DiseaseCIO 多组学数据库发布，整合 94 种疾病的干预结局数据
DiseaseCIO: a multi-omics atlas for characterizing clinical intervention outcomes across diverse diseases
DiseaseCIO 是一个将微生物组、转录组和表观组数据与人类疾病干预结局整合的多组学数据库，用于在患者个体层面刻画与干预结局相关的分子特征。该数据库收录来自 94 种疾病状态的 42 798 份治疗前样本，覆盖 510 种治疗方案，涉及 377 种药物与非药物干预；样本包含四种标本类型，采集自八个采样部位。研究团队用定制的 iMOD 流程处理多组学数据，生成 20 类分子特征，并识别出超过 184 万条候选特征与结局关联。数据库还提供差异分析、预后评估、跨组学相关分析、跨队列整合分析，以及针对用户提交特征集的富集分析和特征构建等交互工具，供探索干预结局异质性并筛选候选多组学标志物。
来源：Nucleic Acids Research · doi.org
该数据库把微生物组、转录组和表观组数据与干预结局配对，为跨疾病寻找治疗反应标志物提供统一入口。
Heterogeneity in clinical intervention outcomes across patients is a major barrier to precision medicine. The integration of multidimensional high-throughput omics can help reveal the molecular basis of differences in clinical interventions and provide insights into outcome prediction, prognostic stratification, regulatory patterns, and resistance mechanisms. We present DiseaseCIO (Disease Clinical Intervention Outcomes, http://db.edbc.org/DiseaseCIO/), a database that integrates microbiome, transcriptome, and epigenome data with human disease intervention outcomes. It is designed to characterize the molecular landscape associated with disease intervention outcomes at the individual patient level. DiseaseCIO encompasses 42 798 pretreatment samples from 94 disease states, covering 510 treatment regimens involving 377 pharmacological and non-pharmacological interventions. These samples comprise four types of specimens collected from eight sampling locations. Using the customized iMOD workflow for multi-omics data processing, we generated 20 categories of molecular features and identified >1.84 million candidate feature–outcome associations. DiseaseCIO also provides interactive tools for differential analysis, prognostic evaluation, cross-omics correlation analysis, and cross-cohort integrative analysis of multidimensional features as well as enrichment analysis and signature construction for user-submitted feature sets. DiseaseCIO provides a molecular-level resource for exploring intervention outcome heterogeneity across diseases and identifying candidate multi-omics biomarkers associated with treatment response and prognosis.