participating-entities --id R-HSA-6804194 --output \u002Ftmp\u002Fentities.json\n```\n\n### 8. Complex & Set Membership\n\nFind which complexes or sets contain a given entity:\n\n```bash\nuv run scripts\u002Freactome_analysis.py component-of --id R-HSA-69488 --output \u002Ftmp\u002Fcomplexes.json\n```\n\n### 9. Pathway Hierarchy Navigation\n\nMove up (ancestors) or down (contained events) the pathway hierarchy:\n\n```bash\nuv run scripts\u002Freactome_analysis.py event-ancestors --id R-HSA-69278 --output \u002Ftmp\u002Fancestors.json\nuv run scripts\u002Freactome_analysis.py contained-events --id R-HSA-69278 --output \u002Ftmp\u002Fsteps.json\nuv run scripts\u002Freactome_analysis.py top-pathways --output \u002Ftmp\u002Ftop.json\nuv run scripts\u002Freactome_analysis.py low-pathways --id R-HSA-69488 --output \u002Ftmp\u002Flow.json\n```\n\n### 10. Diagram Export\n\nExport pathway or reaction diagrams as PNG\u002FSVG, with optional gene highlighting:\n\n```bash\nuv run scripts\u002Freactome_analysis.py diagram --id R-HSA-69278 --output \u002Ftmp\u002Fdiagram.png\nuv run scripts\u002Freactome_analysis.py diagram --id R-HSA-69278 --highlight TP53 --output \u002Ftmp\u002Fhighlighted.png\nuv run scripts\u002Freactome_analysis.py diagram --id R-HSA-69278 --format svg --output \u002Ftmp\u002Fdiagram.svg\nuv run scripts\u002Freactome_analysis.py reaction-diagram --id R-HSA-6804194 --output \u002Ftmp\u002Frxn.png\n```\n\n### 11. Cross-Reference Mapping\n\nResolve identifiers to Reactome internal IDs and cross-references:\n\n```bash\nuv run scripts\u002Freactome_analysis.py xref-mapping --id TP53 --output \u002Ftmp\u002Fxref.json\nuv run scripts\u002Freactome_analysis.py xref-mapping-batch --data \"TP53,BRCA1\" --output \u002Ftmp\u002Fxrefs.json\n```\n\n### 12. Search\n\n```bash\nuv run scripts\u002Freactome_analysis.py search --query \"TP53 apoptosis\" --output \u002Ftmp\u002Fresults.json\n```\n\n### 13. Query Entry by ID\n\n```bash\nuv run scripts\u002Freactome_analysis.py query --id R-HSA-69278 --output \u002Ftmp\u002Fentry.json\n```\n\n### 14. Report & Species Comparison\n\n```bash\nuv run scripts\u002Freactome_analysis.py report --token TOKEN --output \u002Ftmp\u002Freport.pdf\nuv run scripts\u002Freactome_analysis.py species-comparison --species-id 48892 --output \u002Ftmp\u002Fspecies.json\n# Use --summary to truncate large output and avoid workspace file size limits\nuv run scripts\u002Freactome_analysis.py species-comparison --species-id 48892 --summary --output \u002Ftmp\u002Fspecies.json\n```\n\n## Recipe: Interpreting Gene Set Enrichment\n\nA step-by-step workflow for interpreting gene set enrichment results:\n\n1.  **Submit gene list** with projection to human pathways: `bash uv run\n    scripts\u002Freactome_analysis.py analyze-projection \\ --data\n    \"TP53,BRCA1,EGFR,MYC,PTEN\" --fdr 0.05 --output \u002Ftmp\u002Fenrichment.json`\n\n2.  **Inspect top pathways** — examine `pathwaysFound`, top pathway names,\n    p-values, and FDR values in the output.\n\n3.  **Drill into a pathway** — get its sub-events and reaction details: `bash uv\n    run scripts\u002Freactome_analysis.py contained-events --id R-HSA-69278 --output\n    \u002Ftmp\u002Fsteps.json uv run scripts\u002Freactome_analysis.py participants --id\n    \u003Creaction_id> --output \u002Ftmp\u002Fparts.json`\n\n4.  **Visualise** — export a diagram with your genes highlighted: `bash uv run\n    scripts\u002Freactome_analysis.py diagram --id R-HSA-69278 \\ --highlight\n    \"TP53,BRCA1\" --output \u002Ftmp\u002Fdiagram.png`\n\n5.  **Check hierarchy** — navigate up to see broader biological context: `bash\n    uv run scripts\u002Freactome_analysis.py event-ancestors --id R-HSA-69278\n    --output \u002Ftmp\u002Fancestors.json`\n\n6.  **Cross-reference** — map identifiers to other databases: `bash uv run\n    scripts\u002Freactome_analysis.py xref-mapping --id TP53 --output\n    \u002Ftmp\u002Fxrefs.json`\n\n## Reference\n\nFor detailed API endpoint documentation, see\n[references\u002Fapi_reference.md](references\u002Fapi_reference.md).