1. RNA secondary structure prediction with convolutional neural networks
- Author
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Mehdi Saman Booy, Alexander Ilin, and Pekka Orponen
- Subjects
RNA structure prediction ,Deep learning ,Pseudoknotted structures ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete. Results In this paper, we propose a simple, yet effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets, RNAStrAlign and ArchiveII, for 10 RNA families, where our experiments show excellent performance of the model across a wide range of sequence lengths. Since our matrix representation and post-processing approaches do not require the structures to be pseudoknot-free, we get similar good performance also for pseudoknotted structures. Conclusion We show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any energy model.
- Published
- 2022
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