表观遗传学
生物
蛋白质组学
基因组学
计算生物学
药物基因组学
组学
功能基因组学
基因组
数据库
生物信息学
计算机科学
基因
基因表达
遗传学
DNA甲基化
作者
Guang‐Hui Liu,Yīmíng Bào,Jing Qu,Weiqi Zhang,Tāo Zhāng,Wang Kang,Fei Yang,Qianzhao Ji,Xiaoyu Jiang,Yingke Ma,Shuai Ma,Zunpeng Liu,Siyu Chen,Si Wang,Shuhui Sun,Lingling Geng,Kaowen Yan,Pengze Yan,Yanling Fan,Moshi Song
摘要
Organismal aging is driven by interconnected molecular changes encompassing internal and extracellular factors. Combinational analysis of high-throughput 'multi-omics' datasets (gathering information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and pharmacogenomics), at either populational or single-cell levels, can provide a multi-dimensional, integrated profile of the heterogeneous aging process with unprecedented throughput and detail. These new strategies allow for the exploration of the molecular profile and regulatory status of gene expression during aging, and in turn, facilitate the development of new aging interventions. With a continually growing volume of valuable aging-related data, it is necessary to establish an open and integrated database to support a wide spectrum of aging research. 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. The current implementation includes five modules: transcriptomics (RNA-seq), single-cell transcriptomics (scRNA-seq), epigenomics (ChIP-seq), proteomics (protein-protein interaction), and pharmacogenomics (geroprotective compounds). Aging Atlas provides user-friendly functionalities to explore age-related changes in gene expression, as well as raw data download services. Aging Atlas is freely available at https://bigd.big.ac.cn/aging/index.
科研通智能强力驱动
Strongly Powered by AbleSci AI