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Skill v1.0.1
currentAutomated scan100/100internscience/scp/pharmacogenomics-analysis
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version: "1.0.1" name: pharmacogenomics_analysis description: "Pharmacogenomics Analysis - Pharmacogenomics analysis: FDA pharmacogenomics info, variant effects, ClinVar pathogenicity, and gene expression. Use this skill for pharmacogenomics tasks involving get pharmacogenomics info by drug name get vep hgvs clinvar search get gene expression across cancers. Combines 4 tools from 4 SCP server(s)."
Pharmacogenomics Analysis
Discipline: Pharmacogenomics | Tools Used: 4 | Servers: 4
Description
Pharmacogenomics analysis: FDA pharmacogenomics info, variant effects, ClinVar pathogenicity, and gene expression.
Tools Used
- `get_pharmacogenomics_info_by_drug_name` from
fda-drug-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug - `get_vep_hgvs` from
ensembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl - `clinvar_search` from
search-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search - `get_gene_expression_across_cancers` from
tcga-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA
Workflow
- Get FDA pharmacogenomics info
- Predict effect of CYP2C9 variants
- Search ClinVar for CYP2C9 variants
- Check CYP2C9 expression across cancers
Test Case
Input
json
{"drug_name": "warfarin","gene": "CYP2C9"}
Expected Steps
- Get FDA pharmacogenomics info
- Predict effect of CYP2C9 variants
- Search ClinVar for CYP2C9 variants
- Check CYP2C9 expression across cancers
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 asyncioimport jsonfrom mcp import ClientSessionfrom mcp.client.streamable_http import streamablehttp_clientfrom mcp.client.sse import sse_clientSERVERS = {"fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug","ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl","search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search","tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA"}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, transportdef 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.textreturn str(result)except: return str(result)async def main():# Connect to required serverssessions = {}sessions["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")sessions["ensembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", "streamable-http")sessions["search-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", "streamable-http")sessions["tcga-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "streamable-http")# Execute workflow steps# Step 1: Get FDA pharmacogenomics inforesult_1 = await sessions["fda-drug-server"].call_tool("get_pharmacogenomics_info_by_drug_name", arguments={})data_1 = parse(result_1)print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")# Step 2: Predict effect of CYP2C9 variantsresult_2 = await sessions["ensembl-server"].call_tool("get_vep_hgvs", arguments={})data_2 = parse(result_2)print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")# Step 3: Search ClinVar for CYP2C9 variantsresult_3 = await sessions["search-server"].call_tool("clinvar_search", arguments={})data_3 = parse(result_3)print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")# Step 4: Check CYP2C9 expression across cancersresult_4 = await sessions["tcga-server"].call_tool("get_gene_expression_across_cancers", arguments={})data_4 = parse(result_4)print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")# Cleanupprint("Workflow complete!")if __name__ == "__main__":asyncio.run(main())