Scientific Ontology Enrichment
EFO / OLS / Enrichr / UMLS を統合した オントロジー検索・エンリッチメント解析パイプラインを提供する。
When to Use
- •EFO で実験条件 (疾患・細胞型・組織) のオントロジー ID を取得するとき
- •OLS で複数オントロジー横断検索 (HP, MONDO, DOID, GO, CHEBI) するとき
- •Enrichr で遺伝子リストの濃縮解析を行うとき
- •UMLS CUI で異なる用語体系間のマッピングを行うとき
- •GWAS Catalog の trait を EFO 用語で標準化するとき
Quick Start
1. EFO 実験ファクターオントロジー
python
import requests
import pandas as pd
OLS_API = "https://www.ebi.ac.uk/ols4/api"
def search_efo(query, exact=False):
"""
EFO (Experimental Factor Ontology) 検索。
Parameters:
query: str — 検索語 (疾患名、細胞型、組織名等)
exact: bool — 完全一致検索
ToolUniverse:
EFO_search(query=query, exact=exact)
"""
params = {
"q": query,
"ontology": "efo",
"exact": str(exact).lower(),
"rows": 30,
}
resp = requests.get(f"{OLS_API}/search", params=params)
resp.raise_for_status()
data = resp.json()
results = []
for doc in data.get("response", {}).get("docs", []):
results.append({
"efo_id": doc.get("obo_id", ""),
"label": doc.get("label", ""),
"description": (doc.get("description") or [""])[0][:200],
"iri": doc.get("iri", ""),
"ontology": doc.get("ontology_name", ""),
"is_defining_ontology": doc.get("is_defining_ontology", False),
"synonyms": doc.get("synonym", []),
})
df = pd.DataFrame(results)
print(f"EFO search '{query}': {len(df)} terms")
return df
2. OLS マルチオントロジー検索
python
def search_ols(query, ontologies=None, type_filter=None):
"""
OLS (Ontology Lookup Service) マルチオントロジー横断検索。
Parameters:
query: str — 検索語
ontologies: list — オントロジー ID リスト (e.g., ["hp", "mondo", "go"])
type_filter: str — "class", "property", "individual"
ToolUniverse:
OLS_search(query=query, ontology=ontology)
OLS_get_term(ontology=ontology, iri=iri)
OLS_get_ancestors(ontology=ontology, iri=iri)
"""
params = {"q": query, "rows": 50}
if ontologies:
params["ontology"] = ",".join(ontologies)
if type_filter:
params["type"] = type_filter
resp = requests.get(f"{OLS_API}/search", params=params)
resp.raise_for_status()
data = resp.json()
results = []
for doc in data.get("response", {}).get("docs", []):
results.append({
"obo_id": doc.get("obo_id", ""),
"label": doc.get("label", ""),
"ontology": doc.get("ontology_name", ""),
"description": (doc.get("description") or [""])[0][:200],
"iri": doc.get("iri", ""),
"synonyms": doc.get("synonym", []),
"has_children": doc.get("has_children", False),
})
df = pd.DataFrame(results)
print(f"OLS search '{query}' "
f"[{','.join(ontologies) if ontologies else 'all'}]: "
f"{len(df)} terms")
return df
def get_ols_term_hierarchy(ontology, term_id):
"""
OLS 用語の階層構造 (ancestors/descendants) 取得。
Parameters:
ontology: str — オントロジー ID (e.g., "hp", "go")
term_id: str — OBO ID (e.g., "HP:0001250")
"""
iri = f"http://purl.obolibrary.org/obo/{term_id.replace(':', '_')}"
encoded_iri = requests.utils.quote(requests.utils.quote(iri, safe=""), safe="")
# Ancestors
anc_resp = requests.get(
f"{OLS_API}/ontologies/{ontology}/terms/{encoded_iri}/ancestors"
)
# Descendants
desc_resp = requests.get(
f"{OLS_API}/ontologies/{ontology}/terms/{encoded_iri}/descendants"
)
hierarchy = {"ancestors": [], "descendants": []}
if anc_resp.status_code == 200:
for t in anc_resp.json().get("_embedded", {}).get("terms", []):
hierarchy["ancestors"].append({
"id": t.get("obo_id", ""),
"label": t.get("label", ""),
})
if desc_resp.status_code == 200:
for t in desc_resp.json().get("_embedded", {}).get("terms", []):
hierarchy["descendants"].append({
"id": t.get("obo_id", ""),
"label": t.get("label", ""),
})
print(f"OLS hierarchy {term_id}: "
f"{len(hierarchy['ancestors'])} ancestors, "
f"{len(hierarchy['descendants'])} descendants")
return hierarchy
3. Enrichr 遺伝子セット濃縮解析
python
ENRICHR_API = "https://maayanlab.cloud/Enrichr"
def run_enrichr(gene_list, description="", gene_set_libraries=None):
"""
Enrichr 遺伝子リスト濃縮解析。
Parameters:
gene_list: list — 遺伝子シンボルリスト (e.g., ["TP53", "BRCA1", "EGFR"])
description: str — 解析の説明
gene_set_libraries: list — 使用する遺伝子セットライブラリ
ToolUniverse:
Enrichr_submit_gene_list(genes=gene_list)
