TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer

• Published in: IEEE/ACM transactions on computational biology and bioinformatics 2023-11, Vol.20 (6), p.1-13
• Main Authors: Chen, Taoning, Wu, Tingfang, Pan, Deng, Xie, Jinxing, Zhi, Jin, Wang, Xuejiao, Quan, Lijun, Lyu, Qiang
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bioinformatics; Convolutional neural network; Convolutional neural networks; Deep Learning; Feature extraction; Gene sequencing; Learning systems; multi-label classification; Neural networks; Neural Networks, Computer; Nucleotide sequence; Nucleotides; Predictions; Regulatory mechanisms (biology); RNA; RNA - genetics; RNA modification; transformer; Transformers
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2023.3307419

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.