Introduction N6-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. Methodology In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. Results Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. Conclusion In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations.

中文翻译：

---

基于极端随机树的酿酒酵母 N6-甲基腺苷位点预测

简介 N6-甲基腺苷 (m6A) 是 RNA 中最常见的转录后修饰之一，它与多种生物学过程有关。从 RNA 序列中准确预测 m6A 位点是计算生物学中具有挑战性的任务之一。已经提出了几种利用机器学习算法的计算方法，可以加速 m6A 位点的计算机筛选，从而大大减少所涉及的实验时间和劳动力成本。方法 在这项研究中，我们提出了一种称为 ERT-m6Apred 的新型计算预测器，用于准确预测 m6A 位点。为了识别具有更多区分能力的特征编码，我们对七种不同的特征编码应用了两步特征选择技术，并确定了相应的最优特征集。结果随后，相应的基于最优特征集的极其随机树模型的性能比较表明，包含 14 个物理化学特性的伪 k 元组组合编码显着优于其他编码。此外，ERT-m6Apred 在交叉验证分析中达到了 78.84% 的准确率，这比最近报道的预测指标要好。结论 总之，ERT-m6Apred 预测酿酒酵母 m6A 位点的准确性更高，从而促进了生物学假设的产生和实验验证。ERT-m6Apred 在交叉验证分析中达到了 78.84% 的准确率，比最近报道的预测指标要好。结论 总之，ERT-m6Apred 预测酿酒酵母 m6A 位点的准确性更高，从而促进了生物学假设的产生和实验验证。ERT-m6Apred 在交叉验证分析中达到了 78.84% 的准确率，比最近报道的预测指标要好。结论 总之，ERT-m6Apred 预测酿酒酵母 m6A 位点的准确性更高，从而促进了生物学假设的产生和实验验证。