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.
Highlights
RNAs participate in many important biological activities (Xiyuan et al 2017; Zhao et al 2016)
The single-sequence methods only need the sequence of target RNA as input and most of them are based on thermodynamic model or minimum free energy principle (Bellaousov et al 2013; Janssen and Giegerich 2015; Zuker 2003)
We present an end-to-end coupled deep learning model 2dRNA to predict RNA secondary structure with pseudoknots
Summary
RNAs participate in many important biological activities (Xiyuan et al 2017; Zhao et al 2016). To do these, they need to form correct tertiary structures in general. Experimental determination of RNA tertiary structures are more difficult than proteins and so many theoretical or computational methods have been proposed to predict RNA tertiary structures (Cao and Chen 2011; Das et al 2010; Jain and Schlick 2017; Wang et al 2017; Wang and Xiao 2017; Xu et al 2014) These methods use different principles, their performances all depend on the accuracy of RNA secondary structures. Accurate prediction of RNA secondary structure is very important
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