[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-quickgo-database":3,"mdc--5xjoop-key":36,"related-repo-google-deepmind-quickgo-database":983,"related-org-google-deepmind-quickgo-database":1090},{"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},"quickgo-database","query QuickGO and ECO databases","Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this when you need to map genes to biological processes, molecular functions, or cellular components, find genes associated with a specific pathway\u002FGO term, or explore the Gene Ontology hierarchy. Do not use for querying drug targets (use OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).\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},"Ontology","ontology",{"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","2026-07-12T07:52:00.617926",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\u002Fquickgo_database","---\nname: quickgo-database\ndescription: >\n  Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this\n  when you need to map genes to biological processes, molecular functions, or\n  cellular components, find genes associated with a specific pathway\u002FGO term, or\n  explore the Gene Ontology hierarchy. Do not use for querying drug targets (use\n  OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).\n---\n\n# QuickGO Database Skill\n\nGO (Gene Ontology) annotations are one of the main ways to label a gene's\nfunction. QuickGO is a fast, web-based browser for the GO and Evidence &\nConclusion Ontology (ECO), maintained by the Gene Ontology Annotation (GOA)\ngroup at EMBL-EBI.\n\nIt provides a centralised resource to explore the functional attributes of gene\nproducts (proteins, RNA, and complexes). It is a primary tool for functional\nannotation mapping since it allows you to link a gene (e.g., USH2A) to its\nspecific biological processes (e.g. sensory perception of light stimulus),\nmolecular functions, and cellular components.\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\u002Fquickgo_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\u002Fwww.ebi.ac.uk\u002FQuickGO\u002F and\n    https:\u002F\u002Fwww.ebi.ac.uk\u002FQuickGO\u002Fapi\u002Findex.html, then (2) create the file\n    recording the notification text and timestamp.\n\n## Usage\n\nThis skill provides a Python CLI wrapper `scripts\u002Fquickgo_tool.py` that queries\nthe QuickGO REST API. It handles formatting the requests, respecting rate\nlimits, and safely storing the potentially large JSON responses.\n\n## Core Rules\n\n-   **Use the Wrapper**: ALWAYS execute the provided helper scripts to query the\n    database rather than accessing the database directly. The scripts\n    automatically enforce the required rate limit gracefully.\n-   **Pagination & Limits**: Restrict endpoints to a maximum of 100 results per\n    page using `--limit 100` and the `--page` parameter for larger datasets.\n-   **Output Files**: Always use the `--output` flag to save responses to a file\n    incrementally or parse via `jq`.\n-   **Evidence Codes**: Prioritize experimental evidence (`ECO:0000269`) over\n    electronic (`ECO:0000501`) to avoid noisy predictions.\n-   **Taxon Filtering**: Use `--taxonId 9606` to restrict results to Human when\n    analysing clinical or human genomic data.\n-   **Notification**: If this skill is used, ensure this is mentioned in the\n    output.\n\nThe tool has four main subcommands:\n\n1.  **`go`**: For retrieving information about GO terms (e.g. definitions,\n    ancestors, descendants, and slims). See\n    [references\u002Fgo_terms.md](references\u002Fgo_terms.md).\n2.  **`annotation`**: For finding functional annotations linking gene products\n    to GO terms. This is your primary functional mapper. See\n    [references\u002Fannotations.md](references\u002Fannotations.md).\n3.  **`geneproduct`**: For resolving gene symbols (like `PROC`) to their formal\n    database identifiers. See\n    [references\u002Fgene_products.md](references\u002Fgene_products.md).\n4.  **`eco`**: For Evidence & Conclusion Ontology terms (used in annotations to\n    indicate how an annotation was derived, e.g. experimental vs electronic).