pharmacogenomics_analysis: Скилл для анализа генов и лекарств

--- name: pharmacogenomics_analysis description: "Pharmacogenomics Analysis - Pharmacogenomics analysis: FDA pharmacogenomics info, variant effects, ClinVar pathogenicity, and gene expression. Use this skill for pharmacogenomics tasks involving get pharmacogenomics info by drug name get vep hgvs clinvar search get gene expression across cancers. Combines 4 tools from 4 SCP server(s)." --- # Pharmacogenomics Analysis **Discipline**: Pharmacogenomics | **Tools Used**: 4 | **Servers**: 4 ## Description Pharmacogenomics analysis: FDA pharmacogenomics info, variant effects, ClinVar pathogenicity, and gene expression. ## Tools Used - **`get_pharmacogenomics_info_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug` - **`get_vep_hgvs`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`clinvar_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search` - **`get_gene_expression_across_cancers`** from `tcga-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA` ## Workflow 1. Get FDA pharmacogenomics info 2. Predict effect of CYP2C9 variants 3. Search ClinVar for CYP2C9 variants 4. Check CYP2C9 expression across cancers ## Test Case ### Input ```json { "drug_name": "warfarin", "gene": "CYP2C9" } ``` ### Expected Steps 1. Get FDA pharmacogenomics info 2. Predict effect of CYP2C9 variants 3. Search ClinVar for CYP2C9 variants 4. Check CYP2C9 expression across cancers ## Usage Example > **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python import asyncio import json from contextlib import AsyncExitStack from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from mcp.client.sse import sse_client SERVERS = { "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA" } async def connect(url, stack): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"}) read, write, _ = await stack.enter_async_context(transport) ctx = ClientSession(read, write) session = await stack.enter_async_context(ctx) await session.initialize() return session def parse(result): try: if hasattr(result, 'content') and result.content: c = result.content[0] if hasattr(c, 'text'): try: return json.loads(c.text) except: return c.text return str(result) except: return str(result) async def main(): async with AsyncExitStack() as stack: # Connect to required servers sessions = {} sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack) sessions["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack) sessions["tcga-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", stack) # Execute workflow steps # Step 1: Get FDA pharmacogenomics info result_1 = await sessions["fda-drug-server"].call_tool("get_pharmacogenomics_info_by_drug_name", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Predict effect of CYP2C9 variants result_2 = await sessions["ensembl-server"].call_tool("get_vep_hgvs", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Search ClinVar for CYP2C9 variants result_3 = await sessions["search-server"].call_tool("clinvar_search", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Check CYP2C9 expression across cancers result_4 = await sessions["tcga-server"].call_tool("get_gene_expression_across_cancers", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```