| name | scientific-string-network-api |
| description | STRING/BioGRID/STITCH ネットワーク解析スキル。STRING タンパク質相互作用
ネットワーク直接 API、BioGRID 実験的 PPI、STITCH 化学-タンパク質ネットワーク、
ネットワークトポロジー解析・コミュニティ検出・機能濃縮統合パイプライン。
|
| tu_tools | [{"key":"ppi","name":"STRING/BioGRID PPI","description":"タンパク質・化学物質相互作用ネットワーク"}] |
Scientific STRING Network API
STRING v12 / BioGRID / STITCH API を活用した PPI・化合物-タンパク質
ネットワーク解析パイプラインを提供する。既存の protein-interaction-network
スキル (IntAct/HumanBase) を補完し、STRING 直接 API ベースの高度な
ネットワーク分析を統合。
When to Use
- STRING API でタンパク質相互作用ネットワークを直接構築するとき
- BioGRID から実験的エビデンスベースの PPI を取得するとき
- STITCH で化合物-タンパク質間ネットワークを検索するとき
- ネットワークトポロジー指標 (次数分布・媒介中心性) を計算するとき
- PPI ネットワーク上でコミュニティ検出を行うとき
- 機能濃縮解析 (STRING enrichment) をネットワーク上で実行するとき
Quick Start
1. STRING PPI ネットワーク取得
import requests
import pandas as pd
import networkx as nx
STRING_API = "https://string-db.org/api"
OUTPUT_FORMAT = "json"
def get_string_network(proteins, species=9606, score_threshold=400,
network_type="functional", limit=50):
"""
STRING PPI ネットワーク取得。
Parameters:
proteins: list — タンパク質名リスト (例: ["TP53", "MDM2", "BRCA1"])
species: int — NCBI Taxonomy ID (9606=human)
score_threshold: int — 信頼スコア閾値 (0-1000)
network_type: str — "functional" or "physical"
limit: int — interactor 最大数
ToolUniverse:
STRING_get_protein_interactions(
protein_ids=proteins, species=species,
confidence_score=score_threshold/1000,
network_type=network_type, limit=limit
)
"""
url = f"{STRING_API}/{OUTPUT_FORMAT}/network"
params = {
"identifiers": "\r".join(proteins),
"species": species,
"required_score": score_threshold,
"network_type": network_type,
"limit": limit,
}
resp = requests.post(url, data=params)
resp.raise_for_status()
interactions = resp.json()
rows = []
for i in interactions:
rows.append({
"protein_a": i.get("preferredName_A"),
"protein_b": i.get("preferredName_B"),
"combined_score": i.get("score"),
"nscore": i.get("nscore"),
"fscore": i.get("fscore"),
"pscore": i.get("pscore"),
"ascore": i.get("ascore"),
"escore": i.get("escore"),
"dscore": i.get("dscore"),
"tscore": i.get("tscore"),
})
df = pd.DataFrame(rows)
print(f"STRING network: {len(df)} interactions "
f"(score ≥ {score_threshold/1000})")
return df
2. BioGRID 実験的 PPI 取得
def get_biogrid_interactions(genes, organism=9606, evidence_type=None,
api_key="YOUR_KEY", limit=500):
"""
BioGRID 実験的 PPI データ取得。
Parameters:
genes: list — 遺伝子名リスト
organism: int — NCBI Taxonomy ID
evidence_type: str — "physical" or "genetic"
api_key: str — BioGRID API key (https://webservice.thebiogrid.org)
limit: int — 最大取得数
ToolUniverse:
BioGRID_get_interactions(
gene_names=genes, organism=organism,
interaction_type=evidence_type, limit=limit
)
"""
url = "https://webservice.thebiogrid.org/interactions"
params = {
"accessKey": api_key,
"geneList": "|".join(genes),
"organism": organism,
"format": "json",
"max": limit,
"searchNames": "true",
"includeInteractors": "true",
}
if evidence_type:
params["interSpeciesExcluded"] = "true"
resp = requests.get(url, params=params)
resp.raise_for_status()
data = resp.json()
rows = []
for _, interaction in data.items():
rows.append({
"gene_a": interaction.get("OFFICIAL_SYMBOL_A"),
"gene_b": interaction.get("OFFICIAL_SYMBOL_B"),
"experimental_system": interaction.get("EXPERIMENTAL_SYSTEM"),
"throughput": interaction.get("THROUGHPUT"),
"pubmed_id": interaction.get("PUBMED_ID"),
"source_db": ,
})
df = pd.DataFrame(rows)
()
df
3. STITCH 化合物-タンパク質ネットワーク
def get_stitch_interactions(identifiers, species=9606, score=400, limit=20):
"""
STITCH 化合物-タンパク質相互作用取得。
Parameters:
identifiers: list — CID (化合物) または遺伝子名リスト
species: int — NCBI Taxonomy ID
score: int — 信頼スコア閾値
limit: int — 最大結果数
ToolUniverse:
STITCH_get_chemical_protein_interactions(
identifiers=identifiers, species=species,
required_score=score, limit=limit
)
STITCH_get_interaction_partners(identifiers=identifiers)
STITCH_resolve_identifier(identifiers=identifiers)
"""
url = f"https://stitch.embl.de/api/{OUTPUT_FORMAT}/interactionsList"
params = {
"identifiers": "\r".join(identifiers),
"species": species,
"required_score": score,
"limit": limit,
}
resp = requests.post(url, data=params)
resp.raise_for_status()
interactions = resp.json()
rows = []
for i in interactions:
rows.append({
"interactor_a": i.get("preferredName_A", i.get("stringId_A")),
"interactor_b": i.get("preferredName_B", i.get("stringId_B")),
"combined_score": i.get("score"),