\n    See [references\u002Feco_terms.md](references\u002Feco_terms.md).\n\n## Common Workflows\n\n### 1. Map a gene to its functions (Annotations)\n\nTo find out what a gene does, you must first resolve its symbol to a UniProtKB\nID, and then query its annotations. Often it is best to filter for experimental\nevidence (e.g. `ECO:0000269` for EXP, or others like IDA, IMP) to avoid noisy\nelectronic predictions.\n\n```bash\n# Step 1: Find the UniProtKB ID for human (9606) gene PROC\nuv run scripts\u002Fquickgo_tool.py geneproduct search --query \"PROC\" --taxonId 9606 --limit 5 --output proc_id.json\n# (Look at proc_id.json, observe the ID is e.g., UniProtKB:P04070)\n\n# Step 2: Find experimental GO annotations for that ID\nuv run scripts\u002Fquickgo_tool.py annotation search --geneProductId \"UniProtKB:P04070\" --taxonId 9606 --evidenceCode \"ECO:0000269\" --limit 50 --output proc_annotations.json\n```\n\n### 2. Find all genes in a pathway\n\nTo find all genes annotated to a specific GO term (e.g., GO:0003700 for\n\"transcription factor activity\"):\n\n```bash\n# Find human genes with this specific molecular function\nuv run scripts\u002Fquickgo_tool.py annotation search --goId \"GO:0003700\" --taxonId 9606 --limit 50 --output tf_genes.json\n```\n\n### 3. Explore the GO Hierarchy\n\nTo check if a specific GO term is a descendant of a broader category, or to\nfetch its definition:\n\n```bash\n# Fetch term details (definitions, synonyms)\nuv run scripts\u002Fquickgo_tool.py go terms --ids \"GO:0003150\" --output term_details.json\n\n# Check ancestry (e.g., is GO:0001917 a child of something?)\nuv run scripts\u002Fquickgo_tool.py go terms --ids \"GO:0001917\" --relation ancestors --output term_ancestors.json\n```\n\n### 4. Create a GO Slim Summary\n\nIf you have a list of candidate genes and want a high-level functional summary,\nyou can map them up to a predefined GO Slim. First, fetch the annotations for\nthe genes to extract their GO IDs, then pass those IDs to the slim endpoint:\n\n```bash\n# Step 1: Find GO IDs for candidate genes (e.g., via their UniProt IDs, fetching their annotations)\n# ... (output yields e.g., GO:0006915,GO:0008219)\n\n# Step 2: Create a slim summary from those specific GO IDs\nuv run scripts\u002Fquickgo_tool.py go slim --slimsToIds \"GO:0005575,GO:0008150,GO:0003674\" --slimsFromIds \"GO:0006915,GO:0008219\" --output my_slim.json\n```\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,56,61,68,131,137,150,156,276,281,372,378,385,397,615,621,626,707,713,718,859,865,870,977],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"quickgo-database-skill",[47],{"type":48,"value":49},"text","QuickGO Database Skill",{"type":42,"tag":51,"props":52,"children":53},"p",{},[54],{"type":48,"value":55},"GO (Gene Ontology) annotations are one of the main ways to label a gene's\nfunction. QuickGO is a fast, web-based browser for the GO and Evidence &\nConclusion Ontology (ECO), maintained by the Gene Ontology Annotation (GOA)\ngroup at EMBL-EBI.",{"type":42,"tag":51,"props":57,"children":58},{},[59],{"type":48,"value":60},"It provides a centralised resource to explore the functional attributes of gene\nproducts (proteins, RNA, and complexes). It is a primary tool for functional\nannotation mapping since it allows you to link a gene (e.g., USH2A) to its\nspecific biological processes (e.g. sensory perception of light stimulus),\nmolecular functions, and cellular components.",{"type":42,"tag":62,"props":63,"children":65},"h2",{"id":64},"prerequisites",[66],{"type":48,"value":67},"Prerequisites",{"type":42,"tag":69,"props":70,"children":71},"ol",{},[72,103],{"type":42,"tag":73,"props":74,"children":75},"li",{},[76,87,89,94,96,101],{"type":42,"tag":77,"props":78,"children":79},"strong",{},[80],{"type":42,"tag":81,"props":82,"children":84},"code",{"className":83},[],[85],{"type":48,"value":86},"uv",{"type":48,"value":88},": Read the ",{"type":42,"tag":81,"props":90,"children":92},{"className":91},[],[93],{"type":48,"value":86},{"type":48,"value":95}," skill and follow its Setup instructions to ensure\n",{"type":42,"tag":81,"props":97,"children":99},{"className":98},[],[100],{"type":48,"value":86},{"type":48,"value":102}," is