In Frontiers in genetics ; h5-index 62.0
The interactions between RNAs and RNA binding proteins (RBPs) are crucial for understanding post-transcriptional regulation mechanisms. A lot of computational tools have been developed to automatically predict the binding relationship between RNAs and RBPs. However, most of the methods can only predict the presence or absence of binding sites for a sequence fragment, without providing specific information on the position or length of the binding sites. Besides, the existing tools focus on the interaction between RBPs and linear RNAs, while the binding sites on circular RNAs (circRNAs) have been rarely studied. In this study, we model the prediction of binding sites on RNAs as a sequence labeling problem, and propose a new model called circSLNN to identify the specific location of RBP-binding sites on circRNAs. CircSLNN is driven by pretrained RNA embedding vectors and a composite labeling model. On our constructed circRNA datasets, our model has an average F1 score of 0.790. We assess the performance on full-length RNA sequences, the proposed model outperforms previous classification-based models by a large margin.
Ju Yuqi, Yuan Liangliang, Yang Yang, Zhao Hai
RNA–protein binding sites, bidirectional LSTM neural network, convolutional neural network, deep learning, sequence labeling