scientific-cell-line-resources

name: scientific-cell-line-resources description: | 細胞株リソーススキル。Cellosaurus 細胞株データベース検索、 STR プロファイルマッチング、コンタミネーション検出、 細胞株メタデータ (由来組織・疾患・種) 取得パイプライン。 tu_tools:

• key: cellosaurus name: Cellosaurus description: 細胞株データベース (ExPASy)

Scientific Cell Line Resources

Cellosaurus を中心とした細胞株リソースデータベースアクセス パイプラインを提供する。

When to Use

• 細胞株の正式名称・アクセッション番号を確認するとき
• 細胞株の由来 (組織・疾患・種) を調べるとき
• STR プロファイルで細胞株の同一性を検証するとき
• 細胞株のコンタミネーション (ミスアイデンティフィケーション) を確認するとき
• 実験に使用する細胞株の参考文献・データベースリンクを取得するとき

Quick Start

1. Cellosaurus 細胞株検索

import requests
import pandas as pd

CELLOSAURUS_API = "https://api.cellosaurus.org"


def search_cellosaurus(query, limit=25):
    """
    Cellosaurus 細胞株検索。

    Parameters:
        query: str — 細胞株名 (e.g., "HeLa", "MCF-7", "A549")
        limit: int — 最大取得数

    ToolUniverse:
        Cellosaurus_search(query=query)
        Cellosaurus_get_cell_line(accession=accession)
        Cellosaurus_get_str_profile(accession=accession)
    """
    params = {"q": query, "rows": limit, "format": "json"}
    resp = requests.get(f"{CELLOSAURUS_API}/search/cell-line", params=params)
    resp.raise_for_status()
    data = resp.json()

    results = []
    for cell_line in data.get("result", {}).get("cellLineList", []):
        cl = cell_line.get("cellLine", {})
        results.append({
            "accession": cl.get("accession", ""),
            "name": cl.get("name", ""),
            "synonyms": [s.get("value", "") for s in cl.get("synonymList", [])],
            "category": cl.get("category", ""),
            "sex": cl.get("sex", ""),
            "species": cl.get("species", {}).get("value", ""),
            "diseases": [
                d.get("terminology", {}).get("value", "")
                for d in cl.get("diseaseList", [])
            ],
            "derived_from_site": cl.get("derivedFromSite", {}).get("value", ""),
            "is_contaminated": cl.get("isContaminated", False),
            "is_problematic": cl.get("isProblematic", False),
        })

    df = pd.DataFrame(results)
    print(f"Cellosaurus search '{query}': {len(df)} cell lines")
    return df

2. 細胞株詳細情報取得

def get_cellosaurus_entry(accession):
    """
    Cellosaurus 細胞株詳細情報取得。

    Parameters:
        accession: str — Cellosaurus アクセッション (e.g., "CVCL_0030")
    """
    resp = requests.get(
        f"{CELLOSAURUS_API}/cell-line/{accession}",
        params={"format": "json"}
    )
    resp.raise_for_status()
    data = resp.json()

    cl = data.get("cellLine", {})
    entry = {
        "accession": cl.get("accession", ""),
        "name": cl.get("name", ""),
        "category": cl.get("category", ""),
        "sex": cl.get("sex", ""),
        "age": cl.get("age", ""),
        "species": cl.get("species", {}).get("value", ""),
        "diseases": [
            {
                "name": d.get("terminology", {}).get("value", ""),
                "accession": d.get("terminology", {}).get("accession", ""),
            }
            for d in cl.get("diseaseList", [])
        ],
        "derived_from_site": cl.get("derivedFromSite", {}).get("value", ""),
        "is_contaminated": cl.get("isContaminated", False),
        "contamination_comment": cl.get("contaminationComment", ""),
        "str_profile": cl.get("strList", []),
        "references": [
            {
                "pmid": r.get("pubmedId", ""),
                "title": r.get("title", ""),
            }
            for r in cl.get("referenceList", [])
        ],
        "cross_references": [
            {
                "database": xr.get("database", ""),
                "accession": xr.get("accession", ""),
            }
            for xr in cl.get("xrefList", [])
        ],
    }

    print(f"Cellosaurus {accession}: {entry['name']} "
          f"({entry['species']}, {entry['category']})")
    return entry

3. STR プロファイル検証

def check_str_profile(accession, str_data=None):
    """
    STR (Short Tandem Repeat) プロファイルによる細胞株同一性検証。

    Parameters:
        accession: str — Cellosaurus アクセッション
        str_data: dict — 測定した STR データ {marker: alleles}
    """
    entry = get_cellosaurus_entry(accession)
    ref_str = entry.get("str_profile", [])

    if not ref_str:
        print(f"WARNING: {accession} has no STR profile in Cellosaurus")
        return {"match": None, "message": "No reference STR profile available"}

    ref_markers = {}
    for marker in ref_str:
        name = marker.get("marker", "")
        alleles = marker.get("alleles", "")
        ref_markers[name] = alleles

    if str_data is None:
        print(f"Reference STR for {accession}: {len(ref_markers)} markers")
        return {"reference_str": ref_markers, "marker_count": len(ref_markers)}

    # Calculate match percentage
    matched = 0
    total = 0
    details = []
    for marker, ref_alleles in ref_markers.items():
        if marker in str_data:
            total += 1
            measured = str_data[marker]
            if set(str(ref_alleles).split(",")) == set(str(measured).split(",")):
                matched += 1
                details.append({"marker": marker, "match": True})
            else:
                details.append({
                    "marker": marker, "match": False,
                    "reference": ref_alleles, "measured": measured,
                })

    match_pct = (matched / total * 100) if total > 0 else 0
    result = {
        "match_percentage": match_pct,
        "matched": matched,
        "total_compared": total,
        "is_authenticated": match_pct >= 80,
        "details": details,
    }

    status = "PASS" if result["is_authenticated"] else "FAIL"
    print(f"STR verification {accession}: {match_pct:.1f}% match → {status}")
    return result

4. コンタミネーション・問題細胞株チェック

def check_contamination_status(cell_line_names):
    """
    細胞株リストのコンタミネーション/ミスアイデンティフィケーション確認。

    Parameters:
        cell_line_names: list — 細胞株名リスト
    """
    results = []
    for name in cell_line_names:
        df = search_cellosaurus(name, limit=1)
        if df.empty:
            results.append({
                "name": name, "found": False,
                "is_contaminated": None, "is_problematic": None,
            })
            continue

        row = df.iloc[0]
        results.append({
            "name": name,
            "found": True,
            "accession": row.get("accession", ""),
            "official_name": row.get("name", ""),
            "is_contaminated": row.get("is_contaminated", False),
            "is_problematic": row.get("is_problematic", False),
            "species": row.get("species", ""),
            "diseases": row.get("diseases", []),
        })

    df = pd.DataFrame(results)
    contaminated = df["is_contaminated"].sum() if "is_contaminated" in df else 0
    problematic = df["is_problematic"].sum() if "is_problematic" in df else 0
    print(f"Cell line check: {len(cell_line_names)} lines, "
          f"{contaminated} contaminated, {problematic} problematic")
    return df

利用可能ツール

パイプライン出力

パイプライン統合

cancer-genomics ──→ cell-line-resources ──→ lab-automation
  (COSMIC/DepMap)    (Cellosaurus STR)     (プロトコル管理)
                           │
                           ├──→ precision-oncology (腫瘍細胞株)
                           ├──→ disease-research (疾患モデル)
                           └──→ human-protein-atlas (発現データ)