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internscience/scp/multispecies-gene-analysis

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version: "1.0.0" name: multispecies_gene_analysis description: "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)."

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

Get Ensembl homologs
Get NCBI orthologs
Get STRING cross-species similarity
Get taxonomy for comparison

Test Case

Input

json

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

Expected Steps

Get Ensembl homologs
Get NCBI orthologs
Get STRING cross-species similarity
Get taxonomy for comparison

Usage Example

Note: Replace <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.

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 = {
    "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, 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["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")
    sessions["ncbi-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "streamable-http")
    sessions["string-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", "streamable-http")
    # 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())

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