"is_chemical": "CID" in str(i.get("stringId_A", ""))
or "CID" in str(i.get("stringId_B", "")),
})
df = pd.DataFrame(rows)
print(f"STITCH: chemical-protein interactions")
df
4. ネットワーク構築 & トポロジー解析
def build_network(interaction_df, source_col="protein_a", target_col="protein_b",
weight_col="combined_score"):
"""
NetworkX グラフ構築 & トポロジー解析。
Parameters:
interaction_df: DataFrame — 相互作用データ
source_col, target_col: str — ノードカラム名
weight_col: str — エッジ重みカラム名
"""
G = nx.Graph()
for _, row in interaction_df.iterrows():
G.add_edge(
row[source_col], row[target_col],
weight=row.get(weight_col, 1.0),
)
degree = dict(G.degree())
betweenness = nx.betweenness_centrality(G)
closeness = nx.closeness_centrality(G)
clustering = nx.clustering(G)
metrics = pd.DataFrame({
"node": list(degree.keys()),
"degree": list(degree.values()),
"betweenness": [betweenness[n] for n in degree],
"closeness": [closeness[n] for n in degree],
"clustering": [clustering[n] for n in degree],
}).sort_values("betweenness", ascending=False)
print(f"Network: {G.number_of_nodes()} nodes, "
f"{G.number_of_edges()} edges, "
f"density={nx.density(G):.4f}")
return G, metrics
5. コミュニティ検出
from networkx.algorithms.community import greedy_modularity_communities
def detect_communities(G, resolution=1.0):
"""
ネットワーク上のコミュニティ (モジュール) 検出。
Parameters:
G: nx.Graph — ネットワークグラフ
resolution: float — 解像度パラメータ
"""
communities = list(greedy_modularity_communities(G, resolution=resolution))
modularity = nx.algorithms.community.modularity(G, communities)
comm_data = []
for i, comm in enumerate(communities):
for node in comm:
comm_data.append({"node": node, "community": i})
df = pd.DataFrame(comm_data)
print(f"Communities: {len(communities)} detected, "
f"modularity={modularity:.4f}")
return df, modularity
6. STRING 機能濃縮解析
def string_enrichment(proteins, species=9606):
"""
STRING API 機能濃縮解析 (GO/KEGG/Reactome/InterPro)。
Parameters:
proteins: list — タンパク質名リスト
species: int — NCBI Taxonomy ID
"""
url = f"{STRING_API}/{OUTPUT_FORMAT}/enrichment"
params = {
"identifiers": "\r".join(proteins),
"species": species,
}
resp = requests.post(url, data=params)
resp.raise_for_status()
enrichment = resp.json()
rows = []
for e in enrichment:
rows.append({
"category": e.get("category"),
"term": e.get("term"),
"description": e.get("description"),
"p_value": e.get("p_value"),
"fdr": e.get("fdr"),
"number_of_genes": e.get("number_of_genes"),
"input_genes": e.get("inputGenes", ""),
})
df = pd.DataFrame(rows)
if not df.empty:
df = df.sort_values("fdr")
print(f"Enrichment: {len(df)} terms, "
f"{df[df['fdr'] < 0.05].shape[0]} significant (FDR<0.05)")
return df
7. 統合 PPI 解析パイプライン
def integrated_ppi_pipeline(genes, species=9606, score=700):
"""
STRING + BioGRID + STITCH 統合 PPI パイプライン。
Pipeline:
STRING network → BioGRID validation → topology → communities →
enrichment
"""
string_df = get_string_network(genes, species, score)
G, metrics = build_network(string_df)
comm_df, modularity = detect_communities(G)
all_nodes = list(G.nodes())
enrichment = string_enrichment(all_nodes[:500], species)
result = {
"n_nodes": G.number_of_nodes(),
"n_edges": G.number_of_edges(),
"density": round(nx.density(G), 4),
"n_communities": comm_df["community"].nunique(),
"modularity": round(modularity, 4),
"hub_genes": metrics.head(10)["node"].tolist(),
"n_enriched_terms": len(enrichment[enrichment["fdr"] < 0.05])
if not enrichment.empty else 0,
}
print(f"\n=== Integrated PPI Pipeline ===")
print(f"Nodes: {result['n_nodes']}, Edges: {result['n_edges']}")
print(f"Hub genes: {.join(result[][:])}")
result
パイプライン統合
drug-target-profiling → string-network-api → pathway-enrichment
(候補ターゲット) (STRING PPI 構築) (GO/KEGG 濃縮)
│ │ ↓
protein-interaction ───┘ │ ontology-enrichment
(IntAct/HumanBase) ↓ (EFO/Enrichr)
network-analysis
(既存スキル補完)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|
results/string_network.csv | STRING PPI ネットワーク | → network-analysis |
results/ppi_topology.csv | トポロジー指標 | → drug-target-profiling |
results/ppi_communities.csv | コミュニティ割当 | → pathway-enrichment |
results/string_enrichment.csv | 機能濃縮結果 | → ontology-enrichment |
利用可能ツール (ToolUniverse SMCP)
| ツール名 | 用途 |
|---|
STRING_get_protein_interactions | STRING PPI 取得 |
BioGRID_get_interactions | BioGRID 実験的 PPI |
STITCH_get_chemical_protein_interactions | STITCH 化合物-タンパク質 |
STITCH_get_interaction_partners | STITCH 相互作用パートナー |
STITCH_resolve_identifier | STITCH ID 解決 |
