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M1ARegpred: Epitranscriptome Target Prediction of N1-methyladenosine (m1A) Regulators Based on Sequencing Features
Jia-Hui Yao1,2, Meng-Xian Lin1, Wen-Jun Liao1
1Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, 350005 Fuzhou, Fujian, China.
Frontiers in Bioscience (Landmark Edition)
|October 12, 2022
Summary
We developed m1ARegpred, a machine learning tool to predict N1-methyladenosine (m1A) regulator substrates. This method improves understanding of m1A modifications and their roles in biological processes.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genomics
Background:
- N1-methyladenosine (m1A) is a reversible mRNA modification crucial for biological processes.
- m1A regulators, such as YTHDF1-3 and YTHDC1, interact with m1A sites.
- Understanding m1A regulator function is limited by current experimental methods.
Purpose of the Study:
- To develop a computational framework for predicting m1A regulator substrates.
- To enhance the understanding of m1A regulator binding specificity and biological roles.
Main Methods:
- Developed m1ARegpred, a machine learning framework for m1A regulator substrate prediction.
- Integrated sequence-derived and genome-derived features for improved prediction accuracy.
- Utilized motif search and gene ontology enrichment analysis.
Main Results:
- Achieved high AUROC scores: 0.92 for the full transcript model and 0.857 for the mature mRNA model.
- Demonstrated superior performance compared to existing sequence-derived methods.
- Identified potential biological functions of m1A regulators through enrichment analysis.
Conclusions:
- m1ARegpred facilitates the discovery of m1A regulator substrates.
- Provides new avenues for investigating the roles of m1A regulators in human biology.
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