tissue_specific_analysis: Скилл анализа экспрессии генов

--- name: tissue_specific_analysis description: "Tissue-Specific Expression Analysis - Analyze tissue-specific expression: ChEMBL tissue data, TCGA cancer expression, Ensembl gene info, and NCBI gene data. Use this skill for tissue biology tasks involving get tissue by id get gene expression across cancers get lookup symbol get gene metadata by gene name. Combines 4 tools from 4 SCP server(s)." --- # Tissue-Specific Expression Analysis **Discipline**: Tissue Biology | **Tools Used**: 4 | **Servers**: 4 ## Description Analyze tissue-specific expression: ChEMBL tissue data, TCGA cancer expression, Ensembl gene info, and NCBI gene data. ## Tools Used - **`get_tissue_by_id`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL` - **`get_gene_expression_across_cancers`** from `tcga-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA` - **`get_lookup_symbol`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` ## Workflow 1. Get ChEMBL tissue info 2. Get TCGA cancer expression 3. Get Ensembl gene info 4. Get NCBI gene metadata ## Test Case ### Input ```json { "gene": "EGFR", "tissue_id": "CHEMBL3559723" } ``` ### Expected Steps 1. Get ChEMBL tissue info 2. Get TCGA cancer expression 3. Get Ensembl gene info 4. Get NCBI gene metadata ## 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 = { "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI" } 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["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) sessions["tcga-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", stack) sessions["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack) sessions["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack) # Execute workflow steps # Step 1: Get ChEMBL tissue info result_1 = await sessions["chembl-server"].call_tool("get_tissue_by_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 TCGA cancer expression result_2 = await sessions["tcga-server"].call_tool("get_gene_expression_across_cancers", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get Ensembl gene info result_3 = await sessions["ensembl-server"].call_tool("get_lookup_symbol", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Get NCBI gene metadata result_4 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", 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()) ```