Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.

ABSTRACT:

Background

RNA regulation is significantly dependent on its binding protein partner, known as the RNA-binding proteins (RBPs). Unfortunately, the binding preferences for most RBPs are still not well characterized. Interdependencies between sequence and secondary structure specificities is challenging for both predicting RBP binding sites and accurate sequence and structure motifs detection.

Results

In this study, we propose a deep learning-based method, iDeepS, to simultaneously identify the binding sequence and structure motifs from RNA sequences using convolutional neural networks (CNNs) and a bidirectional long short term memory network (BLSTM). We first perform one-hot encoding for both the sequence and predicted secondary structure, to enable subsequent convolution oper

SUBMITTER: Pan X 

PROVIDER: S-EPMC6029131 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature