gene_therapy_target: Скилл анализа мишеней генной терапии

--- name: gene_therapy_target description: "Gene Therapy Target Analysis - Analyze gene therapy target: gene info, variant pathogenicity, protein structure, and clinical evidence. Use this skill for gene therapy tasks involving get gene metadata by gene name get vep hgvs Protein structure prediction ESMFold clinvar search. Combines 4 tools from 4 SCP server(s)." --- # Gene Therapy Target Analysis **Discipline**: Gene Therapy | **Tools Used**: 4 | **Servers**: 4 ## Description Analyze gene therapy target: gene info, variant pathogenicity, protein structure, and clinical evidence. ## Tools Used - **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` - **`get_vep_hgvs`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`Protein_structure_prediction_ESMFold`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` - **`clinvar_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search` ## Workflow 1. Get gene info 2. Predict variant effect 3. Predict protein structure 4. Search ClinVar pathogenicity ## Test Case ### Input ```json { "gene": "CFTR", "hgvs": "ENSP00000003084.6:p.Phe508del" } ``` ### Expected Steps 1. Get gene info 2. Predict variant effect 3. Predict protein structure 4. Search ClinVar pathogenicity ## 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 = { "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search" } 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["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack) sessions["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack) sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack) # Execute workflow steps # Step 1: Get gene info result_1 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_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 variant effect 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: Predict protein structure result_3 = await sessions["server-1"].call_tool("Protein_structure_prediction_ESMFold", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Search ClinVar pathogenicity result_4 = await sessions["search-server"].call_tool("clinvar_search", 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()) ```