Detecting N(6)-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

As one of the most abundant RNA post-transcriptional modifications, N(6)-methyladenosine (m(6)A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m(6)A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m(6)A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m(6)A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m(6)A sites in S. cerevisiae. Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/.