Analysis approaches for the identification and prediction of N(6)-methyladenosine sites

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m(6)A sites, in particular full-transcriptome m(6)A. And individual m(6)A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m(6)A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m(6)A methylation, including the experimental detection of m(6)A methylation sites, techniques of data analysis, the way of predicting m(6)A methylation sites, m(6)A methylation databases, and detection of m(6)A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.