epigenetics_drug: Скилл анализа эпигенетики и лекарств

--- name: epigenetics_drug description: "Epigenetics & Drug Response - Link epigenetics to drug response: gene regulation, variant effects, drug interactions, and expression. Use this skill for epigenetic pharmacology tasks involving get overlap region get vep hgvs get drug interactions by drug name get gene expression across cancers. Combines 4 tools from 3 SCP server(s)." --- # Epigenetics & Drug Response **Discipline**: Epigenetic Pharmacology | **Tools Used**: 4 | **Servers**: 3 ## Description Link epigenetics to drug response: gene regulation, variant effects, drug interactions, and expression. ## Tools Used - **`get_overlap_region`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_vep_hgvs`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_drug_interactions_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug` - **`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 regulatory overlap 2. Predict variant effects 3. Check drug interactions 4. Analyze gene expression ## Test Case ### Input ```json { "region": "7:140753336-140753436", "drug": "vemurafenib", "gene": "BRAF" } ``` ### Expected Steps 1. Get regulatory overlap 2. Predict variant effects 3. Check drug interactions 4. Analyze gene expression ## 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", "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "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["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", 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 regulatory overlap result_1 = await sessions["ensembl-server"].call_tool("get_overlap_region", 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 effects 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: Check drug interactions result_3 = await sessions["fda-drug-server"].call_tool("get_drug_interactions_by_drug_name", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Analyze gene expression 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()) ```