class COVID19VariantTracker:
    """COVID-19変異株追跡システム"""
    
    def __init__(self):
        self.reference_genome = self.load_reference()
        self.variant_database = {}
        
    def analyze_new_sequence(self, sequence_data):
        """新規配列の変異解析"""
        # アライメント
        alignment = self.align_to_reference(sequence_data)
        
        # 変異検出
        mutations = self.call_mutations(alignment)
        
        # 既知変異株との比較
        lineage = self.assign_lineage(mutations)
        
        # 新規変異の評価
        novel_mutations = self.identify_novel_mutations(mutations)
        
        return {
            'lineage': lineage,
            'mutations': mutations,
            'novel_mutations': novel_mutations
        }
    
    def track_mutation_dynamics(self, time_series_data):
        """変異の時間的ダイナミクス追跡"""
        mutation_frequencies = {}
        
        for timepoint in time_series_data:
            for mutation in timepoint['mutations']:
                if mutation not in mutation_frequencies:
                    mutation_frequencies[mutation] = []
                    
                freq = timepoint['frequency']
                mutation_frequencies[mutation].append(
                    (timepoint['date'], freq)
                )
                
        return mutation_frequencies

class TCGAAnalyzer:
    """TCGAデータの統合解析"""
    
    def __init__(self, cancer_type):
        self.cancer_type = cancer_type
        self.load_multiomics_data()
        
    def integrated_analysis(self):
        """マルチオミクス統合解析"""
        # 体細胞変異
        mutations = self.analyze_mutations()
        
        # コピー数変異
        cnv = self.analyze_copy_number()
        
        # 遺伝子発現
        expression = self.analyze_expression()
        
        # メチル化
        methylation = self.analyze_methylation()
        
        # 統合クラスタリング
        subtypes = self.consensus_clustering([
            mutations, cnv, expression, methylation
        ])
        
        return subtypes
    
    def survival_analysis(self, subtypes, clinical_data):
        """サブタイプ別の生存解析"""
        from lifelines import KaplanMeierFitter
        
        results = {}
        
        for subtype in subtypes.unique():
            mask = subtypes == subtype
            
            kmf = KaplanMeierFitter()
            kmf.fit(
                clinical_data[mask]['survival_time'],
                clinical_data[mask]['event']
            )
            
            results[subtype] = {
                'median_survival': kmf.median_survival_time_,
                'survival_function': kmf.survival_function_
            }
            
        return results

class JapaneseReferenceGenome:
    """日本人基準ゲノム構築"""
    
    def __init__(self):
        self.samples = []
        self.assembly = None
        
    def construct_pangenome(self, population_samples):
        """パンゲノムの構築"""
        # 各個体のde novoアセンブリ
        assemblies = []
        for sample in population_samples:
            assembly = self.perform_hifi_assembly(sample)
            assemblies.append(assembly)
            
        # グラフゲノムの構築
        graph_genome = self.build_graph_genome(assemblies)
        
        # 日本人特異的変異の同定
        jp_specific = self.identify_population_specific_variants(
            graph_genome
        )
        
        return graph_genome, jp_specific
    
    def clinical_application(self, patient_genome):
        """臨床応用：患者ゲノムの解析"""
        # 日本人基準ゲノムへのマッピング
        alignment = self.align_to_jp_reference(patient_genome)
        
        # 疾患関連変異の検出
        variants = self.call_variants(alignment)
        
        # 日本人集団での頻度情報を考慮した解釈
        interpreted = self.interpret_with_population_frequency(
            variants
        )
        
        return interpreted

class AIdrugDiscovery:
    """AI創薬プラットフォーム"""
    
    def __init__(self):
        self.molecular_generator = self.build_generator()
        self.activity_predictor = self.build_predictor()
        
    def design_novel_compounds(self, target_protein):
        """新規化合物の設計"""
        # 標的タンパク質の構造解析
        binding_site = self.analyze_binding_site(target_protein)
        
        # 分子生成
        candidates = []
        for _ in range(10000):
            molecule = self.molecular_generator.generate()
            
            # ドッキングシミュレーション
            docking_score = self.perform_docking(
                molecule, binding_site
            )
            
            # ADMET予測
            admet = self.predict_admet_properties(molecule)
            
            if self.passes_filters(docking_score, admet):
                candidates.append(molecule)
                
        return self.rank_candidates(candidates)