\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,57,120,126,161,167,236,242,247,289,295,300,408,444,450,593,599,619,679,689,716,728,734,741,808,814,897,903,908,1104,1180,1186,1447,1453,1608,1614,1712,1718,1723,1805,1811,1816,1861,1867,1872,2019,2025,2030,2202,2208,2213,2302,2308,2362,2368,2412,2418,2550,2556,2561,2662,2668,2679],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"reactome-analysis-content-service",[47],{"type":48,"value":49},"text","Reactome Analysis & Content Service",{"type":42,"tag":51,"props":52,"children":54},"h2",{"id":53},"prerequisites",[55],{"type":48,"value":56},"Prerequisites",{"type":42,"tag":58,"props":59,"children":60},"ol",{},[61,92],{"type":42,"tag":62,"props":63,"children":64},"li",{},[65,76,78,83,85,90],{"type":42,"tag":66,"props":67,"children":68},"strong",{},[69],{"type":42,"tag":70,"props":71,"children":73},"code",{"className":72},[],[74],{"type":48,"value":75},"uv",{"type":48,"value":77},": Read the ",{"type":42,"tag":70,"props":79,"children":81},{"className":80},[],[82],{"type":48,"value":75},{"type":48,"value":84}," skill and follow its Setup instructions to ensure\n",{"type":42,"tag":70,"props":86,"children":88},{"className":87},[],[89],{"type":48,"value":75},{"type":48,"value":91}," is installed and on PATH.",{"type":42,"tag":62,"props":93,"children":94},{},[95,100,102,110,112,118],{"type":42,"tag":66,"props":96,"children":97},{},[98],{"type":48,"value":99},"User Notification",{"type":48,"value":101},": If .licenses\u002Freactome_database_LICENSE.txt does not\nalready exist in the workspace root directory then (1) prominently notify\nthe user to check the terms at ",{"type":42,"tag":103,"props":104,"children":108},"a",{"href":105,"rel":106},"https:\u002F\u002Freactome.org\u002Flicense",[107],"nofollow",[109],{"type":48,"value":105},{"type":48,"value":111}," and\n",{"type":42,"tag":103,"props":113,"children":116},{"href":114,"rel":115},"https:\u002F\u002Freactome.org\u002Fcite",[107],[117],{"type":48,"value":114},{"type":48,"value":119},", then (2) create the file recording the\nnotification text and timestamp.",{"type":42,"tag":51,"props":121,"children":123},{"id":122},"overview",[124],{"type":48,"value":125},"Overview",{"type":42,"tag":127,"props":128,"children":129},"p",{},[130,132,137,139,145,147,152,153,159],{"type":48,"value":131},"Reactome is a free, open-source, curated pathway database. 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\u002Ftmp\u002Fspecies.json\n",[2422],{"type":42,"tag":70,"props":2423,"children":2424},{"__ignoreMap":625},[2425,2462,2501,2510],{"type":42,"tag":631,"props":2426,"children":2427},{"class":633,"line":634},[2428,2432,2436,2440,2445,2449,2453,2457],{"type":42,"tag":631,"props":2429,"children":2430},{"style":638},[2431],{"type":48,"value":75},{"type":42,"tag":631,"props":2433,"children":2434},{"style":643},[2435],{"type":48,"value":646},{"type":42,"tag":631,"props":2437,"children":2438},{"style":643},[2439],{"type":48,"value":651},{"type":42,"tag":631,"props":2441,"children":2442},{"style":643},[2443],{"type":48,"value":2444}," report",{"type":42,"tag":631,"props":2446,"children":2447},{"style":643},[2448],{"type":48,"value":1217},{"type":42,"tag":631,"props":2450,"children":2451},{"style":643},[2452],{"type":48,"value":1222},{"type":42,"tag":631,"props":2454,"children":2455},{"style":643},[2456],{"type":48,"value":772},{"type":42,"tag":631,"props":2458,"children":2459},{"style":643},[2460],{"type":48,"value":2461}," \u002Ftmp\u002Freport.pdf\n",{"type":42,"tag":631,"props":2463,"children":2464},{"class":633,"line":780},[2465,2469,2473,2477,2482,2487,2492,2496],{"type":42,"tag":631,"props":2466,"children":2467},{"style":638},[2468],{"type":48,"value":75},{"type":42,"tag":631,"props":2470,"children":2471},{"style":643},[2472],{"type":48,"value":646},{"type":42,"tag":631,"props":2474,"children":2475},{"style":643},[2476],{"type":48,"value":651},{"type":42,"tag":631,"props":2478,"children":2479},{"style":643},[2480],{"type":48,"value":2481}," species-comparison",{"type":42,"tag":631,"props":2483,"children":2484},{"style":643},[2485],{"type":48,"value":2486}," --species-id",{"type":42,"tag":631,"props":2488,"children":2489},{"style":1091},[2490],{"type":48,"value":2491}," 