Work

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

Year: 2017

Type: article

Abstract: As one of the most abundant RNA post-transcriptional modifications, N6-methyladenosine (m6A) 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 m6A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m6A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m6A 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 m6A 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/ .

Source: Scientific Reports

DOI: 10.1038/srep40242

Authors: Wei Chen, Pengwei Xing, Quan Zou

Institutions: North China University of Science and Technology, Tianjin University, Nankai University, State Key Laboratory of Medicinal Chemical Biology

Language: English

Topic: RNA modifications and cancer

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funders: National Natural Science Foundation of China, State Key Laboratory of Medicinal Chemical Biology