Agent Skills › Runchuan-BU/BioClaw › query-stringdb

query-stringdb

GitHub

用于查询STRING数据库中的蛋白质-蛋白质相互作用网络。支持获取互作伙伴、功能富集分析、网络扩展及下载网络图片，适用于生物信息学研究场景。

container/skills/query-stringdb/SKILL.md Runchuan-BU/BioClaw

Trigger Scenarios

用户询问蛋白质的互作伙伴构建相互作用网络基因间的功能关联分析查询互作置信度评分

Install

npx skills add Runchuan-BU/BioClaw --skill query-stringdb -g -y

More Options

Non-standard path

npx skills add https://github.com/Runchuan-BU/BioClaw/tree/main/container/skills/query-stringdb -g -y

Use without installing

npx skills use Runchuan-BU/BioClaw@query-stringdb

指定 Agent (Claude Code)

npx skills add Runchuan-BU/BioClaw --skill query-stringdb -a claude-code -g -y

安装 repo 全部 skill

npx skills add Runchuan-BU/BioClaw --all -g -y

预览 repo 内 skill

npx skills add Runchuan-BU/BioClaw --list

SKILL.md

Frontmatter

{
    "name": "query-stringdb",
    "description": "Query STRING for protein-protein interactions. Use when user asks about protein interactions, interaction networks, binding partners, or interactome. Triggers on \"string\", \"protein interaction\", \"interaction network\", \"binding partners\", \"interactome\", \"PPI\"."
}

STRING Protein Interaction Database

Query the STRING API for protein-protein interaction networks.

When to Use

User asks about a protein's interaction partners
User wants to build an interaction network
User asks about functional associations between genes
User wants interaction confidence scores

How to Execute

import requests
import json

BASE_URL = "https://version-12-0.string-db.org/api"

# 1. Get interaction partners
def get_interactions(genes, species=9606, score_threshold=400):
    url = f"{BASE_URL}/json/network"
    params = {
        "identifiers": "%0d".join(genes),
        "species": species,
        "required_score": score_threshold,
        "caller_identity": "bioclaw"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 2. Get functional enrichment
def get_enrichment(genes, species=9606):
    url = f"{BASE_URL}/json/enrichment"
    params = {
        "identifiers": "%0d".join(genes),
        "species": species,
        "caller_identity": "bioclaw"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 3. Get interaction partners (expand network)
def get_partners(gene, species=9606, limit=10):
    url = f"{BASE_URL}/json/interaction_partners"
    params = {
        "identifiers": gene,
        "species": species,
        "limit": limit,
        "caller_identity": "bioclaw"
    }
    r = requests.get(url, params=params)
    r.raise_for_status()
    return r.json()

# 4. Download network image
def download_network_image(genes, species=9606, output_path="/workspace/group/network.png"):
    url = f"{BASE_URL}/highres_image/network"
    params = {
        "identifiers": "%0d".join(genes),
        "species": species,
        "caller_identity": "bioclaw"
    }
    r = requests.get(url, params=params)
    with open(output_path, 'wb') as f:
        f.write(r.content)
    return output_path

# Example
interactions = get_interactions(["BRCA1", "BRCA2", "TP53"])
for i in interactions[:10]:
    print(f"{i['preferredName_A']} <-> {i['preferredName_B']}  score: {i['score']}")
    print(f"  Sources: experimental={i.get('escore',0)}, database={i.get('dscore',0)}, textmining={i.get('tscore',0)}")