Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Abstract

Abstract 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/ .