IMR Press / FBL / Volume 27 / Issue 9 / DOI: 10.31083/j.fbl2709269
Open Access Original Research
M1ARegpred: Epitranscriptome Target Prediction of N1-methyladenosine (m1A) Regulators Based on Sequencing Features and Genomic Features
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1 Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, 350005 Fuzhou, Fujian, China
2 Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, 215123 Suzhou, Jiangsu, China
3 Research Center for BioSystems, Land Use, and Nutrition (IFZ), Institute of Applied Microbiology, Justus-Liebig-University Giessen, 35392 Giessen, Germany
4 Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, 350005 Fuzhou, Fujian, China
*Correspondence: wushuxiang@fjmu.edu.cn (Shu-Xiang Wu)
These authors contributed equally.
Academic Editor: Graham Pawelec
Front. Biosci. (Landmark Ed) 2022, 27(9), 269; https://doi.org/10.31083/j.fbl2709269
Submitted: 25 May 2022 | Revised: 25 July 2022 | Accepted: 16 August 2022 | Published: 28 September 2022
Copyright: © 2022 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.
Abstract

Background: N1-methyladenosine (m1A) is a reversible post-transcriptional modification in mRNA, which has been proved to play critical roles in various biological processes through interaction with different m1A regulators. There are several m1A regulators existing in the human genome, including YTHDF1-3 and YTHDC1. Methods: Several techniques have been developed to identify the substrates of m1A regulators, but their binding specificity and biological functions are not yet fully understood due to the limitations of wet-lab approaches. Here, we submitted the framework m1ARegpred (m1A regulators substrate prediction), which is based on machine learning and the combination of sequence-derived and genome-derived features. Results: Our framework achieved area under the receiver operating characteristic (AUROC) scores of 0.92 in the full transcript model and 0.857 in the mature mRNA model, showing an improvement compared to the existing sequence-derived methods. In addition, motif search and gene ontology enrichment analysis were performed to explore the biological functions of each m1A regulator. Conclusions: Our work may facilitate the discovery of m1A regulators substrates of interest, and thereby provide new opportunities to understand their roles in human bodies.

Keywords
m1A
substrate
machine learning
Funding
XRCZX2020012/Scientific Research Foundation for Advanced Talents of Fujian Medical University
Figures
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