epigenomic_landscape: Скилл для анализа эпигенома

--- name: epigenomic_landscape description: "Epigenomic Landscape Mapping - Map epigenomic landscape: overlapping features, regulatory elements, binding matrices, and phenotype links. Use this skill for epigenomics tasks involving get overlap region get phenotype region get species binding matrix get track data. Combines 4 tools from 2 SCP server(s)." --- # Epigenomic Landscape Mapping **Discipline**: Epigenomics | **Tools Used**: 4 | **Servers**: 2 ## Description Map epigenomic landscape: overlapping features, regulatory elements, binding matrices, and phenotype links. ## Tools Used - **`get_overlap_region`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_phenotype_region`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_species_binding_matrix`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_track_data`** from `ucsc-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC` ## Workflow 1. Get overlapping regulatory features 2. Get phenotype associations in region 3. Get binding matrix data 4. Get UCSC epigenomic track data ## Test Case ### Input ```json { "region": "17:43044295-43125370", "species": "homo_sapiens", "genome": "hg38" } ``` ### Expected Steps 1. Get overlapping regulatory features 2. Get phenotype associations in region 3. Get binding matrix data 4. Get UCSC epigenomic track data ## 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", "ucsc-server": "https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC" } 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["ucsc-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC", stack) # Execute workflow steps # Step 1: Get overlapping regulatory features 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: Get phenotype associations in region result_2 = await sessions["ensembl-server"].call_tool("get_phenotype_region", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get binding matrix data result_3 = await sessions["ensembl-server"].call_tool("get_species_binding_matrix", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Get UCSC epigenomic track data result_4 = await sessions["ucsc-server"].call_tool("get_track_data", 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()) ```