DeepRibSt: a multi-feature convolutional neural network for predicting ribosome stalling

Ribosomes are a kind of organelle in cells, which are mainly involved in the translation process of genetic materials, but the underlying mechanisms associated with ribosome stalling are not fully understood. Thanks to the development of biological experimental techniques, many ribosome footprintings are generated, which can help us to study ribosome stalling. Effectively obtaining a precise ribosome stalling site will be helpful for the treatment of the related diseases, however there is still much room for the improvement of ribosome stalling prediction. In this study, we propose a new deep neural network model named DeepRibSt for the prediction of ribosome stalling sites. We first process the ribosome footprinting data to the training set. Then three new features, including evolutionary conservation, hydrophobicity, and amino dissociation constant, along with the previous sequence features, are extracted as input to the network. To improve the performance of the algorithm in ribosome stalling prediction, we use two convolutional layers and three fully connected layers to design a new network architecture. To verify the validity of our proposed DeepRibSt, we compare DeepRibSt with four popular deep neural networks, i.e., AlexNet, LeNet, ResNet, and LSTM on human (i.e., Battle2015 and Stumpf13) and yeast (i.e., Pop2014, Young15, and Brar12) data. To further demonstrate the effectiveness of DeepRibS, we compare DeepRibSt with the state-of-the-art method. The experimental results show that DeepRibSt outperforms all other methods and achieves the state-of-the-art performance in accuracy, recall, specificity, F1-score, and the area under the receiver operating characteristic curve (AUC).