“…The following are the emerged databases of methylation modifications associated with some specific diseases and fields: (1) After m6A methylation-related genes based on The Cancer Genome Atlas (TCGA) were used to predict the prognosis of hepatocellular carcinoma ( Group et al, 2020 ; Liu et al, 2020b ; Li et al, 2021b ), cancer-related methylation modification databases have begun to emerge, including, Lnc2Cancer 3.0 and OncoDB ( Gao et al, 2021 ; Tang et al, 2022 ); (2) Osteoarthritis-omics and molecular biomarkers (OAOB), which are a group of database containing differential molecular biomarkers related to osteoarthritis ( Li et al, 2022a ); (3) Other than disease, Pan et al (2022) established an integrative multi-omic database (iMOMdb) of Asian pregnant women providing the first blood-based multi-omic analysis of pregnant women in Asia. This database contains high-resolution genotypes, DNA methylation, and transcriptome profiles, and fills the knowledge gap of complex traits in populations of Asian ancestry; (4) Gao et al (2022) developed AgingBank, an experimentally supported multiomics database of information related to aging in multiple species; (5) ProMetheusDB, a database generated by analyzing and sorting cell culture experiments data using ML tools from the protein perspective ( Massignani et al, 2022 ); (6) compendium of protein lysine modifications (CPLM 4.0), a post-translational modification (PTMs) database ( Zhang et al, 2022 ); (7) tRNA-related databases containing high-throughput tRNA sequencing data ( Sajek et al, 2020 ); (8) RNAWRE and RM2 Target are two databases focusing on information of writers, readers, and erasers ( Nie et al, 2020 ; Bao et al, 2022 ); and (9) SyStemCell, a multiple-levels experimental database for stem cell research ( Yu et al, 2012 ). With the emergence of these specialized and multi-angle RNA methylation related databases, the traditional databases are also constantly updated and developed.…”