Analysis approaches for the identification and prediction of N6-methyladenosine sites

Yuwei Yang; Zhiyu Liu; Junru Lu; Yuqing Sun; Yue Fu; Min Pan; Xueying Xie; Qinyu Ge

doi:10.1080/15592294.2022.2158284

Analysis approaches for the identification and prediction of N⁶-methyladenosine sites

Epigenetics. 2023 Dec;18(1):2158284. doi: 10.1080/15592294.2022.2158284. Epub 2022 Dec 23.

Authors

Yuwei Yang¹, Zhiyu Liu¹, Junru Lu¹, Yuqing Sun¹, Yue Fu¹, Min Pan², Xueying Xie¹, Qinyu Ge¹

Affiliations

¹ State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, People's Republic of China.
² Department of Pathology and Pathophysiology School of Medicine, Southeast University, Nanjing, China.

Abstract

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m⁶A sites, in particular full-transcriptome m⁶A. And individual m⁶A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m⁶A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m⁶A methylation, including the experimental detection of m⁶A methylation sites, techniques of data analysis, the way of predicting m⁶A methylation sites, m⁶A methylation databases, and detection of m⁶A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

Keywords: analytical tools; detection methods; m6A methylation; prediction.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Adenosine* / metabolism
Computational Biology / methods
DNA Methylation*
Machine Learning

Substances

N-methyladenosine
Adenosine

Grants and funding

This work was supported by the National Key Research and Development Program of China (2022YFF0710800), the National Natural Science Foundation of China (61801108) and the Natural Science Foundation of Jiangsu Province (BK20201148, BK20211166)