Direct inference of base-pairing probabilities with neural networks improves RNA secondary structure prediction with pseudoknots

Motivation Existing approaches for predicting RNA secondary structures depend on howto decompose a secondary structure into substructures, so-called the architecture , to define their parameter space. However, the architecture has not been sufficiently investigated especially for pseudoknotted secondary structures. Results In this paper, we propose a novel algorithm to directly infer base-pairing probabilities with neural networks that does not depend on the architecture of RNA secondary structures, followed by performing the maximum expected accuracy (MEA) based decoding algorithms; Nussinov-style decoding for pseudoknot-free structures, and IPknot-style decoding for pseudoknotted structures. To train the neural networks connected to each base-pair, we adopt a max-margin framework, called structured support vector machines (SSVM), as the output layer. Our benchmarks for predicting RNA secondary structures with and without pseudoknots show that our algorithm achieves the best prediction accuracy compared with existing methods. Availability The source code is available at https://github.com/keio-bioinformatics/neuralfold/ . Contact [email protected]