installed and on PATH.",{"type":42,"tag":73,"props":104,"children":105},{},[106,111,113,121,123,129],{"type":42,"tag":77,"props":107,"children":108},{},[109],{"type":48,"value":110},"User Notification",{"type":48,"value":112},": If .licenses\u002Fquickgo_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":114,"props":115,"children":119},"a",{"href":116,"rel":117},"https:\u002F\u002Fwww.ebi.ac.uk\u002FQuickGO\u002F",[118],"nofollow",[120],{"type":48,"value":116},{"type":48,"value":122}," and\n",{"type":42,"tag":114,"props":124,"children":127},{"href":125,"rel":126},"https:\u002F\u002Fwww.ebi.ac.uk\u002FQuickGO\u002Fapi\u002Findex.html",[118],[128],{"type":48,"value":125},{"type":48,"value":130},", then (2) create the file\nrecording the notification text and timestamp.",{"type":42,"tag":62,"props":132,"children":134},{"id":133},"usage",[135],{"type":48,"value":136},"Usage",{"type":42,"tag":51,"props":138,"children":139},{},[140,142,148],{"type":48,"value":141},"This skill provides a Python CLI wrapper ",{"type":42,"tag":81,"props":143,"children":145},{"className":144},[],[146],{"type":48,"value":147},"scripts\u002Fquickgo_tool.py",{"type":48,"value":149}," that queries\nthe QuickGO REST API. It handles formatting the requests, respecting rate\nlimits, and safely storing the potentially large JSON responses.",{"type":42,"tag":62,"props":151,"children":153},{"id":152},"core-rules",[154],{"type":48,"value":155},"Core Rules",{"type":42,"tag":157,"props":158,"children":159},"ul",{},[160,170,196,222,248,266],{"type":42,"tag":73,"props":161,"children":162},{},[163,168],{"type":42,"tag":77,"props":164,"children":165},{},[166],{"type":48,"value":167},"Use the Wrapper",{"type":48,"value":169},": ALWAYS execute the provided helper scripts to query the\ndatabase rather than accessing the database directly. The scripts\nautomatically enforce the required rate limit gracefully.",{"type":42,"tag":73,"props":171,"children":172},{},[173,178,180,186,188,194],{"type":42,"tag":77,"props":174,"children":175},{},[176],{"type":48,"value":177},"Pagination & Limits",{"type":48,"value":179},": Restrict endpoints to a maximum of 100 results per\npage using ",{"type":42,"tag":81,"props":181,"children":183},{"className":182},[],[184],{"type":48,"value":185},"--limit 100",{"type":48,"value":187}," and the ",{"type":42,"tag":81,"props":189,"children":191},{"className":190},[],[192],{"type":48,"value":193},"--page",{"type":48,"value":195}," parameter for larger datasets.",{"type":42,"tag":73,"props":197,"children":198},{},[199,204,206,212,214,220],{"type":42,"tag":77,"props":200,"children":201},{},[202],{"type":48,"value":203},"Output Files",{"type":48,"value":205},": Always use the ",{"type":42,"tag":81,"props":207,"children":209},{"className":208},[],[210],{"type":48,"value":211},"--output",{"type":48,"value":213}," flag to save responses to a file\nincrementally or parse via ",{"type":42,"tag":81,"props":215,"children":217},{"className":216},[],[218],{"type":48,"value":219},"jq",{"type":48,"value":221},".",{"type":42,"tag":73,"props":223,"children":224},{},[225,230,232,238,240,246],{"type":42,"tag":77,"props":226,"children":227},{},[228],{"type":48,"value":229},"Evidence Codes",{"type":48,"value":231},": Prioritize experimental evidence (",{"type":42,"tag":81,"props":233,"children":235},{"className":234},[],[236],{"type":48,"value":237},"ECO:0000269",{"type":48,"value":239},") over\nelectronic (",{"type":42,"tag":81,"props":241,"children":243},{"className":242},[],[244],{"type":48,"value":245},"ECO:0000501",{"type":48,"value":247},") to avoid noisy predictions.",{"type":42,"tag":73,"props":249,"children":250},{},[251,256,258,264],{"type":42,"tag":77,"props":252,"children":253},{},[254],{"type":48,"value":255},"Taxon Filtering",{"type":48,"value":257},": Use ",{"type":42,"tag":81,"props":259,"children":261},{"className":260},[],[262],{"type":48,"value":263},"--taxonId 9606",{"type":48,"value":265}," to restrict results to Human when\nanalysing clinical or human genomic data.",{"type":42,"tag":73,"props":267,"children":268},{},[269,274],{"type":42,"tag":77,"props":270,"children":271},{},[272],{"type":48,"value":273},"Notification",{"type":48,"value":275},": If