48892",{"type":42,"tag":631,"props":2493,"children":2494},{"style":643},[2495],{"type":48,"value":772},{"type":42,"tag":631,"props":2497,"children":2498},{"style":643},[2499],{"type":48,"value":2500}," \u002Ftmp\u002Fspecies.json\n",{"type":42,"tag":631,"props":2502,"children":2503},{"class":633,"line":1003},[2504],{"type":42,"tag":631,"props":2505,"children":2507},{"style":2506},"--shiki-light:#90A4AE;--shiki-light-font-style:italic;--shiki-default:#546E7A;--shiki-default-font-style:italic;--shiki-dark:#676E95;--shiki-dark-font-style:italic",[2508],{"type":48,"value":2509},"# Use --summary to truncate large output and avoid workspace file size limits\n",{"type":42,"tag":631,"props":2511,"children":2512},{"class":633,"line":1050},[2513,2517,2521,2525,2529,2533,2537,2542,2546],{"type":42,"tag":631,"props":2514,"children":2515},{"style":638},[2516],{"type":48,"value":75},{"type":42,"tag":631,"props":2518,"children":2519},{"style":643},[2520],{"type":48,"value":646},{"type":42,"tag":631,"props":2522,"children":2523},{"style":643},[2524],{"type":48,"value":651},{"type":42,"tag":631,"props":2526,"children":2527},{"style":643},[2528],{"type":48,"value":2481},{"type":42,"tag":631,"props":2530,"children":2531},{"style":643},[2532],{"type":48,"value":2486},{"type":42,"tag":631,"props":2534,"children":2535},{"style":1091},[2536],{"type":48,"value":2491},{"type":42,"tag":631,"props":2538,"children":2539},{"style":643},[2540],{"type":48,"value":2541}," --summary",{"type":42,"tag":631,"props":2543,"children":2544},{"style":643},[2545],{"type":48,"value":772},{"type":42,"tag":631,"props":2547,"children":2548},{"style":643},[2549],{"type":48,"value":2500},{"type":42,"tag":51,"props":2551,"children":2553},{"id":2552},"recipe-interpreting-gene-set-enrichment",[2554],{"type":48,"value":2555},"Recipe: Interpreting Gene Set Enrichment",{"type":42,"tag":127,"props":2557,"children":2558},{},[2559],{"type":48,"value":2560},"A step-by-step workflow for interpreting gene set enrichment results:",{"type":42,"tag":58,"props":2562,"children":2563},{},[2564,2580,2598,2614,2630,2646],{"type":42,"tag":62,"props":2565,"children":2566},{},[2567,2572,2574],{"type":42,"tag":66,"props":2568,"children":2569},{},[2570],{"type":48,"value":2571},"Submit gene list",{"type":48,"value":2573}," with projection to human pathways: ",{"type":42,"tag":70,"props":2575,"children":2577},{"className":2576},[],[2578],{"type":48,"value":2579},"bash uv run scripts\u002Freactome_analysis.py analyze-projection \\ --data \"TP53,BRCA1,EGFR,MYC,PTEN\" --fdr 0.05 --output \u002Ftmp\u002Fenrichment.json",{"type":42,"tag":62,"props":2581,"children":2582},{},[2583,2588,2590,2596],{"type":42,"tag":66,"props":2584,"children":2585},{},[2586],{"type":48,"value":2587},"Inspect top pathways",{"type":48,"value":2589}," — examine ",{"type":42,"tag":70,"props":2591,"children":2593},{"className":2592},[],[2594],{"type":48,"value":2595},"pathwaysFound",{"type":48,"value":2597},", top pathway names,\np-values, and FDR values in the output.",{"type":42,"tag":62,"props":2599,"children":2600},{},[2601,2606,2608],{"type":42,"tag":66,"props":2602,"children":2603},{},[2604],{"type":48,"value":2605},"Drill into a pathway",{"type":48,"value":2607}," — get its sub-events and reaction details: ",{"type":42,"tag":70,"props":2609,"children":2611},{"className":2610},[],[2612],{"type":48,"value":2613},"bash uv run scripts\u002Freactome_analysis.py contained-events --id R-HSA-69278 --output \u002Ftmp\u002Fsteps.json uv run