sincFold: end-to-end learning of short- and long-range interactions for RNA folding

Abstract

AbstractMotivationCoding and non-coding RNA molecules participate in many important biological processes. Non-coding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which has stagnated in the last decades. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but still leaving a wide margin for improvement.ResultsIn this work we present sincFold, an end-to-end deep learning approach that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on hierarchical 1D-2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments on well known benchmark datasets were conducted, comparing sincFold against classical methods and recent deep learning models. Results show that sincFold can outperform state-of-the-art methods on all datasets assessed.AvailabilityThe source code is available athttps://github.com/sinc-lab/sincFold/(v0.13) and a webdemo is provided athttps://sinc.unl.edu.ar/web-demo/sincFoldContactlbugnon@sinc.unl.edu.ar