this skill is used, ensure this is mentioned in the\noutput.",{"type":42,"tag":51,"props":277,"children":278},{},[279],{"type":48,"value":280},"The tool has four main subcommands:",{"type":42,"tag":69,"props":282,"children":283},{},[284,304,324,352],{"type":42,"tag":73,"props":285,"children":286},{},[287,296,298,303],{"type":42,"tag":77,"props":288,"children":289},{},[290],{"type":42,"tag":81,"props":291,"children":293},{"className":292},[],[294],{"type":48,"value":295},"go",{"type":48,"value":297},": For retrieving information about GO terms (e.g. definitions,\nancestors, descendants, and slims). See\n",{"type":42,"tag":114,"props":299,"children":301},{"href":300},"references\u002Fgo_terms.md",[302],{"type":48,"value":300},{"type":48,"value":221},{"type":42,"tag":73,"props":305,"children":306},{},[307,316,318,323],{"type":42,"tag":77,"props":308,"children":309},{},[310],{"type":42,"tag":81,"props":311,"children":313},{"className":312},[],[314],{"type":48,"value":315},"annotation",{"type":48,"value":317},": For finding functional annotations linking gene products\nto GO terms. This is your primary functional mapper. See\n",{"type":42,"tag":114,"props":319,"children":321},{"href":320},"references\u002Fannotations.md",[322],{"type":48,"value":320},{"type":48,"value":221},{"type":42,"tag":73,"props":325,"children":326},{},[327,336,338,344,346,351],{"type":42,"tag":77,"props":328,"children":329},{},[330],{"type":42,"tag":81,"props":331,"children":333},{"className":332},[],[334],{"type":48,"value":335},"geneproduct",{"type":48,"value":337},": For resolving gene symbols (like ",{"type":42,"tag":81,"props":339,"children":341},{"className":340},[],[342],{"type":48,"value":343},"PROC",{"type":48,"value":345},") to their formal\ndatabase identifiers. See\n",{"type":42,"tag":114,"props":347,"children":349},{"href":348},"references\u002Fgene_products.md",[350],{"type":48,"value":348},{"type":48,"value":221},{"type":42,"tag":73,"props":353,"children":354},{},[355,364,366,371],{"type":42,"tag":77,"props":356,"children":357},{},[358],{"type":42,"tag":81,"props":359,"children":361},{"className":360},[],[362],{"type":48,"value":363},"eco",{"type":48,"value":365},": For Evidence & Conclusion Ontology terms (used in annotations to\nindicate how an annotation was derived, e.g. experimental vs electronic).\nSee ",{"type":42,"tag":114,"props":367,"children":369},{"href":368},"references\u002Feco_terms.md",[370],{"type":48,"value":368},{"type":48,"value":221},{"type":42,"tag":62,"props":373,"children":375},{"id":374},"common-workflows",[376],{"type":48,"value":377},"Common Workflows",{"type":42,"tag":379,"props":380,"children":382},"h3",{"id":381},"_1-map-a-gene-to-its-functions-annotations",[383],{"type":48,"value":384},"1. Map a gene to its functions (Annotations)",{"type":42,"tag":51,"props":386,"children":387},{},[388,390,395],{"type":48,"value":389},"To find out what a gene does, you must first resolve its symbol to a UniProtKB\nID, and then query its annotations. Often it is best to filter for experimental\nevidence (e.g. ",{"type":42,"tag":81,"props":391,"children":393},{"className":392},[],[394],{"type":48,"value":237},{"type":48,"value":396}," for EXP, or others like IDA, IMP) to avoid noisy\nelectronic predictions.",{"type":42,"tag":398,"props":399,"children":404},"pre",{"className":400,"code":401,"language":402,"meta":403,"style":403},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Step 1: Find the UniProtKB ID for human (9606) gene PROC\nuv run scripts\u002Fquickgo_tool.py geneproduct search --query \"PROC\" --taxonId 9606 --limit 5 --output proc_id.json\n# (Look at proc_id.json, observe the ID is e.g., UniProtKB:P04070)\n\n# Step 2: Find experimental GO annotations for that ID\nuv run scripts\u002Fquickgo_tool.py annotation search --geneProductId \"UniProtKB:P04070\" --taxonId 9606 --evidenceCode \"ECO:0000269\" --limit 50 --output 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Find all genes in a pathway",{"type":42,"tag":51,"props":622,"children":623},{},[624],{"type":48,"value":625},"To find all genes annotated to a specific GO term (e.g., GO:0003700 