IEEE/ACM Trans Comput Biol Bioinform. 2023 Nov-Dec;20(6):3623-3634. doi: 10.1109/TCBB.2023.3307419. Epub 2023 Dec 25.
Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurate prediction of RNA modifications. However, those methods generally predicted only a single type of RNA modification. In addition, such methods suffered from the scarcity of the interpretability for their predicted results. In this work, a new Transformer-based deep learning method was proposed to predict multiple RNA modifications simultaneously, referred to as TransRNAm. More specifically, TransRNAm employs Transformer to extract contextual feature and convolutional neural networks to further learn high-latent feature representations of RNA sequences relevant for RNA modifications. Importantly, by integrating the self-attention mechanism in Transformer with convolutional neural network, TransRNAm is capable of not only capturing the critical nucleotide sites that contribute significantly to RNA modification prediction, but also revealing the underlying association among different types of RNA modifications. Consequently, this work provided an accurate and interpretable predictor for multiple RNA modification prediction, which may contribute to uncovering the sequence-based forming mechanism of RNA modification sites.
准确识别 RNA 修饰位点对于理解 RNA 的功能和调控机制具有重要意义。最近的研究进展表明，基于深度学习的计算方法在准确预测 RNA 修饰方面具有很大的潜力。然而，这些方法通常只能预测单一类型的 RNA 修饰。此外，这些方法的预测结果缺乏可解释性。在这项工作中，提出了一种新的基于 Transformer 的深度学习方法，用于同时预测多种 RNA 修饰，称为 TransRNAm。具体来说，TransRNAm 使用 Transformer 提取上下文特征，使用卷积神经网络进一步学习与 RNA 修饰相关的 RNA 序列的高潜在特征表示。重要的是，通过将 Transformer 中的自注意力机制与卷积神经网络相结合，TransRNAm 不仅能够捕捉到对 RNA 修饰预测有重要贡献的关键核苷酸位点，还能够揭示不同类型的 RNA 修饰之间的潜在关联。因此，这项工作为多种 RNA 修饰预测提供了一个准确且可解释的预测器，这可能有助于揭示 RNA 修饰位点的基于序列的形成机制。
