Improving N(6)-methyladenosine site prediction with heuristic selection of nucleotide physical-chemical properties.
N(6)-methyladenosine (m(6)A) is one of the most common and abundant post-transcriptional RNA modifications found in viruses and most eukaryotes. m(6)A plays an essential role in many vital biological processes to regulate gene expression. Because of its widespread distribution across the genomes, the identification of m(6)A sites from RNA sequences is of significant importance for better understanding the regulatory mechanism of m(6)A. Although progress has been achieved in m(6)A site prediction, challenges remain. This article aims to further improve the performance of m(6)A site prediction by introducing a new heuristic nucleotide physical-chemical property selection (HPCS) algorithm. The proposed HPCS algorithm can effectively extract an optimized subset of nucleotide physical-chemical properties under the prescribed feature representation for encoding an RNA sequence into a feature vector. We demonstrate the efficacy of the proposed HPCS algorithm under different feature representations, including pseudo dinucleotide composition (PseDNC), auto-covariance (AC), and cross-covariance (CC). Based on the proposed HPCS algorithm, we implemented an m(6)A site predictor, called M6A-HPCS, which is freely available at http://csbio.njust.edu.cn/bioinf/M6A-HPCS. Experimental results over rigorous jackknife tests on benchmark datasets demonstrated that the proposed M6A-HPCS achieves higher success rates and outperforms existing state-of-the-art sequence-based m(6)A site predictors.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China. Electronic address: zhangming@just.edu.cn.
Ming
M
Zhang
School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Jia-Wei
JW
Sun
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Zi
Z
Liu
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Ming-Wu
MW
Ren
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China.
Hong-Bin
HB
Shen
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: njyudj@njust.edu.cn.
Dong-Jun
DJ
Yu
D000241:Adenosine / Q000031:analogs & derivatives* / Q000737:chemistry; D000465:Algorithms; D001665:Binding Sites; D000066506:Heuristics; D009711:Nucleotides / Q000737:chemistry*
D009711:Nucleotides; C010223:N-methyladenosine; D000241:Adenosine
Feature representation; N(6)-methyladenosine; Physical–chemical property selection; RNA sequence; Support vector machine