MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model

Author:

Pian Cong12ORCID,Zhang Guangle3,Li Fei2,Fan Xiaodan1ORCID

Affiliation:

1. Department of Statistics, The Chinese University of Hong Kong, Sha Tin, Hong Kong

2. State Key Laboratory of Rice Biology and Ministry of Agricultural and Rural Affairs, Key Laboratory of Molecular Biology of Crop Pathogens and Insect Pests, Institute of Insect Sciences, Zhejiang University, Hangzhou, China

3. Binjiang College, Nanjing University of Information Science and Technology, Jiangsu, China

Abstract

Abstract Motivation Recent studies have shown that DNA N6-methyladenine (6mA) plays an important role in epigenetic modification of eukaryotic organisms. It has been found that 6mA is closely related to embryonic development, stress response and so on. Developing a new algorithm to quickly and accurately identify 6mA sites in genomes is important for explore their biological functions. Results In this paper, we proposed a new classification method called MM-6mAPred based on a Markov model which makes use of the transition probability between adjacent nucleotides to identify 6mA site. The sensitivity and specificity of our method are 89.32% and 90.11%, respectively. The overall accuracy of our method is 89.72%, which is 6.59% higher than that of the previous method i6mA-Pred. It indicated that, compared with the 41 nucleotide chemical properties used by i6mA-Pred, the transition probability between adjacent nucleotides can capture more discriminant sequence information. Availability and implementation The web server of MM-6mAPred is freely accessible at http://www.insect-genome.com/MM-6mAPred/ Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Key Research Development Program

Hong Kong Scholars Program

Research Grants Council of the Hong Kong Special Administrative Region, China

General Research Fund

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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