| name | scientific-gdc-portal |
| description | NCI Genomic Data Commons ポータルスキル。GDC REST API
を用いたがんゲノムプロジェクト横断検索・ケースメタデータ・
体細胞変異 (SSM)・遺伝子発現・ファイル取得。
ToolUniverse 連携: gdc。
|
| tu_tools | [{"key":"gdc","name":"GDC","description":"NCI Genomic Data Commons REST API"}] |
Scientific GDC Portal
NCI Genomic Data Commons (GDC) REST API を活用した
がんゲノムプロジェクト横断検索・ケースメタデータ取得・
体細胞変異 (SSM)・遺伝子発現パイプラインを提供する。
When to Use
- TCGA/TARGET 等のがんゲノムデータを横断検索するとき
- がん種別のケースメタデータを取得するとき
- 特定遺伝子の体細胞変異 (SSM) 頻度を調べるとき
- がんプロジェクトの統計サマリーを取得するとき
- GDC ファイルメタデータを検索してダウンロード URL を取得するとき
Quick Start
1. プロジェクト検索・統計
import requests
import pandas as pd
GDC_API = "https://api.gdc.cancer.gov"
def gdc_projects(disease_type=None, limit=50):
"""
GDC — プロジェクト検索。
Parameters:
disease_type: str — 疾患タイプフィルタ
(例: "Breast Invasive Carcinoma")
limit: int — 最大結果数
"""
url = f"{GDC_API}/projects"
params = {
"size": limit,
"fields": ("project_id,name,primary_site,"
"disease_type,summary.case_count,"
"summary.file_count"),
}
if disease_type:
params["filters"] = (
'{"op":"=","content":{"field":'
f'"disease_type","value":"{disease_type}"}}}}'
)
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
rows = []
for hit in data.get("data", {}).get("hits", []):
summary = hit.get("summary", {})
rows.append({
"project_id": hit.get("project_id", ""),
"name": hit.get("name", ""),
"primary_site": "; ".join(
hit.get("primary_site", [])),
"disease_type": "; ".join(
hit.get("disease_type", [])),
"case_count": summary.get(
"case_count", 0),
"file_count": summary.get(
"file_count", 0),
})
df = pd.DataFrame(rows)
print(f"GDC projects: {len(df)}")
return df
2. ケースメタデータ
def gdc_cases(project_id, limit=100):
"""
GDC — ケースメタデータ取得。
Parameters:
project_id: str — プロジェクト ID
(例: "TCGA-BRCA")
limit: int — 最大結果数
"""
url = f"{GDC_API}/cases"
filters = {
"op": "=",
"content": {
"field": "project.project_id",
"value": project_id,
},
}
params = {
"filters": str(filters).replace("'", '"'),
"fields": ("case_id,submitter_id,"
"demographic.gender,"
"demographic.race,"
"demographic.vital_status,"
"diagnoses.primary_diagnosis,"
"diagnoses.tumor_stage,"
"diagnoses.age_at_diagnosis"),
"size": limit,
}
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
rows = []
for hit in data.get("data", {}).get("hits", []):
demo = hit.get("demographic", {}) or {}
diag = (hit.get("diagnoses", [{}]) or [{}])[0]
rows.append({
"case_id": hit.get("case_id", ""),
"submitter_id": hit.get("submitter_id", ""),
: demo.get(, ),
: demo.get(, ),
: demo.get(
, ),
: diag.get(
, ),
: diag.get(, ),
: diag.get(
, ),
})
df = pd.DataFrame(rows)
()
df
3. 体細胞変異 (SSM) 検索
def gdc_ssm_by_gene(gene_symbol, project_id=None,
limit=100):
"""
GDC — 遺伝子別体細胞変異検索。
Parameters:
gene_symbol: str — 遺伝子シンボル (例: "TP53")
project_id: str — プロジェクト ID フィルタ
limit: int — 最大結果数
"""
url = f"{GDC_API}/ssms"
filters = {
"op": "and",
"content": [
{
"op": "=",
"content": {
"field":
"consequence.transcript."
"gene.symbol",
"value": gene_symbol,
},
}
],
}
if project_id:
filters["content"].append({
"op": "=",
"content": {
"field": "cases.project.project_id",
"value": project_id,
},
})
params = {
"filters": str(filters).replace("'", '"'),
"fields": ("ssm_id,genomic_dna_change,"
"consequence.transcript.aa_change,"
"consequence.transcript."
"consequence_type,"
"consequence.transcript."
"gene.symbol"),
"size": limit,
}
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
rows = []
hit data.get(, {}).get(, []):
csq hit.get(, []):
tx = csq.get(, {})
rows.append({
: hit.get(, ),
: hit.get(
, ),
: tx.get(, {}).get(
, ),
: tx.get(, ),
: tx.get(
, ),
})
df = pd.DataFrame(rows)
()
df
4. GDC 統合パイプライン
def gdc_pipeline(project_id, gene_symbol=None,
output_dir="results"):
"""
GDC 統合パイプライン。
Parameters:
project_id: str — プロジェクト ID
gene_symbol: str — 遺伝子フィルタ
output_dir: str — 出力ディレクトリ
"""
from pathlib import Path
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
projects = gdc_projects()
projects.to_csv(output_dir / "gdc_projects.csv",
index=False)
cases = gdc_cases(project_id)
cases.to_csv(output_dir / "gdc_cases.csv",
index=False)
if gene_symbol:
ssm = gdc_ssm_by_gene(gene_symbol, project_id)
ssm.to_csv(output_dir / "gdc_ssm.csv",
index=False)
print(f"GDC pipeline: {project_id} → {output_dir}")
return {"cases": cases}
ToolUniverse 連携
| TU Key | ツール名 | 連携内容 |
|---|
gdc | GDC | NCI Genomic Data Commons REST API |
パイプライン統合
cancer-genomics → gdc-portal → precision-oncology
(COSMIC/DepMap) (GDC API) (MTB レポート)
│ │ ↓
icgc-cancer-data ───────┘ variant-interpretation
(ICGC DCC) (ClinVar/ACMG)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|
results/gdc_projects.csv | プロジェクト一覧 | → cancer-genomics |
results/gdc_cases.csv | ケースメタデータ | → precision-oncology |
results/gdc_ssm.csv | 体細胞変異 | → variant-interpretation |
