Aging Atlas - National Genomics Data Center Aging Atlas is developed and maintained by China National Center for Bioinformation and State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences
RNA-Seq-Aging Atlas The transcriptomics module catalogs genome-wide transcriptomic changes related to aging This contains a list of differentially expressed genes (DEGs), fold changes, and descriptions of underlying experimental conditions
Single Cell-Aging Atlas - National Genomics Data Center A single-cell transcriptomic atlas of human skin aging In this study, single-cell transcriptome sequencing (scRNA-seq) analysis was performed on the human eyelid skin of 9 healthy individuals of different ages 11 major skin cell types were identified and previously unclassified 3 quiescent epidermal stem cells (basal cells) and 3 amplifying
Aging Atlas: a multi-omics database for aging biology. - National . . . The Aging Atlas database aims to provide a wide range of life science researchers with valuable resources that allow access to a large-scale of gene expression and regulation datasets created by various high-throughput omics technologies
Pubication Aging-Atlas - National Genomics Data Center Here, we built comprehensive single-cell and single-nucleus transcriptomic atlases across various rat tissues undergoing aging and CR CR attenuated aging-related changes in cell type composition, gene expression, and core transcriptional regulatory networks
Metabolomics-Aging Atlas - National Genomics Data Center The metabolomics module catalogs metabolic changes related to aging This contains a list of metabolic molecules, fold changes, and descriptions of underlying experimental conditions
A Single-Cell Transcriptomic Atlas of Human Skin Aging To obtain the first aging atlas of human skin, we systematically analyzed 35,678 cells of skin sampled from young,middle and old human eyelid Systemic effects of aging on different skin cell types were evaluated in terms of cell-type composition, cell-specific molecular programs, transcription factors network