iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

被引:263
作者
Feng, Pengmian [1 ]
Ding, Hui [2 ]
Yang, Hui [2 ]
Chen, Wei [3 ,4 ,5 ]
Lin, Hao [2 ,5 ]
Chou, Kuo-Chen [2 ,5 ]
机构
[1] North China Univ Sci & Technol, Sch Publ Hlth, Hebei Prov Key Lab Occupat Hlth & Safety Coal Ind, Tangshan 063000, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Informat Biol, Sch Life Sci & Technol, Key Lab Neuroinformat,Minist Educ, Chengdu 610054, Peoples R China
[3] North China Univ Sci & Technol, Sch Sci, Dept Phys, Tangshan 063000, Peoples R China
[4] North China Univ Sci & Technol, Ctr Genom & Computat Biol, Tangshan 063000, Peoples R China
[5] Gordon Life Sci Inst, Boston, MA 02478 USA
基金
中国博士后科学基金;
关键词
AMINO-ACID-COMPOSITION; SEQUENCE-BASED PREDICTOR; MEMBRANE-PROTEIN TYPES; LABEL LEARNING CLASSIFIER; SUPPORT VECTOR MACHINES; 3 DIFFERENT MODES; K-TUPLE; PHYSICOCHEMICAL PROPERTIES; SUBCELLULAR-LOCALIZATION; ENSEMBLE CLASSIFIER;
D O I
10.1016/j.omtn.2017.03.006
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called "iRNA-PseColl" has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform's web-server has been provided at http:// lin. uestc. edu. cn/ server/ iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.
引用
收藏
页码:155 / 163
页数:9
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