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N6-methyladenosine (m6A), the most common posttranscriptional modification in eukaryotic mRNAs, plays an important role in mRNA splicing, editing, stability, degradation, etc. Since the methylation state is dynamic, methylation sequencing needs to be carried out over different time periods, which brings some difficulties to identify the RNA methyladenine sites. Thus, it is necessary to develop a fast and accurate method to identify the RNA N6-methyladenosine sites in the transcriptome. In this study, we use first-order and second-order Markov models to identify RNA N6-methyladenine sites in three species (Saccharomyces cerevisiae, mouse, and Homo sapiens). These two methods can fully consider the correlation between adjacent nucleotides. The results show that the performance of our method is better than that of other existing methods. Furthermore, the codons encoded by three nucleotides have biases in mRNA, and a second-order Markov model can capture this kind of information exactly. This may be the main reason why the performance of the second-order Markov model is better than that of the first-order Markov model in the m6A prediction problem. In addition, we provide a corresponding web tool called MM-m6APred.

N6-methyladenosine (m6A), the most common posttranscriptional modification in eukaryotic mRNAs, plays an important role in mRNA splicing, editing, stability, degradation, etc. Since the methylation state is dynamic, methylation sequencing needs to be carried out over different time periods, which brings some difficulties to identify the RNA methyladenine sites. Thus, it is necessary to develop a fast and accurate method to identify the RNA N6-methyladenosine sites in the transcriptome. In this study, we use first-order and second-order Markov models to identify RNA N6-methyladenine sites in three species (Saccharomyces cerevisiae , mouse, and Homo sapiens). These two methods can fully consider the correlation between adjacent nucleotides. The results show that the performance of our method is better than that of other existing methods. Furthermore, the codons encoded by three nucleotides have biases in mRNA, and a second-order Markov model can capture this kind of information exactly. This may be the main reason why the performance of the second-order Markov model is better than that of the first-order Markov model in the m6A prediction problem. In addition, we provide a corresponding web tool called MM-m6APred. [ABSTRACT FROM AUTHOR]