scripts\u002Freactome_analysis.py participants --id \u003Creaction_id> --output \u002Ftmp\u002Fparts.json",{"type":42,"tag":62,"props":2615,"children":2616},{},[2617,2622,2624],{"type":42,"tag":66,"props":2618,"children":2619},{},[2620],{"type":48,"value":2621},"Visualise",{"type":48,"value":2623}," — export a diagram with your genes highlighted: ",{"type":42,"tag":70,"props":2625,"children":2627},{"className":2626},[],[2628],{"type":48,"value":2629},"bash uv run scripts\u002Freactome_analysis.py diagram --id R-HSA-69278 \\ --highlight \"TP53,BRCA1\" --output \u002Ftmp\u002Fdiagram.png",{"type":42,"tag":62,"props":2631,"children":2632},{},[2633,2638,2640],{"type":42,"tag":66,"props":2634,"children":2635},{},[2636],{"type":48,"value":2637},"Check hierarchy",{"type":48,"value":2639}," — navigate up to see broader biological context: ",{"type":42,"tag":70,"props":2641,"children":2643},{"className":2642},[],[2644],{"type":48,"value":2645},"bash uv run scripts\u002Freactome_analysis.py event-ancestors --id R-HSA-69278 --output \u002Ftmp\u002Fancestors.json",{"type":42,"tag":62,"props":2647,"children":2648},{},[2649,2654,2656],{"type":42,"tag":66,"props":2650,"children":2651},{},[2652],{"type":48,"value":2653},"Cross-reference",{"type":48,"value":2655}," — map identifiers to other databases: ",{"type":42,"tag":70,"props":2657,"children":2659},{"className":2658},[],[2660],{"type":48,"value":2661},"bash uv run scripts\u002Freactome_analysis.py xref-mapping --id TP53 --output \u002Ftmp\u002Fxrefs.json",{"type":42,"tag":51,"props":2663,"children":2665},{"id":2664},"reference",[2666],{"type":48,"value":2667},"Reference",{"type":42,"tag":127,"props":2669,"children":2670},{},[2671,2673,2678],{"type":48,"value":2672},"For detailed API endpoint documentation, see\n",{"type":42,"tag":103,"props":2674,"children":2676},{"href":2675},"references\u002Fapi_reference.md",[2677],{"type":48,"value":2675},{"type":48,"value":443},{"type":42,"tag":2680,"props":2681,"children":2682},"style",{},[2683],{"type":48,"value":2684},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":2686,"total":2791},[2687,2700,2715,2735,2747,2762,2778],{"slug":2688,"name":2688,"fn":2689,"description":2690,"org":2691,"tags":2692,"stars":25,"repoUrl":26,"updatedAt":2699},"alphafold-database-fetch-and-analyze","retrieve and analyze AlphaFold protein structures","Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2693,2694,2697,2698],{"name":23,"slug":24,"type":15},{"name":2695,"slug":2696,"type":15},"Genomics","genomics",{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:51.827211",{"slug":2701,"name":2701,"fn":2702,"description":2703,"org":2704,"tags":2705,"stars":25,"repoUrl":26,"updatedAt":2714},"alphagenome-single-variant-analysis","analyze genetic variant effects with AlphaGenome","Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue\u002Fcell-type ontologies (UBERON\u002FCL) or analyzing variants in chr:pos:ref>alt format.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2706,2707,2710,2711],{"name":23,"slug":24,"type":15},{"name":2708,"slug":2709,"type":15},"Genetics","genetics",{"name":13,"slug":14,"type":15},{"name":2712,"slug":2713,"type":15},"RNA-seq","rna-seq","2026-07-12T07:51:39.494803",{"slug":2716,"name":2716,"fn":2717,"description":2718,"org":2719,"tags":2720,"stars":25,"repoUrl":26,"updatedAt":2734},"chembl-database","query ChEMBL database for bioactive molecules","Query the ChEMBL database for bioactive molecules, drug targets, bioactivity data, approved drugs, and chemical structures. Use when the user asks about compounds, targets, IC50\u002FKi values, drug mechanisms, or structure searches.