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Cited by 179RNA modifications and cancerMachine Learning in BioinformaticsRNA regulation and disease

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M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

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Wei, Leyi, Chen, Huangrong, Su, Ran·2018·Molecular Therapy Nucleic Acids
DOI 10.1016/j.omtn.2018.07.004PMC PMC6082921
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Abstract

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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Crossref · 10.1016/j.omtn.2018.07.004

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Graphical abstract CC BY-NC-ND · PubMed Central
Figure 1 Performance of the PS(k-mer)NP Feature Descriptor with Varied k Values on Benchmarking Dataset (A) ROC curves of the PS(k-mer)NP feature descriptor under different k values (k = 1, 2, 3, 4, 5). (B) PR curves of the PS(k-mer)NP feature descriptor under different k values (k = 1, 2, 3, 4, 5).
Figure 1 Performance of the PS(k-mer)NP Feature Descriptor with Varied k Values on Benchmarking Dataset (A) ROC curves of the PS(k-mer)NP feature descriptor under different k values (k = 1, 2, 3, 4, 5). (B) PR curves of the PS(k-mer)NP feature descriptor under different k values (k = 1, 2, 3, 4, 5).CC BY-NC-ND · PubMed Central
Figure 2 Performance of the Proposed Ensemble Classifier and Its Three Base Classifiers (A) ROC curves of the proposed ensemble classifier and its three base classifiers trained with PCP, PS(1-mer)NP, and RFHC-GAC, respectively. (B) PR curves of the proposed ensemble classifier and its three base classifiers trained with PCP, PS(1-mer)NP, and RFHC-GAC, respectively.
Figure 2 Performance of the Proposed Ensemble Classifier and Its Three Base Classifiers (A) ROC curves of the proposed ensemble classifier and its three base classifiers trained with PCP, PS(1-mer)NP, and RFHC-GAC, respectively. (B) PR curves of the proposed ensemble classifier and its three base classifiers trained with PCP, PS(1-mer)NP, and RFHC-GAC, respectively.CC BY-NC-ND · PubMed Central
Figure 3 Performance of the Ensemble Classifier and the Classifier Based on the Feature Fusion Strategy
Figure 3 Performance of the Ensemble Classifier and the Classifier Based on the Feature Fusion StrategyCC BY-NC-ND · PubMed Central
Figure 4 Framework of M6APred-EL The procedure of m6A site identification is described in the following three steps. First, original input RNA sequences are scanned with a 51-nt window. Those sequences, including the GAC motif, were retained; the others were discarded. Second, the remaining sequences are submitted to three feature representation approaches and predicted by three well-trained SVM models to generate three prediction scores. Finally, the prediction result is generated by the major voting strategy.
Figure 4 Framework of M6APred-EL The procedure of m6A site identification is described in the following three steps. First, original input RNA sequences are scanned with a 51-nt window. Those sequences, including the GAC motif, were retained; the others were discarded. Second, the remaining sequences are submitted to three feature representation approaches and predicted by three well-trained SVM models to generate three prediction scores. Finally, the prediction result is generated by the major voting strategy.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Wei, L., Chen, H., & Su, R.
Affiliations
Tianjin University (CN)
Corresponding author
Su, Ran
ORCID
Venue
Molecular Therapy Nucleic Acids
Publisher
Elsevier BV
Published
2018-09
Volume
12
Pages
635-644
ISSN
2162-2531 (ISSN-L)
DOI
10.1016/j.omtn.2018.07.004
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
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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