I.1 概要：バイオインフォマティクスにおけるデータベースの重要性

I.1.1 データベースエコシステムの理解

バイオインフォマティクス研究では、多様なデータベースが相互に連携し、包括的な生物学的情報基盤を形成しています。

図 I-1: バイオインフォマティクスにおけるデータベースエコシステム。1次/2次/専門/統合DBおよび解析プラットフォームの役割とデータフローを整理。

I.1.2 データベース選択の戦略的アプローチ

研究目的に応じた効果的なデータベース選択フレームワーク：

Step 1: 研究クエスチョンの分類

def classify_research_question(question_type, data_scope, analysis_depth):
    """
    研究クエスチョンに基づくデータベース推奨システム
    
    Args:
        question_type: "functional", "structural", "evolutionary", "clinical"
        data_scope: "single_gene", "pathway", "genome_wide", "multi_omics"
        analysis_depth: "descriptive", "comparative", "predictive", "causal"
    
    Returns:
        dict: 推奨データベースとアクセス戦略
    """
    
    recommendations = {
        "functional": {
            "single_gene": {
                "descriptive": ["UniProt", "GO", "InterPro"],
                "comparative": ["UniProt", "GO", "OMA"],
                "predictive": ["STRING", "GO", "KEGG"],
                "causal": ["GO", "KEGG", "Reactome"]
            },
            "pathway": {
                "descriptive": ["KEGG", "Reactome", "BioCyc"],
                "comparative": ["KEGG", "STRING", "GO"],
                "predictive": ["KEGG", "STRING", "MetaCyc"],
                "causal": ["Reactome", "KEGG", "SIGNOR"]
            }
        },
        "structural": {
            "single_gene": {
                "descriptive": ["PDB", "UniProt", "Pfam"],
                "comparative": ["PDB", "CATH", "SCOP"],
                "predictive": ["AlphaFold", "ModBase", "I-TASSER"],
                "causal": ["PDB", "CASTp", "ConCavity"]
            }
        },
        "clinical": {
            "single_gene": {
                "descriptive": ["ClinVar", "OMIM", "PharmGKB"],
                "comparative": ["ClinVar", "COSMIC", "ExAC"],
                "predictive": ["ClinVar", "PharmGKB", "DGIdb"],
                "causal": ["ClinVar", "OMIM", "DisGeNET"]
            },
            "genome_wide": {
                "descriptive": ["GWAS Catalog", "UK Biobank", "GTEx"],
                "comparative": ["GWAS Catalog", "PhenoScanner", "Open Targets"],
                "predictive": ["PRS Catalog", "GWAS Catalog", "UK Biobank"],
                "causal": ["Open Targets", "DisGeNET", "STRING"]
            }
        }
    }
    
    try:
        return {
            "primary_databases": recommendations[question_type][data_scope][analysis_depth],
            "access_strategy": generate_access_strategy(question_type, data_scope),
            "integration_approach": suggest_integration_methods(data_scope, analysis_depth)
        }
    except KeyError:
        return {"error": "Invalid combination of parameters"}

def generate_access_strategy(question_type, data_scope):
    """データアクセス戦略の生成"""
    if data_scope in ["genome_wide", "multi_omics"]:
        return {
            "method": "bulk_download",
            "tools": ["FTP", "API", "rsync"],
            "preprocessing": "required",
            "storage": "local_database_recommended"
        }
    else:
        return {
            "method": "query_based",
            "tools": ["REST_API", "web_interface"],
            "preprocessing": "minimal",
            "storage": "cache_sufficient"
        }

def suggest_integration_methods(data_scope, analysis_depth):
    """データ統合手法の提案"""
    integration_matrix = {
        ("single_gene", "descriptive"): ["manual_curation", "simple_joins"],
        ("single_gene", "comparative"): ["orthology_mapping", "sequence_alignment"],
        ("pathway", "predictive"): ["network_analysis", "enrichment_analysis"],
        ("genome_wide", "causal"): ["mendelian_randomization", "colocalization"],
        ("multi_omics", "predictive"): ["multi_modal_ML", "network_integration"]
    }
    
    return integration_matrix.get((data_scope, analysis_depth), ["custom_integration"])

# 使用例
recommendation = classify_research_question("clinical", "single_gene", "predictive")
print(f"推奨データベース: {recommendation['primary_databases']}")
print(f"アクセス戦略: {recommendation['access_strategy']['method']}")