Rujiao Li: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Fang Liang: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Mengwei Li: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Dong Zou: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Shixiang Sun: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Yongbing Zhao: Lymphocyte Nuclear Biology, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
Wenming Zhao: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Yiming Bao: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Jingfa Xiao: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Zhang Zhang: BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
MethBank (http://bigd.big.ac.cn/methbank) is a database that integrates high-quality DNA methylomes across a variety of species and provides an interactive browser for visualization of methylation data. Here, we present an updated implementation of MethBank (version 3.0) by incorporating more DNA methylomes from multiple species and equipping with more enhanced functionalities for data annotation and more friendly web interfaces for data presentation, search and visualization. MethBank 3.0 features large-scale integration of high-quality methylomes, involving 34 consensus reference methylomes derived from a large number of human samples, 336 single-base resolution methylomes from different developmental stages and/or tissues of five plants, and 18 single-base resolution methylomes from gametes and early embryos at multiple stages of two animals. Additionally, it is enhanced by improving the functionalities for data annotation, which accordingly enables systematic identification of methylation sites closely associated with age, sites with constant methylation levels across different ages, differentially methylated promoters, age-specific differentially methylated cytosines/regions, and methylated CpG islands. Moreover, MethBank provides tools to estimate human methylation age online and to identify differentially methylated promoters, respectively. Taken together, MethBank is upgraded with significant improvements and advances over the previous version, which is of great help for deciphering DNA methylation regulatory mechanisms for epigenetic studies.
