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.

N6-甲基腺苷（m ⁶ A）修饰是最丰富的RNA甲基化修饰，涉及多种生物学过程，例如RNA剪接和降解。最近的研究表明，使用高通量测序技术鉴定m ⁶ A峰的可行性。但是，此类技术无法准确识别特定的甲基化位点，这对于更好地了解m ⁶ A的功能很重要。在这项研究中，我们开发了一种新颖的基于机器学习的预测器，称为M6APred-EL，用于识别m ⁶ A位点。预测m ⁶我们精确地定位了基因组序列，我们训练了三个支持向量机分类器，它们从位置特异性k-mer核苷酸的倾向性，物理化学性质和环功能-氢-化学性质。我们检查了预测变量的性能，并与其他基准数据集的最新方法进行了比较。比较结果表明，所提出的M6APred-EL在m ⁶ A位点识别方面表现更为准确。而且，已经很好地实现了实施所建议的M6APred-EL的用户友好型Web服务器，并且当前可在http://server.malab.cn/M6APred-EL/上找到。有望成为调查m ⁶的实用有效工具一种功能机制。