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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".
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Run BLAST sequence similarity searches. Use when the user asks to BLAST a sequence, find similar sequences, identify a gene/protein, or do homology search. Triggers on "blast", "sequence similarity", "homology", "identify sequence".
Search PubMed for scientific literature. Use when the user asks to find papers, search literature, look up research, find publications, or asks about recent studies. Triggers on "pubmed", "papers", "literature", "publications", "research on", "studies about".
Query AlphaFold protein structure predictions. Use when user asks about protein structure, 3D structure, protein folding, or structure prediction. Triggers on "alphafold", "protein structure", "3D structure", "folding", "pLDDT", "structure prediction".
Query ClinVar for clinical variant significance. Use when user asks about variant pathogenicity, genetic variants, clinical significance, or disease-causing mutations. Triggers on "clinvar", "pathogenic", "variant significance", "clinical significance", "disease variant", "mutation pathogenicity".
Query NCBI GEO for gene expression datasets. Use when user asks about RNA-seq datasets, microarray data, expression data, GEO accessions, or finding public datasets. Triggers on "geo", "gene expression omnibus", "expression dataset", "RNA-seq dataset", "microarray dataset", "GSE", "GDS".
| 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". |
Query the STRING API for protein-protein interaction networks.
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)}")
Human=9606, Mouse=10090, Rat=10116, Fly=7227, Yeast=4932, E.coli=511145