N6-methyladenosine (m⁶A) is a prevalent methylation modification and plays a vital role in various biological processes, such as metabolism, mRNA processing, synthesis, and transport. Recent studies have suggested that m⁶A modification is related to common diseases such as cancer, tumours, and obesity. Therefore, accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of bioinformatics. However, traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs, significant time requirements and inaccurate identification of sites. But through the use of traditional experimental methods, researchers have produced many large databases of m⁶A sites. With the support of these basic databases and existing deep learning methods, we developed an m⁶A site predictor named DeepM6ASeq-EL, which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard voting. Compared to the state-of-the-art prediction method WHISTLE (average AUC 0.948 and 0.880), the DeepM6ASeq-EL had a lower accuracy in m⁶A site prediction (average AUC: 0.861 for the full transcript models and 0.809 for the mature messenger RNA models) when tested on six independent datasets.

N6-甲基腺苷 (m ⁶ A) 是一种普遍的甲基化修饰，在各种生物过程中起着至关重要的作用，例如代谢、mRNA 加工、合成和运输。最近的研究表明，m ⁶ A 修饰与癌症、肿瘤和肥胖等常见疾病有关。因此，准确预测 RNA 序列中的甲基化位点已成为生物信息学领域的一个关键问题。然而，传统的高通量测序和湿台实验技术存在成本高、时间要求长、位点识别不准确等缺点。但是通过使用传统的实验方法，研究人员已经制作了许多m ^{6 的}大型数据库一个站点。在这些基础数据库和现有深度学习方法的支持下，我们开发了一个名为 DeepM6ASeq-EL的 m ⁶ A 站点预测器，它集成了五个 LSTM 和 CNN 分类器的集成以及硬投票的组合策略。与最先进的预测方法 WHISTLE（平均 AUC 0.948 和 0.880）相比，DeepM6ASeq-EL 在 m ⁶ A 位点预测中的准确度较低（平均 AUC：完整转录本模型为 0.861，成熟模型为 0.809信使 RNA 模型）在六个独立数据集上进行测试时。