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DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning

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Abstract

N6-methyladenosine (m6A) is a prevalent methylation modification and plays a vital role in various biological processes, such as metabolism, mRNA processing, synthesis, and transport. Recent studies have suggested that m6A modification is related to common diseases such as cancer, tumours, and obesity. Therefore, accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics. However, traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs, significant time requirements and inaccurate identification of sites. But through the use of traditional experimental methods, researchers have produced many large databases of m6A sites. With the support of these basic databases and existing deep learning methods, we developed an m6A site predictor named DeepM6ASeq-EL, which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting. Compared to the state-of-the-art prediction method WHISTLE (average AUC 0.948 and 0.880), the DeepM6ASeq-EL had a lower accuracy in m6A site prediction (average AUC: 0.861 for the full transcript models and 0.809 for the mature messenger RNA models) when tested on six independent datasets.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61922020, 61771331, 91935302).

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Authors and Affiliations

  1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610051, China

    Juntao Chen & Quan Zou

  2. Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, 571158, China

    Quan Zou

  3. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Jing Li

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  1. Juntao Chen
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Corresponding author

Correspondence to Quan Zou.

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Juntao Chen is a senior student in University of Electronic Science and Technology of China, China. His research is currently in sequence classification with deep learning.

Quan Zou received his BS, MS and PhD degrees in computer science from Harbin Institute of Technology, China, in 2004, 2007 and 2009, respectively. He is currently a professor in the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, China. His research is in the areas of bioinformatics, machine learning and parallel computing. Several related works have been published by Science, Briefings in Bioinformatics, Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. He is the editor-in-chief of Current Bioinformatics, associate editor of IEEE Access, Frontiers in Genetics and Frontiers in Plant Science. He was selected as one of the Clarivate Analytics Highly Cited Researchers in 2018 and 2019.

Jing Li is a graduate student in Tianjin University, China. Her research interest is protein classification.

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Chen, J., Zou, Q. & Li, J. DeepM6ASeq-EL: prediction of human N6-methyladenosine (m6A) sites with LSTM and ensemble learning. Front. Comput. Sci. 16, 162302 (2022). https://doi.org/10.1007/s11704-020-0180-0

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