The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m
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A sites, in particular full-transcriptome m
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A. And individual m
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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
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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
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A methylation, including the experimental detection of m
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A methylation sites, techniques of data analysis, the way of predicting m
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A methylation sites, m
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A methylation databases, and detection of m
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A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.