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

Abstract

N6-methyladenosine (m⁶A) 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 m⁶A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m⁶A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m⁶A sites. To predict m⁶A 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 m⁶A 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 m⁶A functional mechanisms.

Keywords: N6-methyladenosine; PS(k-mer)NP; RNA methylation; ensemble learning; support vector machine.

Graphical abstract

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 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 3

Performance of the Ensemble Classifier and the Classifier Based on the Feature Fusion 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.