[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-google-deepmind-gtex-database":3,"mdc--17utcn-key":36,"related-org-google-deepmind-gtex-database":1257,"related-repo-google-deepmind-gtex-database":1421},{"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},"gtex-database","retrieve RNA expression and eQTL data","Use when you want to retrieve quantitative RNA expression data and variant eQTL information from the GTEx (Genotype-Tissue Expression) Project across 54 non-diseased tissue sites.\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},"Life Sciences","life-sciences",{"name":20,"slug":21,"type":15},"Bioinformatics","bioinformatics",{"name":23,"slug":24,"type":15},"RNA-seq","rna-seq",2333,"https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Fscience-skills","2026-07-12T07:51:55.676755",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\u002Fgtex_database","---\nname: gtex-database\ndescription: >\n  Use when you want to retrieve quantitative RNA expression data and variant\n  eQTL information from the GTEx (Genotype-Tissue Expression) Project across 54\n  non-diseased tissue sites.\n---\n\n# GTEx Database Integration\n\nThis skill retrieves transcriptomics data (RNA expression baselines) and\nexpression Quantitative Trait Loci (eQTLs) from the GTEx Portal API V2. It\nprovides access to median TPM (Transcripts Per Million) values for genes and\nsignificant eQTLs for variants across 54 human tissue sites.\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\u002Fgtex_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\u002Fgtexportal.org\u002Fhome\u002Flicense and\n    https:\u002F\u002Fgtexportal.org\u002Fhome\u002FdocumentationPage#gtexApi, then (2) create the\n    file recording the notification text and timestamp.\n\n## When to Use\n\n**Use this skill when you need to:**\n\n-   Map a gene symbol to its Versioned GENCODE ID.\n-   Retrieve the baseline median expression level (in TPM) of a gene across\n    various tissues.\n-   Find the top tissues where a particular gene is most highly expressed.\n-   Fetch significant single-tissue eQTLs for a variant or within a chromosomal\n    window.\n-   Get all significant eQTLs associated with a specific gene.\n-   Contextualise a variant within GWAS loci using eQTL data.\n\n**Do NOT use when you need to:**\n\n-   Query for protein-level expression or post-translational modifications\n    (PTMs). GTEx only measures mRNA abundance.\n-   Query gene expression in diseased tissues (e.g., tumor samples, cirrhosis).\n    GTEx is a baseline atlas of normal, non-diseased tissues.\n-   Query embryonic or fetal gene expression. GTEx donors are adults only.\n\n## Core Rules\n\n**CRITICAL**: You MUST respect GTEx Portal API Terms of Use.\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-   Limit requests to maximum 250 items per page where applicable.\n-   **Notification**: If this skill is used, ensure this is mentioned in the\n    output.\n\n## Command Selection Guide\n\n**Pick the right command on the first try.** Match the user's input to the\ncorrect subcommand below.\n\n-   Map a gene symbol to GENCODE ID: `resolve-gencode-id`\n-   Get median expression (TPM) for a gene: `get-median-expression`\n-   Find tissues with highest expression for a gene: `get-top-expressed-tissues`\n-   Get all eQTLs for a specific gene: `get-gene-eqtls`\n-   Find eQTLs within a chromosomal region: `get-eqtls-in-region`\n\n## Quick Start\n\n```bash\n# Map the TNF gene symbol to its GENCODE ID\nuv run scripts\u002Fgtex_cli.py resolve-gencode-id TNF --output \u002Ftmp\u002Ftnf_id.json\n\n# Get median expression of a gene by GENCODE ID\nuv run scripts\u002Fgtex_cli.py get-median-expression ENSG00000232810.2 --output \u002Ftmp\u002Ftnf_expr.json\n```\n\nAll subcommands write JSON to disk. Always save output in the `\u002Ftmp\u002F` directory.