Secondary structure prediction approaches rely typically on models of equilibrium free energies that are themselves based on in vitro physical chemistry. Recent transcriptome-wide experiments of in vivo RNA structure based on SHAPE-MaP experiments provide important information that may make it possible to extend current in vitro-based RNA folding models in order to improve the accuracy of computational RNA folding simulations with respect to the experimentally measured in vivo RNA secondary structure. Here we present a machine learning approach that utilizes RNA secondary structure prediction results and nucleotide sequence in order to predict in vivo SHAPE scores. We show that this approach has a higher Pearson correlation coefficient with experimental SHAPE scores than thermodynamic folding. This could be an important step towards augmenting experimental results with computational predictions and help with RNA secondary structure predictions that inherently take in-vivo folding properties into account.
Keywords: RNA; SHAPE; SHAPE-MaP; deep learning; neural network; secondary structure.