A review of methods for predicting DNA N6-methyladenine sites. Issue 1 (10th December 2022)

• Record Type: Journal Article
• Authors: Han, Ke; Wang, Jianchun; Wang, Yu; Zhang, Lei; Yu, Mengyao; Xie, Fang; Zheng, Dequan; Xu, Yaoqun; Ding, Yijie; Wan, Jie
• Is Part Of: Briefings in bioinformatics. Volume 24:Issue 1(2023)
• Publication Date: 2022-12-10
• Subjects: DNA N6-methyladenine -- computational methods -- prediction -- evaluation metrics; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac514
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.