genome-annotation

# Genome Annotation Pipeline **Discipline**: Genomics | **Tools Used**: 4 | **Servers**: 4 ## Description Annotate a genome: NCBI annotation report, Ensembl gene lookup, UCSC tracks, and KEGG pathway links. ## Tools Used - **`get_genome_annotation_report`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` - **`get_lookup_symbol`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl` - **`list_tracks`** from `ucsc-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC` - **`kegg_link`** from `kegg-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG` ## Workflow 1. Get NCBI genome annotation 2. Look up gene in Ensembl 3. List UCSC tracks 4. Link to KEGG pathways ## Test Case ### Input ```json { "accession": "GCF_000001405.40", "gene_symbol": "BRCA1", "genome": "hg38" } ``` ### Expected Steps 1. Get NCBI genome annotation 2. Look up gene in Ensembl 3. List UCSC tracks 4. Link to KEGG pathways ## Usage Example > **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` 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 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", "ucsc-server": "https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC", "kegg-server": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG" } async def connect(url, transport_type): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"}) read, write, _ = await transport.__aenter__() ctx = ClientSession(read, write) session = await ctx.__aenter__() await session.initialize() return session, ctx, transport 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(): # Connect to required servers sessions = {} sessions["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http") sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http") sessions["ucsc-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/13/Origene-UCSC", "streamable-http") sessions["kegg-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", "streamable-http") # Execute workflow steps # Step 1: Get NCBI genome annotation result_1 = await sessions["ncbi-server"].call_tool("get_genome_annotation_report", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Look up gene in Ensembl result_2 = await sessions["ensembl-server"].call_tool("get_lookup_symbol", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: List UCSC tracks result_3 = await sessions["ucsc-server"].call_tool("list_tracks", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Link to KEGG pathways result_4 = await sessions["kegg-server"].call_tool("kegg_link", 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()) ```