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multispecies_gene_analysis

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Multi-Species Gene Analysis - Analyze gene across species: Ensembl homologs, NCBI orthologs, cross-species STRING similarity, and taxonomy. Use this skill for comparative genomics tasks involving get homology symbol get gene orthologs get best similarity hits between species get taxonomy. Combines 4 tools from 3 SCP server(s).

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Multi-Species Gene Analysis

Discipline: Comparative Genomics | Tools Used: 4 | Servers: 3

Description

Analyze gene across species: Ensembl homologs, NCBI orthologs, cross-species STRING similarity, and taxonomy.

Tools Used

  • get_homology_symbol from ensembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl
  • get_gene_orthologs from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
  • get_best_similarity_hits_between_species from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING
  • get_taxonomy from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI

Workflow

  1. Get Ensembl homologs
  2. Get NCBI orthologs
  3. Get STRING cross-species similarity
  4. Get taxonomy for comparison

Test Case

Input

{
    "gene": "TP53",
    "species": "homo_sapiens",
    "gene_id": 7157
}

Expected Steps

  1. Get Ensembl homologs
  2. Get NCBI orthologs
  3. Get STRING cross-species similarity
  4. Get taxonomy for comparison

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.

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",
    "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI",
    "string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING"
}

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["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack)
        sessions["string-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", stack)

        # Execute workflow steps
        # Step 1: Get Ensembl homologs
        result_1 = await sessions["ensembl-server"].call_tool("get_homology_symbol", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get NCBI orthologs
        result_2 = await sessions["ncbi-server"].call_tool("get_gene_orthologs", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Get STRING cross-species similarity
        result_3 = await sessions["string-server"].call_tool("get_best_similarity_hits_between_species", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Get taxonomy for comparison
        result_4 = await sessions["ncbi-server"].call_tool("get_taxonomy", 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())