for\n\"transcription factor activity\"):",{"type":42,"tag":398,"props":627,"children":629},{"className":400,"code":628,"language":402,"meta":403,"style":403},"# Find human genes with this specific molecular function\nuv run scripts\u002Fquickgo_tool.py annotation search --goId \"GO:0003700\" --taxonId 9606 --limit 50 --output tf_genes.json\n",[630],{"type":42,"tag":81,"props":631,"children":632},{"__ignoreMap":403},[633,641],{"type":42,"tag":409,"props":634,"children":635},{"class":411,"line":412},[636],{"type":42,"tag":409,"props":637,"children":638},{"style":416},[639],{"type":48,"value":640},"# Find human genes with this specific molecular function\n",{"type":42,"tag":409,"props":642,"children":643},{"class":411,"line":422},[644,648,652,656,660,664,669,673,678,682,686,690,694,698,702],{"type":42,"tag":409,"props":645,"children":646},{"style":426},[647],{"type":48,"value":86},{"type":42,"tag":409,"props":649,"children":650},{"style":431},[651],{"type":48,"value":434},{"type":42,"tag":409,"props":653,"children":654},{"style":431},[655],{"type":48,"value":439},{"type":42,"tag":409,"props":657,"children":658},{"style":431},[659],{"type":48,"value":549},{"type":42,"tag":409,"props":661,"children":662},{"style":431},[663],{"type":48,"value":449},{"type":42,"tag":409,"props":665,"children":666},{"style":431},[667],{"type":48,"value":668}," --goId",{"type":42,"tag":409,"props":670,"children":671},{"style":457},[672],{"type":48,"value":460},{"type":42,"tag":409,"props":674,"children":675},{"style":431},[676],{"type":48,"value":677},"GO:0003700",{"type":42,"tag":409,"props":679,"children":680},{"style":457},[681],{"type":48,"value":469},{"type":42,"tag":409,"props":683,"children":684},{"style":431},[685],{"type":48,"value":474},{"type":42,"tag":409,"props":687,"children":688},{"style":477},[689],{"type":48,"value":480},{"type":42,"tag":409,"props":691,"children":692},{"style":431},[693],{"type":48,"value":485},{"type":42,"tag":409,"props":695,"children":696},{"style":477},[697],{"type":48,"value":605},{"type":42,"tag":409,"props":699,"children":700},{"style":431},[701],{"type":48,"value":495},{"type":42,"tag":409,"props":703,"children":704},{"style":431},[705],{"type":48,"value":706}," tf_genes.json\n",{"type":42,"tag":379,"props":708,"children":710},{"id":709},"_3-explore-the-go-hierarchy",[711],{"type":48,"value":712},"3. Explore the GO Hierarchy",{"type":42,"tag":51,"props":714,"children":715},{},[716],{"type":48,"value":717},"To check if a specific GO term is a descendant of a broader category, or to\nfetch its definition:",{"type":42,"tag":398,"props":719,"children":721},{"className":400,"code":720,"language":402,"meta":403,"style":403},"# Fetch term details (definitions, synonyms)\nuv run scripts\u002Fquickgo_tool.py go terms --ids \"GO:0003150\" --output term_details.json\n\n# Check ancestry (e.g., is GO:0001917 a child of something?)\nuv run scripts\u002Fquickgo_tool.py go terms --ids \"GO:0001917\" --relation ancestors --output term_ancestors.json\n",[722],{"type":42,"tag":81,"props":723,"children":724},{"__ignoreMap":403},[725,733,785,792,800],{"type":42,"tag":409,"props":726,"children":727},{"class":411,"line":412},[728],{"type":42,"tag":409,"props":729,"children":730},{"style":416},[731],{"type":48,"value":732},"# Fetch term details (definitions, synonyms)\n",{"type":42,"tag":409,"props":734,"children":735},{"class":411,"line":422},[736,740,744,748,753,758,763,767,772,776,780],{"type":42,"tag":409,"props":737,"children":738},{"style":426},[739],{"type":48,"value":86},{"type":42,"tag":409,"props":741,"children":742},{"style":431},[743],{"type":48,"value":434},{"type":42,"tag":409,"props":745,"children":746},{"style":431},[747],{"type":48,"value":439},{"type":42,"tag":409,"props":749,"children":750},{"style":431},[751],{"type":48,"value":752}," go",{"type":42,"tag":409,"props":754,"children":755},{"style":431},[756],{"type":48,"value":757}," terms",{"type":42,"tag":409,"props":759,"children":760},{"style":431},[761],{"type":48,"value":762}," --ids",{"type":42,"tag":409,"props":764,"children":765},{"style":457},[766],{"type":48,"value":460},{"type":42,"tag":409,"props":768,"children":769},{"style":431},[770],{"