\nThe default output file is `\u002Ftmp\u002Fgtex_output.json` if `--output` is not\nspecified.\n\n## Commands\n\n### 1. `resolve-gencode-id` — Gene Symbol → GENCODE ID\n\nMaps a standard gene symbol (e.g., \"JUN\", \"TNF\") to its Versioned GENCODE ID.\nThis ID is required for all other expression and eQTL calls.\n\n```bash\nuv run scripts\u002Fgtex_cli.py resolve-gencode-id TNF --output \u002Ftmp\u002Ftnf_id.json\n```\n\n*Arguments:*\n\n-   `gene_symbol` (positional): The standard gene symbol (e.g., \"TNF\").\n-   `--output`: Output file path (default: `\u002Ftmp\u002Fgtex_output.json`).\n\n### 2. `get-median-expression` — Get Median Expression (TPM)\n\nRetrieves the median TPM for a gene across all 54 GTEx tissue sites or specified\ntissues.\n\n```bash\nuv run scripts\u002Fgtex_cli.py get-median-expression ENSG00000232810.2 \\\n  --tissues \"Whole Blood,Spleen\" --output \u002Ftmp\u002Fexpr.json\n```\n\n*Arguments:*\n\n-   `gencode_id` (positional): The Versioned GENCODE ID.\n-   `--tissues`: Comma-separated list of tissue IDs (optional, defaults to all\n    54 tissues).\n-   `--output`: Output file path (default: `\u002Ftmp\u002Fgtex_output.json`).\n\n### 3. `get-top-expressed-tissues` — Get Top Expressed Tissues\n\nReturns the `n` tissues with the highest median expression for the target gene.\n\n```bash\nuv run scripts\u002Fgtex_cli.py get-top-expressed-tissues ENSG00000232810.2 \\\n  --n 5 --output \u002Ftmp\u002Ftop_tissues.json\n```\n\n*Arguments:*\n\n-   `gencode_id` (positional): The Versioned GENCODE ID.\n-   `--n`: Number of top tissues to return (default: 5).\n-   `--output`: Output file path.\n\n### 4. `get-gene-eqtls` — Get All eQTLs for a Gene\n\nReturns every significant eQTL associated with the gene across specified\ntissues.\n\n```bash\nuv run scripts\u002Fgtex_cli.py get-gene-eqtls ENSG00000232810.2 \\\n  --tissues \"Whole Blood\" --output \u002Ftmp\u002Feqtls.json\n```\n\n*Arguments:*\n\n-   `gencode_id` (positional): The Versioned GENCODE ID.\n-   `--tissues`: Comma-separated list of tissue IDs (optional, defaults to all).\n-   `--output`: Output file path.\n\n### 5. `get-eqtls-in-region` — Get eQTLs in Chromosomal Region\n\nReturns all significant single-tissue eQTLs within a chromosomal window (up to\n8Mb).\n\n```bash\nuv run scripts\u002Fgtex_cli.py get-eqtls-in-region chr17 7000000 7100000 \"Esophagus - Muscularis\" \\\n  --output \u002Ftmp\u002Fregion_eqtls.json\n```\n\n*Arguments:*\n\n-   `chromosome` (positional): Chromosome name (e.g., `chr17`).\n-   `start` (positional): Start position.\n-   `end` (positional): End position (max 8Mb from start).\n-   `tissue_id` (positional): The target tissue ID.\n-   `--output`: Output file path.\n\n## Typical Workflows\n\n### Identify highest expressing tissues for a gene\n\n```bash\n# Step 1: Map symbol to GENCODE ID\nuv run scripts\u002Fgtex_cli.py resolve-gencode-id GATA4 --output \u002Ftmp\u002Fgata4_id.json\n\n# Step 2: Query for top tissues using the resolved ID\nuv run scripts\u002Fgtex_cli.py get-top-expressed-tissues \u003Cgencode_id> --n 5 \\\n  --output \u002Ftmp\u002Fgata4_top.json\n```\n",{"data":37,"body":38},{"name":4,"description":6},{"type":39,"children":40},"root",[41,50,56,63,126,132,140,174,182,200,206,216,244,250,260,318,324,441,470,476,490,495,533,542,573,586,591,660,667,707,720,733,791,798,831,844,849,913,920,951,964,969,1041,1048,1111,1117,1123,1251],{"type":42,"tag":43,"props":44,"children":46},"element","h1",{"id":45},"gtex-database-integration",[47],{"type":48,"value":49},"text","GTEx Database Integration",{"type":42,"tag":51,"props":52,"children":53},"p",{},[54],{"type":48,"value":55},"This skill retrieves transcriptomics data (RNA expression baselines) and\nexpression Quantitative Trait Loci (eQTLs) from the GTEx Portal API V2. 