RAMPred: identifying the N-1-methyladenosine sites in eukaryotic transcriptomes

被引:55
作者
Chen, Wei [1 ,2 ]
Feng, Pengmian [3 ]
Tang, Hua [4 ]
Ding, Hui [5 ,6 ]
Lin, Hao [5 ,6 ]
机构
[1] North China Univ Sci & Technol, Dept Phys, Sch Sci, Tangshan 063000, Tangshan, Peoples R China
[2] North China Univ Sci & Technol, Ctr Genom & Computat Biol, Tangshan 063000, Tangshan, Peoples R China
[3] North China Univ Sci & Technol, Sch Publ Hlth, Tangshan 063000, Peoples R China
[4] Southwest Med Univ, Dept Pathophysiol, Luzhou 646000, Peoples R China
[5] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Ctr Bioinformat, Key Lab Neuroinformat,Minist Educ, Chengdu 610054, Peoples R China
[6] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Ctr Informat Biomed, Chengdu 610054, Peoples R China
来源
SCIENTIFIC REPORTS | 2016年 / 6卷
基金
中国博士后科学基金;
关键词
K-TUPLE; PREDICTION; IDENTIFICATION; N-6-METHYLADENOSINE; METHYLATION; PSEKNC;
D O I
10.1038/srep31080
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
N-1-methyladenosine (m(1)A) is a prominent RNA modification involved in many biological processes. Accurate identification of m(1)A site is invaluable for better understanding the biological functions of m(1)A. However, limitations in experimental methods preclude the progress towards the identification of m(1)A site. As an excellent complement of experimental methods, a support vector machine based-method called RAMPred is proposed to identify m(1)A sites in H. sapiens, M. musculus and S. cerevisiae genomes for the first time. In this method, RNA sequences are encoded by using nucleotide chemical property and nucleotide compositions. RAMPred achieves promising performances in jackknife tests, cross cell line tests and cross species tests, indicating that RAMPred holds very high potential to become a useful tool for identifying m(1)A sites. For the convenience of experimental scientists, a webserver based on the proposed model was constructed and could be freely accessible at http://lin.uestc.edu.cn/server/RAMPred.
引用
收藏
页数:8
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