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/
....

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

10.1038/srep40242