Enrichr_get_enrichment(user_list_id=id, library=library)
"""
if gene_set_libraries is None:
gene_set_libraries = [
"GO_Biological_Process_2023",
"GO_Molecular_Function_2023",
"KEGG_2021_Human",
"Reactome_2022",
"WikiPathway_2023_Human",
"DisGeNET",
]
# Submit gene list
genes_str = "\n".join(gene_list)
submit_resp = requests.post(
f"{ENRICHR_API}/addList",
files={"list": (None, genes_str), "description": (None, description)},
)
submit_resp.raise_for_status()
user_list_id = submit_resp.json().get("userListId")
print(f"Enrichr: submitted {len(gene_list)} genes (ID={user_list_id})")
# Get enrichment results per library
all_results = {}
for library in gene_set_libraries:
enrich_resp = requests.get(
f"{ENRICHR_API}/enrich",
params={"userListId": user_list_id, "backgroundType": library},
)
enrich_resp.raise_for_status()
data = enrich_resp.json()
results = []
for term_data in data.get(library, []):
results.append({
"rank": term_data[0],
"term": term_data[1],
"p_value": term_data[2],
"z_score": term_data[3],
"combined_score": term_data[4],
"overlap_genes": term_data[5],
"adjusted_p": term_data[6],
})
df = pd.DataFrame(results)
if not df.empty:
df = df.sort_values("adjusted_p")
all_results[library] = df
sig_count = (df["adjusted_p"] < 0.05).sum() if not df.empty else 0
print(f" {library}: {sig_count} significant terms (FDR < 0.05)")
return all_results
4. UMLS メタシソーラスマッピング
python
UMLS_API = "https://uts-ws.nlm.nih.gov/rest"
def search_umls(query, api_key, search_type="words"):
"""
UMLS メタシソーラス検索。
Parameters:
query: str — 検索語 (疾患名、症状、薬剤名)
api_key: str — UMLS API キー
search_type: str — "words", "exact", "leftTruncation"
ToolUniverse:
UMLS_search(query=query, search_type=search_type)
UMLS_get_concept(cui=cui)
"""
params = {
"string": query,
"searchType": search_type,
"apiKey": api_key,
"pageSize": 25,
}
resp = requests.get(f"{UMLS_API}/search/current", params=params)
resp.raise_for_status()
data = resp.json()
results = []
for item in data.get("result", {}).get("results", []):
results.append({
"cui": item.get("ui", ""),
"name": item.get("name", ""),
"root_source": item.get("rootSource", ""),
"uri": item.get("uri", ""),
})
df = pd.DataFrame(results)
print(f"UMLS search '{query}': {len(df)} concepts")
return df
def get_umls_crosswalk(cui, api_key, target_source=None):
"""
UMLS CUI からの用語体系間マッピング。
Parameters:
cui: str — UMLS CUI (e.g., "C0023264")
api_key: str — UMLS API キー
target_source: str — ターゲット用語体系 (e.g., "SNOMEDCT_US", "ICD10CM", "MeSH")
"""
params = {"apiKey": api_key, "pageSize": 100}
if target_source:
params["sabs"] = target_source
resp = requests.get(f"{UMLS_API}/content/current/CUI/{cui}/atoms", params=params)
resp.raise_for_status()
data = resp.json()
mappings = []
for atom in data.get("result", []):
mappings.append({
"source": atom.get("rootSource", ""),
"code": atom.get("sourceConcept", ""),
"name": atom.get("name", ""),
"term_type": atom.get("termType", ""),
})
df = pd.DataFrame(mappings)
if target_source:
df = df[df["source"] == target_source]
print(f"UMLS crosswalk {cui}: {len(df)} mappings "
f"({target_source or 'all sources'})")
return df
利用可能ツール
| ToolUniverse カテゴリ | 主なツール |
|---|---|
efo | EFO_search |
ols | OLS_search, OLS_get_term, OLS_get_ancestors |
enrichr | Enrichr_submit_gene_list, Enrichr_get_enrichment |
umls | UMLS_search, UMLS_get_concept |
パイプライン出力
| 出力ファイル | 説明 | 連携先スキル |
|---|---|---|
results/efo_terms.csv | EFO 標準化用語 | → disease-research, gene-expression |
results/enrichr_results/ | 遺伝子セット濃縮結果 | → pathway-enrichment, multi-omics |
results/umls_mapping.json | UMLS 用語マッピング | → clinical-decision-support, public-health-data |
results/ontology_hierarchy.json | オントロジー階層 | → text-mining-nlp, knowledge-graph |
パイプライン統合
code
disease-research ──→ ontology-enrichment ──→ pathway-enrichment
(GWAS/DisGeNET) (EFO/OLS/UMLS/Enrichr) (KEGG/Reactome/GO)
│
├──→ biothings-idmapping (CUI→Gene→Protein)
├──→ public-health-data (UMLS→RxNorm)
└──→ clinical-reporting (SNOMED/ICD マッピング)