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2721,2724,2727,2730,2733],{"name":2722,"slug":2723,"type":15},"ChEMBL","chembl",{"name":2725,"slug":2726,"type":15},"Chemistry","chemistry",{"name":2728,"slug":2729,"type":15},"Database","database",{"name":2731,"slug":2732,"type":15},"Pharmacology","pharmacology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:35.544306",{"slug":2736,"name":2736,"fn":2737,"description":2738,"org":2739,"tags":2740,"stars":25,"repoUrl":26,"updatedAt":2746},"clinical-trials-database","query clinical trial data","Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility\u002Finclusion criteria; count trials across conditions or time periods; identify a sponsor's trial portfolio; find recruiting trials for patient matching.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2741,2744,2745],{"name":2742,"slug":2743,"type":15},"Clinical Trials","clinical-trials",{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:06.846705",{"slug":2748,"name":2748,"fn":2749,"description":2750,"org":2751,"tags":2752,"stars":25,"repoUrl":26,"updatedAt":2761},"clinvar-database","retrieve clinical significance from ClinVar database","Use when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding \"hard positive\" benchmark controls for human genomic variants.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2753,2756,2757,2760],{"name":2754,"slug":2755,"type":15},"ClinVar","clinvar",{"name":2708,"slug":2709,"type":15},{"name":2758,"slug":2759,"type":15},"Healthcare","healthcare",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:36.86094",{"slug":2763,"name":2763,"fn":2764,"description":2765,"org":2766,"tags":2767,"stars":25,"repoUrl":26,"updatedAt":2777},"credentials","manage and verify API credentials safely","Instructions for handling API keys and credentials safely, verifying their presence, and prompting the user to add them if missing using a safe protocol.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2768,2771,2774],{"name":2769,"slug":2770,"type":15},"Compliance","compliance",{"name":2772,"slug":2773,"type":15},"Operations","operations",{"name":2775,"slug":2776,"type":15},"Security","security","2026-07-12T07:52:17.355491",{"slug":2779,"name":2779,"fn":2780,"description":2781,"org":2782,"tags":2783,"stars":25,"repoUrl":26,"updatedAt":2790},"dbsnp-database","search genetic variants in dbSNP database","Use when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database. Resolves between rsIDs, genomic coordinates in VCF format, and HGVS strings. For an rsID, returns variant type, gene associations, clinical significance, allele frequencies, and genomic coordinates (GRCh38).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2784,2785,2786,2789],{"name":23,"slug":24,"type":15},{"name":2708,"slug":2709,"type":15},{"name":2787,"slug":2788,"type":15},"NCBI","ncbi",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:33.054229",38,{"items":2793,"total":2791},[2794,2801,2808,2816,2822,2829,2835,2842,2854,2869,2880,2890],{"slug":2688,"name":2688,"fn":2689,"description":2690,"org":2795,"tags":2796,"stars":25,"repoUrl":26,"updatedAt":2699},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2797,2798,2799,2800],{"name":23,"slug":24,"type":15},{"name":2695,"slug":2696,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":2701,"name":2701,"fn":2702,"description":2703,"org":2802,"tags":2803,"stars":25,"repoUrl":26,"updatedAt":2714},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2804,2805,2806,2807],{"name":23,"slug":24,"type":15},{"name":2708,"slug":2709,"type":15},{"name":13,"slug":14,"type":15},{"name":2712,"slug":2713,"type":15},{"slug":2716,"name":2716,"fn":2717,"description":2718,"org":2809,"tags":2810,"stars":25,"repoUrl":26,"updatedAt":2734},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2811,2812,2813,2814,2815],{"name":2722,"slug":2723,"type":15},{"name":2725,"slug":2726,"type":15},{"name":2728,"slug":2729,"type":15},{"name":2731,"slug":2732,"type":15},{"name":13,"slug":14,"type":15},{"slug":2736,"name":2736,"fn":2737,"description":2738,"org":2817,"tags":2818,"stars":25,"repoUrl":26,"updatedAt":2746},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2819,2820,2821],{"name":2742,"slug":2743,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