type":48,"value":771},"GO:0003150",{"type":42,"tag":409,"props":773,"children":774},{"style":457},[775],{"type":48,"value":469},{"type":42,"tag":409,"props":777,"children":778},{"style":431},[779],{"type":48,"value":495},{"type":42,"tag":409,"props":781,"children":782},{"style":431},[783],{"type":48,"value":784}," term_details.json\n",{"type":42,"tag":409,"props":786,"children":787},{"class":411,"line":503},[788],{"type":42,"tag":409,"props":789,"children":790},{"emptyLinePlaceholder":516},[791],{"type":48,"value":519},{"type":42,"tag":409,"props":793,"children":794},{"class":411,"line":512},[795],{"type":42,"tag":409,"props":796,"children":797},{"style":416},[798],{"type":48,"value":799},"# Check ancestry (e.g., is GO:0001917 a child of something?)\n",{"type":42,"tag":409,"props":801,"children":802},{"class":411,"line":522},[803,807,811,815,819,823,827,831,836,840,845,850,854],{"type":42,"tag":409,"props":804,"children":805},{"style":426},[806],{"type":48,"value":86},{"type":42,"tag":409,"props":808,"children":809},{"style":431},[810],{"type":48,"value":434},{"type":42,"tag":409,"props":812,"children":813},{"style":431},[814],{"type":48,"value":439},{"type":42,"tag":409,"props":816,"children":817},{"style":431},[818],{"type":48,"value":752},{"type":42,"tag":409,"props":820,"children":821},{"style":431},[822],{"type":48,"value":757},{"type":42,"tag":409,"props":824,"children":825},{"style":431},[826],{"type":48,"value":762},{"type":42,"tag":409,"props":828,"children":829},{"style":457},[830],{"type":48,"value":460},{"type":42,"tag":409,"props":832,"children":833},{"style":431},[834],{"type":48,"value":835},"GO:0001917",{"type":42,"tag":409,"props":837,"children":838},{"style":457},[839],{"type":48,"value":469},{"type":42,"tag":409,"props":841,"children":842},{"style":431},[843],{"type":48,"value":844}," --relation",{"type":42,"tag":409,"props":846,"children":847},{"style":431},[848],{"type":48,"value":849}," ancestors",{"type":42,"tag":409,"props":851,"children":852},{"style":431},[853],{"type":48,"value":495},{"type":42,"tag":409,"props":855,"children":856},{"style":431},[857],{"type":48,"value":858}," term_ancestors.json\n",{"type":42,"tag":379,"props":860,"children":862},{"id":861},"_4-create-a-go-slim-summary",[863],{"type":48,"value":864},"4. Create a GO Slim Summary",{"type":42,"tag":51,"props":866,"children":867},{},[868],{"type":48,"value":869},"If you have a list of candidate genes and want a high-level functional summary,\nyou can map them up to a predefined GO Slim. First, fetch the annotations for\nthe genes to extract their GO IDs, then pass those IDs to the slim endpoint:",{"type":42,"tag":398,"props":871,"children":873},{"className":400,"code":872,"language":402,"meta":403,"style":403},"# Step 1: Find GO IDs for candidate genes (e.g., via their UniProt IDs, fetching their annotations)\n# ... (output yields e.g., GO:0006915,GO:0008219)\n\n# Step 2: Create a slim summary from those specific GO IDs\nuv run scripts\u002Fquickgo_tool.py go slim --slimsToIds \"GO:0005575,GO:0008150,GO:0003674\" --slimsFromIds \"GO:0006915,GO:0008219\" --output my_slim.json\n",[874],{"type":42,"tag":81,"props":875,"children":876},{"__ignoreMap":403},[877,885,893,900,908],{"type":42,"tag":409,"props":878,"children":879},{"class":411,"line":412},[880],{"type":42,"tag":409,"props":881,"children":882},{"style":416},[883],{"type":48,"value":884},"# Step 1: Find GO IDs for candidate genes (e.g., via their UniProt IDs, fetching their annotations)\n",{"type":42,"tag":409,"props":886,"children":887},{"class":411,"line":422},[888],{"type":42,"tag":409,"props":889,"children":890},{"style":416},[891],{"type":48,"value":892},"# ... (output yields e.g., GO:0006915,GO:0008219)\n",{"type":42,"tag":409,"props":894,"children":895},{"class":411,"line":503},[896],{"type":42,"tag":409,"props":897,"children":898},{"emptyLinePlaceholder":516},[899],{"type":48,"value":519},{"type":42,"tag":409,"props":901,"children":902},{"class":411,"line":512},[903],{"type":42,"tag":409,"props":904,"children":905},{"style":416},[906],{"type":48,"value":907},"# Step 2: Create a slim summary from those specific GO