proteome-analysis

Proteome-Level Analysis - Analyze at proteome level: get proteome from UniProt, gene-centric view, functional annotation from STRING. Use this skill for proteomics tasks involving get proteome by id get gene centric by proteome get functional annotation. Combines 3 tools from 2 SCP server(s).

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저장소: SpectrAI-Initiative/InnoClaw
최근 소스 활동: 2026년 4월 1일 17:03
감지된 SKILL.md 언어: 영어
스타: 390
포크: 28

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npx skills add https://github.com/SpectrAI-Initiative/InnoClaw --skill proteome-analysis

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이 저장소의 모든 Skills 보기

name	proteome_analysis
description	Proteome-Level Analysis - Analyze at proteome level: get proteome from UniProt, gene-centric view, functional annotation from STRING. Use this skill for proteomics tasks involving get proteome by id get gene centric by proteome get functional annotation. Combines 3 tools from 2 SCP server(s).

Proteome-Level Analysis

Discipline: Proteomics | Tools Used: 3 | Servers: 2

Description

Analyze at proteome level: get proteome from UniProt, gene-centric view, functional annotation from STRING.

Tools Used

get_proteome_by_id from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt
get_gene_centric_by_proteome from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt
get_functional_annotation from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING

Workflow

Get human proteome info
Get gene-centric view
Run functional annotation on key proteins

Test Case

Input

{
    "proteome_id": "UP000005640"
}

Expected Steps

Get human proteome info
Get gene-centric view
Run functional annotation on key proteins

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 = { "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "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["uniprot-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", 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 human proteome info result_1 = await sessions["uniprot-server"].call_tool("get_proteome_by_id", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Get gene-centric view result_2 = await sessions["uniprot-server"].call_tool("get_gene_centric_by_proteome", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Run functional annotation on key proteins result_3 = await sessions["string-server"].call_tool("get_functional_annotation", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main())

proteome-analysis

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이 저장소의 다른 Skills

이 저장소의 다른 Skills

Proteome-Level Analysis

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Proteome-Level Analysis

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example