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",{"type":42,"tag":76,"props":302,"children":304},{"className":303},[],[305],{"type":48,"value":306},"get-gene-eqtls",{"type":42,"tag":68,"props":308,"children":309},{},[310,312],{"type":48,"value":311},"Find eQTLs within a chromosomal region: ",{"type":42,"tag":76,"props":313,"children":315},{"className":314},[],[316],{"type":48,"value":317},"get-eqtls-in-region",{"type":42,"tag":57,"props":319,"children":321},{"id":320},"quick-start",[322],{"type":48,"value":323},"Quick Start",{"type":42,"tag":325,"props":326,"children":331},"pre",{"className":327,"code":328,"language":329,"meta":330,"style":330},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Map the TNF gene symbol to its GENCODE ID\nuv run scripts\u002Fgtex_cli.py resolve-gencode-id TNF --output \u002Ftmp\u002Ftnf_id.json\n\n# Get median expression of a gene by GENCODE ID\nuv run scripts\u002Fgtex_cli.py get-median-expression ENSG00000232810.2 --output 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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":1258,"total":1420},[1259,1272,1285,1305,1317,1332,1348,1361,1373,1388,1399,1409],{"slug":1260,"name":1260,"fn":1261,"description":1262,"org":1263,"tags":1264,"stars":25,"repoUrl":26,"updatedAt":1271},"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},[1265,1266,1269,1270],{"name":20,"slug":21,"type":15},{"name":1267,"slug":1268,"type":15},"Genomics","genomics",{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:51.827211",{"slug":1273,"name":1273,"fn":1274,"description":1275,"org":1276,"tags":1277,"stars":25,"repoUrl":26,"updatedAt":1284},"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},[1278,1279,1282,1283],{"name":20,"slug":21,"type":15},{"name":1280,"slug":1281,"type":15},"Genetics","genetics",{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},"2026-07-12T07:51:39.494803",{"slug":1286,"name":1286,"fn":1287,"description":1288,"org":1289,"tags":1290,"stars":25,"repoUrl":26,"updatedAt":1304},"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},[1291,1294,1297,1300,1303],{"name":1292,"slug":1293,"type":15},"ChEMBL","chembl",{"name":1295,"slug":1296,"type":15},"Chemistry","chemistry",{"name":1298,"slug":1299,"type":15},"Database","database",{"name":1301,"slug":1302,"type":15},"Pharmacology","pharmacology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:35.544306",{"slug":1306,"name":1306,"fn":1307,"description":1308,"org":1309,"tags":1310,"stars":25,"repoUrl":26,"updatedAt":1316},"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},[1311,1314,1315],{"name":1312,"slug":1313,"type":15},"Clinical Trials","clinical-trials",{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:06.846705",{"slug":1318,"name":1318,"fn":1319,"description":1320,"org":1321,"tags":1322,"stars":25,"repoUrl":26,"updatedAt":1331},"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},[1323,1326,1327,1330],{"name":1324,"slug":1325,"type":15},"ClinVar","clinvar",{"name":1280,"slug":1281,"type":15},{"name":1328,"slug":1329,"type":15},"Healthcare","healthcare",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:36.86094",{"slug":1333,"name":1333,"fn":1334,"description":1335,"org":1336,"tags":1337,"stars":25,"repoUrl":26,"updatedAt":1347},"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},[1338,1341,1344],{"name":1339,"slug":1340,"type":15},"Compliance","compliance",{"name":1342,"slug":1343,"type":15},"Operations","operations",{"name":1345,"slug":1346,"type":15},"Security","security","2026-07-12T07:52:17.355491",{"slug":1349,"name":1349,"fn":1350,"description":1351,"org":1352,"tags":1353,"stars":25,"repoUrl":26,"updatedAt":1360},"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},[1354,1355,1356,1359],{"name":20,"slug":21,"type":15},{"name":1280,"slug":1281,"type":15},{"name":1357,"slug":1358,"type":15},"NCBI","ncbi",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:33.054229",{"slug":1362,"name":1362,"fn":1363,"description":1364,"org":