Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing

Zhen-Dong Zhong, Ying-Yuan Xie, Hong-Xuan Chen, Ye-Lin Lan, Xue-Hong Liu, Jing-Yun Ji, Fu Wu, Lingmei Jin, Jiekai Chen, Daniel W. Mak, Zhang Zhang () and Guan-Zheng Luo ()
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Zhen-Dong Zhong: Sun Yat-sen University
Ying-Yuan Xie: Sun Yat-sen University
Hong-Xuan Chen: Sun Yat-sen University
Ye-Lin Lan: Sun Yat-sen University
Xue-Hong Liu: Sun Yat-sen University
Jing-Yun Ji: Sun Yat-sen University
Fu Wu: Sun Yat-sen University
Lingmei Jin: Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences
Jiekai Chen: Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences
Daniel W. Mak: The University of Hong Kong
Zhang Zhang: Sun Yat-sen University
Guan-Zheng Luo: Sun Yat-sen University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract N6-methyladenosine (m6A) has been increasingly recognized as a new and important regulator of gene expression. To date, transcriptome-wide m6A detection primarily relies on well-established methods using next-generation sequencing (NGS) platform. However, direct RNA sequencing (DRS) using the Oxford Nanopore Technologies (ONT) platform has recently emerged as a promising alternative method to study m6A. While multiple computational tools are being developed to facilitate the direct detection of nucleotide modifications, little is known about the capabilities and limitations of these tools. Here, we systematically compare ten tools used for mapping m6A from ONT DRS data. We find that most tools present a trade-off between precision and recall, and integrating results from multiple tools greatly improve performance. Using a negative control could improve precision by subtracting certain intrinsic bias. We also observed variation in detection capabilities and quantitative information among motifs, and identified sequencing depth and m6A stoichiometry as potential factors affecting performance. Our study provides insight into the computational tools currently used for mapping m6A based on ONT DRS data and highlights the potential for further improving these tools, which may serve as the basis for future research.

Date: 2023
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DOI: 10.1038/s41467-023-37596-5

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