Cai Fu-Hong, Qian Feng-Cui, Li Bing-Long, Li Li-Dong, Liao Bi-Hong, Yu Zheng-Min, Fang Qiao-Li, Li Yan-Yu, Dong Fu-Juan, Zhou Li-Wei, Li Chao, Wang Qiu-Yu, Liu Jiang-Hua
The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Department of Endocrinology and Metabolism, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
Comput Struct Biotechnol J. 2025 May 9;27:2147-2154. doi: 10.1016/j.csbj.2025.05.008. eCollection 2025.
Diabetes is a complex disease that involves multiple molecular mechanisms. Recent advances in multi-omics sequencing techniques have significantly enhanced the understanding of the pathogenesis of diabetes. To address the critical need for molecular resources in diabetes research, we present DiabetesOmic (https://bio.liclab.net/diabetesOmicdb/), a comprehensive multi-omics database designed to collect and analyze transcriptional regulatory information across five high-throughput sequencing modalities, including ChIP-seq, RNA-seq, ATAC-seq, scATAC-seq, and scRNA-seq. Currently, DiabetesOmic contains 487 samples, encompassing type 1 and type 2 diabetes spanning multiple tissues. These data underwent stringent quality assessment to ensure high-quality molecular profiles. Notably, we manually curated clinical complication annotations including diabetic nephropathy, retinopathy, and atherosclerosis to enhance translational relevance. For each type of sequencing data, we implemented specific analytical pipelines to generate multi-dimensional transcriptional regulatory information, including regulatory network identification, differential gene expression analysis, chromatin accessibility analysis, and transcription factor enrichment analysis. This comprehensive analysis enables the identification of disease-associated regulatory elements, epigenetic modifications, and cell type-specific molecular signatures, providing valuable insights into the molecular mechanisms of diabetes and its complications. This resource represents a significant advancement in diabetes research, facilitating deeper investigations into the disease's pathology and progression.
糖尿病是一种涉及多种分子机制的复杂疾病。多组学测序技术的最新进展显著增进了对糖尿病发病机制的理解。为满足糖尿病研究中对分子资源的迫切需求，我们推出了DiabetesOmic（https://bio.liclab.net/diabetesOmicdb/），这是一个综合性多组学数据库，旨在收集和分析来自五种高通量测序模式的转录调控信息，包括ChIP-seq、RNA-seq、ATAC-seq、scATAC-seq和scRNA-seq。目前，DiabetesOmic包含487个样本，涵盖多种组织的1型和2型糖尿病。这些数据经过了严格的质量评估，以确保高质量的分子图谱。值得注意的是，我们手动整理了包括糖尿病肾病、视网膜病变和动脉粥样硬化在内的临床并发症注释，以增强转化相关性。对于每种测序数据类型，我们实施了特定的分析流程，以生成多维转录调控信息，包括调控网络识别、差异基因表达分析、染色质可及性分析和转录因子富集分析。这种全面的分析能够识别与疾病相关的调控元件、表观遗传修饰和细胞类型特异性分子特征，为糖尿病及其并发症的分子机制提供有价值的见解。这一资源代表了糖尿病研究的重大进展，有助于更深入地研究该疾病的病理和进展。
