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.
