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JEDI: Circular RNA Prediction based on Junction Encoders and Deep Interaction among Splice Sites

View ORCID ProfileJyun-Yu Jiang, View ORCID ProfileChelsea J.-T. Ju, View ORCID ProfileJunheng Hao, Muhao Chen, Wei Wang
doi: https://doi.org/10.1101/2020.02.03.932038
Jyun-Yu Jiang
1Department of Computer Science, University of California, Los Angeles, 90095, USA
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  • For correspondence: jyunyu@cs.ucla.edu
Chelsea J.-T. Ju
1Department of Computer Science, University of California, Los Angeles, 90095, USA
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Junheng Hao
1Department of Computer Science, University of California, Los Angeles, 90095, USA
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Muhao Chen
2Department of Computer and Information Science, University of Pennsylvania, Philadelphia, 19104, USA
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Wei Wang
1Department of Computer Science, University of California, Los Angeles, 90095, USA
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Abstract

Circular RNA is a novel class of endogenous non-coding RNAs that have been largely discovered in eukaryotic transcriptome. The circular structure arises from a non-canonical splicing process, where the donor site backsplices to an upstream acceptor site. These circular form of RNAs are conserved across species, and often show tissue or cell-specific expression. Emerging evidences have suggested its vital roles in gene regulation, which are further associated with various types of diseases. As the fundamental effort to elucidate its function and mechanism, numerous efforts have been devoted to predicting circular RNA from its primary sequence. However, statistical learning methods are constrained by the information presented with explicit features, and the existing deep learning approach falls short on fully exploring the positional information of the splice sites and their deep interaction.

We present an effective and robust end-to-end framework, JEDI, for circular RNA prediction using only the nucleotide sequence. Our framework first leverages the attention mechanism to encode each junction site based on deep bidirectional recurrent neural networks and then presents the novel cross-attention layer to model deep interaction among these sites for backsplicing. Finally, JEDI is capable of not only addressing the task of circular RNA prediction but also interpreting the relationships among splice sites to discover the hotspots for backsplicing within a gene region. Experimental evaluations demonstrate that JEDI significantly outperforms several state-of-the-art approaches in circular RNA prediction on both isoform-level and gene-level. Moreover, JEDI also shows promising results on zero-shot backsplicing discovery, where none of the existing approaches can achieve.

The implementation of our framework is available at https://github.com/hallogameboy/JEDI.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 10, 2020.
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JEDI: Circular RNA Prediction based on Junction Encoders and Deep Interaction among Splice Sites
Jyun-Yu Jiang, Chelsea J.-T. Ju, Junheng Hao, Muhao Chen, Wei Wang
bioRxiv 2020.02.03.932038; doi: https://doi.org/10.1101/2020.02.03.932038
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JEDI: Circular RNA Prediction based on Junction Encoders and Deep Interaction among Splice Sites
Jyun-Yu Jiang, Chelsea J.-T. Ju, Junheng Hao, Muhao Chen, Wei Wang
bioRxiv 2020.02.03.932038; doi: https://doi.org/10.1101/2020.02.03.932038

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