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

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Abstract

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).

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Acknowledgements

The authors are grateful to Prof. Jianyang Zeng and Dr. Hailin Hu for their help with the ideas and code of this work. The authors would like to thank Dr. Dapeng Xiong for the helpful discussions about this work. The authors would also like to thank the anonymous reviewers for their insightful comments, which greatly helped to improve the quality of this paper. This work was supported in part by the National Natural Science Foundation of China under Grants 61972333, 61802328 and 61771415, in part by the Natural Science Foundation of Hunan Province in China under Grant 2019JJ50606, in part by the Research Foundation of Education Department of Hunan Province of China under Grant 19B561, and in part by the Baidu Pinecone Program.

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Authors and Affiliations

  1. School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China

    Yuan Zhang

  2. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China

    Yuan Zhang, Xizhi He, Jing Lu & Xieping Gao

  3. Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305-5101, USA

    Sai Zhang

  4. College of Medical Imaging and Inspection, Xiangnan University, Chenzhou, 423000, China

    Xieping Gao

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  1. Yuan Zhang
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Correspondence to Xieping Gao.

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Zhang, Y., Zhang, S., He, X. et al. DeepRibSt: a multi-feature convolutional neural network for predicting ribosome stalling. Multimed Tools Appl 80, 17239–17255 (2021). https://doi.org/10.1007/s11042-020-09598-8

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  • DOI: https://doi.org/10.1007/s11042-020-09598-8

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