IDs\n",{"type":42,"tag":409,"props":909,"children":910},{"class":411,"line":522},[911,915,919,923,927,932,937,941,946,950,955,959,964,968,972],{"type":42,"tag":409,"props":912,"children":913},{"style":426},[914],{"type":48,"value":86},{"type":42,"tag":409,"props":916,"children":917},{"style":431},[918],{"type":48,"value":434},{"type":42,"tag":409,"props":920,"children":921},{"style":431},[922],{"type":48,"value":439},{"type":42,"tag":409,"props":924,"children":925},{"style":431},[926],{"type":48,"value":752},{"type":42,"tag":409,"props":928,"children":929},{"style":431},[930],{"type":48,"value":931}," slim",{"type":42,"tag":409,"props":933,"children":934},{"style":431},[935],{"type":48,"value":936}," --slimsToIds",{"type":42,"tag":409,"props":938,"children":939},{"style":457},[940],{"type":48,"value":460},{"type":42,"tag":409,"props":942,"children":943},{"style":431},[944],{"type":48,"value":945},"GO:0005575,GO:0008150,GO:0003674",{"type":42,"tag":409,"props":947,"children":948},{"style":457},[949],{"type":48,"value":469},{"type":42,"tag":409,"props":951,"children":952},{"style":431},[953],{"type":48,"value":954}," --slimsFromIds",{"type":42,"tag":409,"props":956,"children":957},{"style":457},[958],{"type":48,"value":460},{"type":42,"tag":409,"props":960,"children":961},{"style":431},[962],{"type":48,"value":963},"GO:0006915,GO:0008219",{"type":42,"tag":409,"props":965,"children":966},{"style":457},[967],{"type":48,"value":469},{"type":42,"tag":409,"props":969,"children":970},{"style":431},[971],{"type":48,"value":495},{"type":42,"tag":409,"props":973,"children":974},{"style":431},[975],{"type":48,"value":976}," my_slim.json\n",{"type":42,"tag":978,"props":979,"children":980},"style",{},[981],{"type":48,"value":982},"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":984,"total":1089},[985,998,1013,1033,1045,1060,1076],{"slug":986,"name":986,"fn":987,"description":988,"org":989,"tags":990,"stars":25,"repoUrl":26,"updatedAt":997},"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},[991,992,995,996],{"name":23,"slug":24,"type":15},{"name":993,"slug":994,"type":15},"Genomics","genomics",{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:51.827211",{"slug":999,"name":999,"fn":1000,"description":1001,"org":1002,"tags":1003,"stars":25,"repoUrl":26,"updatedAt":1012},"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},[1004,1005,1008,1009],{"name":23,"slug":24,"type":15},{"name":1006,"slug":1007,"type":15},"Genetics","genetics",{"name":13,"slug":14,"type":15},{"name":1010,"slug":1011,"type":15},"RNA-seq","rna-seq","2026-07-12T07:51:39.494803",{"slug":1014,"name":1014,"fn":1015,"description":1016,"org":1017,"tags":1018,"stars":25,"repoUrl":26,"updatedAt":1032},"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},[1019,1022,1025,1028,1031],{"name":1020,"slug":1021,"type":15},"ChEMBL","chembl",{"name":1023,"slug":1024,"type":15},"Chemistry","chemistry",{"name":1026,"slug":1027,"type":15},"Database","database",{"name":1029,"slug":1030,"type":15},"Pharmacology","pharmacology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:35.544306",{"slug":1034,"name":1034,"fn":1035,"description":1036,"org":1037,"tags":1038,"stars":25,"repoUrl":26,"updatedAt":1044},"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},[1039,1042,1043],{"name":1040,"slug":1041,"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":1046,"name":1046,"fn":1047,"description":1048,"org":1049,"tags":1050,"stars":25,"repoUrl":26,"updatedAt":1059},"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},[1051,1054,1055,1058],{"name":1052,"slug":1053,"type":15},"ClinVar","clinvar",{"name":1006,"slug":1007,"type":15},{"name":1056,"slug":1057,"type":15},"Healthcare","healthcare",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:36.86094",{"slug":1061,"name":1061,"fn":1062,"description":1063,"org":1064,"tags":1065,"stars":25,"repoUrl":26,"updatedAt":1075},"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},[1066,1069,1072],{"name":1067,"slug":1068,"type":15},"Compliance","compliance",{"name":1070,"slug":1071,"type":15},"Operations","operations",{"name":1073,"slug":1074,"type":15},"Security","security","2026-07-12T07:52:17.355491",{"slug":1077,"name":1077,"fn":1078,"description