Computational advances in epigenetic regulation databases and prediction tools

Epigenetic regulation refers to heritable changes in gene expression without altering the DNA sequence. It mainly includes DNA methylation, histone modification, RNA modification and non-coding RNAs (ncRNAs). Abnormalities in epigenetic regulation play a key role in the development of many diseases. In recent years, with the rapid development of high-throughput sequencing technology, epigenetic modification datasets have accumulated rapidly, which has facilitated the development of various database resources and computational analysis tools. At the same time, the emergence of artificial intelligence technologies such as machine learning and deep learning has further improved the speed and accuracy of epigenetic modification site prediction and functional mining. However, most existing reviews focus on databases and computational tools for single types of epigenetic modifications, whereas systematic overviews of integrated databases, multi-algorithm prediction tools, and applications of artificial intelligence technologies in this field remain scarce. Here, we systematically introduce the current major databases and prediction tools for DNA methylation, histone modification, RNA modification, ncRNAs, and disease-related epigenetic modifications, highlighting the potential of artificial intelligence technologies, including deep learning, in epigenetic modification research and pharmaceutical research. In addition, we discuss the challenges faced by existing computational methods in the field and future directions, providing references for data repositories and computational tools selected by researchers, as well as new insights into epigenetic mechanism studies.