1365,"tags":1366,"stars":25,"repoUrl":26,"updatedAt":1372},"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},[1367,1368,1371],{"name":20,"slug":21,"type":15},{"name":1369,"slug":1370,"type":15},"Ontology","ontology",{"name":13,"slug":14,"type":15},"2026-07-12T07:51:59.368324",{"slug":1374,"name":1374,"fn":1375,"description":1376,"org":1377,"tags":1378,"stars":25,"repoUrl":26,"updatedAt":1387},"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},[1379,1380,1383,1384],{"name":20,"slug":21,"type":15},{"name":1381,"slug":1382,"type":15},"GraphQL","graphql",{"name":13,"slug":14,"type":15},{"name":1385,"slug":1386,"type":15},"REST API","rest-api","2026-07-12T07:52:10.597139",{"slug":1389,"name":1389,"fn":1390,"description":1391,"org":1392,"tags":1393,"stars":25,"repoUrl":26,"updatedAt":1398},"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},[1394,1395,1396,1397],{"name":20,"slug":21,"type":15},{"name":1280,"slug":1281,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:41.645835",{"slug":1400,"name":1400,"fn":1401,"description":1402,"org":1403,"tags":1404,"stars":25,"repoUrl":26,"updatedAt":1408},"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},[1405,1406,1407],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:52:09.354992",{"slug":1410,"name":1410,"fn":1411,"description":1412,"org":1413,"tags":1414,"stars":25,"repoUrl":26,"updatedAt":1419},"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},[1415,1416,1417,1418],{"name":20,"slug":21,"type":15},{"name":1280,"slug":1281,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-07-12T07:51:38.213009",38,{"items":1422,"total":1420},[1423,1430,1437,1445,1451,1458,1464],{"slug":1260,"name":1260,"fn":1261,"description":1262,"org":1424,"tags":1425,"stars":25,"repoUrl":26,"updatedAt":1271},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1426,1427,1428,1429],{"name":20,"slug":21,"type":15},{"name":1267,"slug":1268,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":1273,"name":1273,"fn":1274,"description":1275,"org":1431,"tags":1432,"stars":25,"repoUrl":26,"updatedAt":1284},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1433,1434,1435,1436],{"name":20,"slug":21,"type":15},{"name":1280,"slug":1281,"type":15},{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},{"slug":1286,"name":1286,"fn":1287,"description":1288,"org":1438,"tags":1439,"stars":25,"repoUrl":26,"updatedAt":1304},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1440,1441,1442,1443,1444],{"name":1292,"slug":1293,"type":15},{"name":1295,"slug":1296,"type":15},{"name":1298,"slug":1299,"type":15},{"name":1301,"slug":1302,"type":15},{"name":13,"slug":14,"type":15},{"slug":1306,"name":1306,"fn":1307,"description":1308,"org":1446,"tags":1447,"stars":25,"repoUrl":26,"updatedAt":1316},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1448,1449,1450],{"name":1312,"slug":1313,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"slug":1318,"name":1318,"fn":1319,"description":1320,"org":1452,"tags":1453,"stars":25,"repoUrl":26,"updatedAt":1331},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1454,1455,1456,1457],{"name":1324,"slug":1325,"type":15},{"name":1280,"slug":1281,"type":15},{"name":1328,"slug":1329,"type":15},{"name":13,"slug":14,"type":15},{"slug":1333,"name":1333,"fn":1334,"description":1335,"org":1459,"tags":1460,"stars":25,"repoUrl":26,"updatedAt":1347},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1461,1462,1463],{"name":1339,"slug":1340,"type":15},{"name":1342,"slug":1343,"type":15},{"name":1345,"slug":1346,"type":15},{"slug":1349,"name":1349,"fn":1350,"description":1351,"org":1465,"tags":1466,"stars":25,"repoUrl":26,"updatedAt":1360},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1467,1468,1469,1470],{"name":20,"slug":21,"type":15},{"name":1280,"slug":1281,"type":15},{"name":1357,"slug":1358,"type":15},{"name":13,"slug":14,"type":15}]