":2748,"name":2748,"fn":2749,"description":2750,"org":2823,"tags":2824,"stars":25,"repoUrl":26,"updatedAt":2761},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2825,2826,2827,2828],{"name":2754,"slug":2755,"type":15},{"name":2708,"slug":2709,"type":15},{"name":2758,"slug":2759,"type":15},{"name":13,"slug":14,"type":15},{"slug":2763,"name":2763,"fn":2764,"description":2765,"org":2830,"tags":2831,"stars":25,"repoUrl":26,"updatedAt":2777},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2832,2833,2834],{"name":2769,"slug":2770,"type":15},{"name":2772,"slug":2773,"type":15},{"name":2775,"slug":2776,"type":15},{"slug":2779,"name":2779,"fn":2780,"description":2781,"org":2836,"tags":2837,"stars":25,"repoUrl":26,"updatedAt":2790},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2838,2839,2840,2841],{"name":23,"slug":24,"type":15},{"name":2708,"slug":2709,"type":15},{"name":2787,"slug":2788,"type":15},{"name":13,"slug":14,"type":15},{"slug":2843,"name":2843,"fn":2844,"description":2845,"org":2846,"tags":2847,"stars":25,"repoUrl":26,"updatedAt":2853},"embl-ebi-ols","search biomedical ontologies in EMBL-EBI OLS","Query and search the EMBL-EBI Ontology Lookup Service (OLS) for biomedical ontology terms, definitions, and hierarchies across 250+ ontologies (e.g., GO, DOID, HP). Use when the user asks to search for terms, retrieve details, navigate hierarchies (parents, children, ancestors), look up properties and individuals, get autocomplete suggestions, or access ontology metadata and statistics.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2848,2849,2852],{"name":23,"slug":24,"type":15},{"name":2850,"slug":2851,"type":15},"Ontology","ontology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:59.368324",{"slug":2855,"name":2855,"fn":2856,"description":2857,"org":2858,"tags":2859,"stars":25,"repoUrl":26,"updatedAt":2868},"encode-ccres-database","query ENCODE regulatory and experimental data","Query the ENCODE Registry of cis-Regulatory Elements (cCREs) via the SCREEN GraphQL API, or make custom queries to the ENCODE Portal REST API for experiments and files (ChIP-seq peaks, etc.). Use when you want to query regulatory annotations or raw experimental data across human cell types.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2860,2861,2864,2865],{"name":23,"slug":24,"type":15},{"name":2862,"slug":2863,"type":15},"GraphQL","graphql",{"name":13,"slug":14,"type":15},{"name":2866,"slug":2867,"type":15},"REST API","rest-api","2026-07-12T07:52:10.597139",{"slug":2870,"name":2870,"fn":2871,"description":2872,"org":2873,"tags":2874,"stars":25,"repoUrl":26,"updatedAt":2879},"ensembl-database","query genomic and protein data from Ensembl","Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2875,2876,2877,2878],{"name":23,"slug":24,"type":15},{"name":2708,"slug":2709,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":2881,"name":2881,"fn":2882,"description":2883,"org":2884,"tags":2885,"stars":25,"repoUrl":26,"updatedAt":2889},"foldseek-structural-search","perform 3D protein structural searches","Performs 3D structural searches of proteins against various databases (PDB, AlphaFold, CATH, MGnify, etc.) using the Foldseek API. Use ONLY when the user provides a physical 3D coordinate file (.cif, .mmcif, or .pdb) and wants to find structurally similar proteins. Do NOT use if the user only provides a protein sequence, gene name, or UniProt ID.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2886,2887,2888],{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:09.354992",{"slug":2891,"name":2891,"fn":2892,"description":2893,"org":2894,"tags":2895,"stars":25,"repoUrl":26,"updatedAt":2900},"gnomad-database","query genetic variant data from gnomAD","Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2896,2897,2898,2899],{"name":23,"slug":24,"type":15},{"name":2708,"slug":2709,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:38.213009"]