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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.01184

CircSLNN: Identifying RBP-binding sites on circRNAs via sequence labeling neural networks

 Yuqi Ju1, Liangliang Yuan2,  Yang Yang1* and Hai Zhao2
  • 1Shanghai Jiao Tong University, China
  • 2Computer Science and Engineering, Shanghai Jiao Tong University, China

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 studies.
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 0.787. We assess the performance on full-length RNA sequences, the proposed model outperforms previous classification-based models by a large margin.

Keywords: RNA-protein binding sites, Sequence labeling, Convolutional Neural Network, bidirectional LSTM neural network, deep learning

Received: 24 Aug 2019; Accepted: 25 Oct 2019.

Copyright: © 2019 Ju, Yuan, Yang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Yang Yang, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai Municipality, China, yangyang@cs.sjtu.edu.cn