Mouse Model Analysis

---
name: mouse_model_analysis
description: 'Mouse Model Disease Analysis - Analyze mouse disease models: MouseMine
  search, NCBI mouse gene data, Ensembl cross-species comparison, and orthologs. Use
  this skill for model organisms tasks involving mousemine search get gene metadata
  by gene name get homology symbol get gene orthologs. Combines 4 tools from 3 SCP
  server(s).'
i18n:
  zh:
    description: 小鼠模型疾病分析。
---

# Mouse Model Disease Analysis

**Discipline**: Model Organisms | **Tools Used**: 4 | **Servers**: 3

## Description

Analyze mouse disease models: MouseMine search, NCBI mouse gene data, Ensembl cross-species comparison, and orthologs.

## Tools Used

- **`mousemine_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search`
- **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI`
- **`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`

## Workflow

1. Search MouseMine
2. Get mouse gene data
3. Find human-mouse homologs
4. Get gene orthologs

## Test Case

### Input
```json
{
    "query": "Trp53 tumor mouse model",
    "gene": "TP53"
}
```

### Expected Steps
1. Search MouseMine
2. Get mouse gene data
3. Find human-mouse homologs
4. Get gene orthologs

## 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 = {
    "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search",
    "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"
}

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["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http")
    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")

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

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

    # Step 3: Find human-mouse homologs
    result_3 = await sessions["ensembl-server"].call_tool("get_homology_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 gene orthologs
    result_4 = await sessions["ncbi-server"].call_tool("get_gene_orthologs", 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())
```