DeeProPre: A promoter predictor based on deep learning. (December 2022)

• Record Type: Journal Article
• Authors: Ma, Zhi-Wen; Zhao, Jian-Ping; Tian, Jing; Zheng, Chun-Hou
• Is Part Of: Computational biology and chemistry. Volume 101(2022)
• Publication Date: 2022-12
• Subjects: Bioinformatics -- Eukaryotic promoter -- Deep learning -- Attention mechanism -- BiLSTM; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107770
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5′end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for RNA polymerase, thus affecting the level of gene expression. Therefore, the correct identification of core promoter and common structural gene is of great significance in the field of biomedicine. At present, many methods have been proposed to improve the accuracy of promoter recognition, but the performances still need to be further improved. In this study, a deep learning algorithm (DeeProPre) based on bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) was proposed. Firstly, the supervised embedding layer was applied to map the sequence to a high-dimensional space. Secondly, two 1D convolutional layers, BiLSTM and attentional mechanism layer were used for extracting features. Finally, the full connection layer activated by Sigmoid function was used to obtain the probability of classification into target categories. This model can identify the promoter region of eukaryotes with high accuracy, providing an analytical basis for further understanding of promoter physiological functions and studies of gene transcription mechanisms. The source code of DeeProPre is freely available at https://github.com/zzwwmmm/DeeProPre/tree/master. Graphical Abstract: ga1 Highlights: The correct identification of core promoters and common structural genes is of great significance in the field of biomedicine. Deep learning plays an important role in the identification of promoters generated by next-generation sequencing technology. The integration of CNN and BiLSTM can effectively avoided the problem of gradient disappearance. The attention mechanism can give large weights to TATA-box features.