Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks

Kangkun Mao; Jun Wang; Yi Xiao

doi:10.1007/s41048-020-00114-x

Volume 6 Issue 4

Mar. 2021

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Article Navigation > Biophysics Reports > 2020 > 6(4): 146-154

Kangkun Mao, Jun Wang, Yi Xiao. Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks. Biophysics Reports, 2020, 6(4): 146-154. doi: 10.1007/s41048-020-00114-x

Citation:

Kangkun Mao, Jun Wang, Yi Xiao. Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks. Biophysics Reports, 2020, 6(4): 146-154. doi: 10.1007/s41048-020-00114-x

Kangkun Mao, Jun Wang, Yi Xiao. Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks. Biophysics Reports, 2020, 6(4): 146-154. doi: 10.1007/s41048-020-00114-x

Citation:

Kangkun Mao, Jun Wang, Yi Xiao. Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks. Biophysics Reports, 2020, 6(4): 146-154. doi: 10.1007/s41048-020-00114-x

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Prediction of RNA secondary structure with pseudoknots using coupled deep neural networks

doi: 10.1007/s41048-020-00114-x

School of Physics and Key Laboratory of Molecular Biophysics of the Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: Yi Xiao

Received Date: 12 June 2020
Rev Recd Date: 03 July 2020
Publish Date: 31 August 2020

Abstract

Abstract

Noncoding RNAs play important roles in cell and their secondary structures are vital for understanding their tertiary structures and functions. Many prediction methods of RNA secondary structures have been proposed but it is still challenging to reach high accuracy, especially for those with pseudoknots. Here we present a coupled deep learning model, called 2dRNA, to predict RNA secondary structure. It combines two famous neural network architectures bidirectional LSTM and U-net and only needs the sequence of a target RNA as input. Benchmark shows that our method can achieve state-of-the-art performance compared to current methods on a testing dataset. Our analysis also shows that 2dRNA can learn structural information from similar RNA sequences without aligning them.
- RNA secondary structure prediction,
- Deep learning,
- Minimum free energy

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References(38)

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