Detecting N(6)-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.
Detecting N(6)-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.
复制标题
使用集成支持向量机检测 RNA 转录组中的 N-6-甲基腺苷位点
DOI：
10.1038/srep40242
复制
发表时间：
2017-01-12
影响因子：
4.6
通讯作者：
Zou Q
中科院分区：
文献类型：
--
作者：
Chen W;Xing P;Zou Q
N6-甲基腺苷（m6 A）作为RNA转录后修饰中最丰富的一种，参与了从mRNA剪接、稳定性到细胞分化和重编程等广泛的生物学和生理学过程。然而，m6 A位点的实验鉴定是昂贵且费力的。因此，迫切需要发展可靠的预测m6 A位点的计算方法。在本研究中，开发了一种新的方法，称为RAM-ESVM检测m6 A位点的酿酒酵母转录组，该方法采用集成支持向量机分类器和新的序列特征。刀切测试结果表明，RAM-ESVM的分类性能优于单一支持向量机分类器和其他现有方法，表明该方法是一种有效的检测沙门氏菌m6 A位点的计算工具。啤酒。此外，还建立了一个名为RAM-ESVM的网络服务器，可在http://server.malab.cn/RAM-ESVM/上免费访问。
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/.