snp_functional_analysis: Скилл для анализа функциональных последствий

--- name: snp_functional_analysis description: "SNP Functional Impact Analysis - Analyze SNP function: VEP prediction, variation details, phenotype association, and literature evidence. Use this skill for functional genomics tasks involving get vep id get variation get phenotype accession pubmed search. Combines 4 tools from 2 SCP server(s)." --- # SNP Functional Impact Analysis **Discipline**: Functional Genomics | **Tools Used**: 4 | **Servers**: 2 ## Description Analyze SNP function: VEP prediction, variation details, phenotype association, and literature evidence. ## Tools Used - **`get_vep_id`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_variation`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_phenotype_accession`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`pubmed_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search` ## Workflow 1. Predict functional effects with VEP 2. Get variant details 3. Get phenotype associations 4. Search PubMed for evidence ## Test Case ### Input ```json { "variant_id": "rs1800497", "species": "homo_sapiens" } ``` ### Expected Steps 1. Predict functional effects with VEP 2. Get variant details 3. Get phenotype associations 4. Search PubMed for evidence ## 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 = { "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" } 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["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) # Execute workflow steps # Step 1: Predict functional effects with VEP result_1 = await sessions["ensembl-server"].call_tool("get_vep_id", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Get variant details result_2 = await sessions["ensembl-server"].call_tool("get_variation", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get phenotype associations result_3 = await sessions["ensembl-server"].call_tool("get_phenotype_accession", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Search PubMed for evidence result_4 = await sessions["search-server"].call_tool("pubmed_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()) ```