M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning
Open accessAbstract
N6-methyladenosine (m 6 A) modification is the most abundant RNA methylation modification and involves various biological processes, such as RNA splicing and degradation. Recent studies have demonstrated the feasibility of identifying m 6 A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m 6 A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m 6 A sites. To predict m 6 A sites accurately within genomic sequences, we trained an ensemble of three support vector machine classifiers that explore the position-specific information and physical chemical information from position-specific k-mer nucleotide propensity, physical-chemical properties, and ring-function-hydrogen-chemical properties. We examined and compared the performance of our predictor with other state-of-the-art methods of benchmarking datasets. Comparative results showed that the proposed M6APred-EL performed more accurately for m 6 A site identification. Moreover, a user-friendly web server that implements the proposed M6APred-EL is well established and is currently available at http://server.malab.cn/M6APred-EL/. It is expected to be a practical and effective tool for the investigation of m 6 A functional mechanisms.
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- Authors
- Wei, L., Chen, H., & Su, R.
- Affiliations
- Tianjin University (CN)
- Corresponding author
- Su, Ran
- Venue
- Molecular Therapy Nucleic Acids
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 12
- Pages
- 635-644
- ISSN
- 2162-2531 (ISSN-L)
- PMC
- PMC6082921
- OpenAlex
- W2813360597
- Cited by
- 179
- References
- 70
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 11 · 2023: 14 · 2022: 28 · 2021: 37 · 2020: 29 · 2019: 40 · 2018: 7
- FWCI
- 10.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Therapy — Nucleic Acids (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61701340, 61702361)
- State Key Laboratory of Medicinal Chemical Biology
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- N6-methyladenosine, RNA methylation, m6A site prediction, ensemble learning, support vector machine, physicochemical properties, web server, RNA modification
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