":1079,"org":1080,"tags":1081,"stars":25,"repoUrl":26,"updatedAt":1088},"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},[1082,1083,1084,1087],{"name":23,"slug":24,"type":15},{"name":1006,"slug":1007,"type":15},{"name":1085,"slug":1086,"type":15},"NCBI","ncbi",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:33.054229",38,{"items":1091,"total":1089},[1092,1099,1106,1114,1120,1127,1133,1140,1150,1165,1176,1186],{"slug":986,"name":986,"fn":987,"description":988,"org":1093,"tags":1094,"stars":25,"repoUrl":26,"updatedAt":997},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1095,1096,1097,1098],{"name":23,"slug":24,"type":15},{"name":993,"slug":994,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":999,"name":999,"fn":1000,"description":1001,"org":1100,"tags":1101,"stars":25,"repoUrl":26,"updatedAt":1012},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1102,1103,1104,1105],{"name":23,"slug":24,"type":15},{"name":1006,"slug":1007,"type":15},{"name":13,"slug":14,"type":15},{"name":1010,"slug":1011,"type":15},{"slug":1014,"name":1014,"fn":1015,"description":1016,"org":1107,"tags":1108,"stars":25,"repoUrl":26,"updatedAt":1032},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1109,1110,1111,1112,1113],{"name":1020,"slug":1021,"type":15},{"name":1023,"slug":1024,"type":15},{"name":1026,"slug":1027,"type":15},{"name":1029,"slug":1030,"type":15},{"name":13,"slug":14,"type":15},{"slug":1034,"name":1034,"fn":1035,"description":1036,"org":1115,"tags":1116,"stars":25,"repoUrl":26,"updatedAt":1044},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1117,1118,1119],{"name":1040,"slug":1041,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":1046,"name":1046,"fn":1047,"description":1048,"org":1121,"tags":1122,"stars":25,"repoUrl":26,"updatedAt":1059},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1123,1124,1125,1126],{"name":1052,"slug":1053,"type":15},{"name":1006,"slug":1007,"type":15},{"name":1056,"slug":1057,"type":15},{"name":13,"slug":14,"type":15},{"slug":1061,"name":1061,"fn":1062,"description":1063,"org":1128,"tags":1129,"stars":25,"repoUrl":26,"updatedAt":1075},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1130,1131,1132],{"name":1067,"slug":1068,"type":15},{"name":1070,"slug":1071,"type":15},{"name":1073,"slug":1074,"type":15},{"slug":1077,"name":1077,"fn":1078,"description":1079,"org":1134,"tags":1135,"stars":25,"repoUrl":26,"updatedAt":1088},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1136,1137,1138,1139],{"name":23,"slug":24,"type":15},{"name":1006,"slug":1007,"type":15},{"name":1085,"slug":1086,"type":15},{"name":13,"slug":14,"type":15},{"slug":1141,"name":1141,"fn":1142,"description":1143,"org":1144,"tags":1145,"stars":25,"repoUrl":26,"updatedAt":1149},"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},[1146,1147,1148],{"name":23,"slug":24,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:59.368324",{"slug":1151,"name":1151,"fn":1152,"description":1153,"org":1154,"tags":1155,"stars":25,"repoUrl":26,"updatedAt":1164},"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},[1156,1157,1160,1161],{"name":23,"slug":24,"type":15},{"name":1158,"slug":1159,"type":15},"GraphQL","graphql",{"name":13,"slug":14,"type":15},{"name":1162,"slug":1163,"type":15},"REST API","rest-api","2026-07-12T07:52:10.597139",{"slug":1166,"name":1166,"fn":1167,"description":1168,"org":1169,"tags":1170,"stars":25,"repoUrl":26,"updatedAt":1175},"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},[1171,1172,1173,1174],{"name":23,"slug":24,"type":15},{"name":1006,"slug":1007,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":1177,"name":1177,"fn":1178,"description":1179,"org":1180,"tags":1181,"stars":25,"repoUrl":26,"updatedAt":1185},"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},[1182,1183,1184],{"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":1187,"name":1187,"fn":1188,"description":1189,"org":1190,"tags":1191,"stars":25,"repoUrl":26,"updatedAt":1196},"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},[1192,1193,1194,1195],{"name":23,"slug":24,"type":15},{"name":1006,"slug":1007,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:38.213009"]