A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.

The promoter is a regulatory region of the DNA typically located upstream of a gene and plays a key role in regulating gene transcription. Accurate prediction of promoters is crucial for the analysis of gene expression patterns and for the development and understanding of genetic regulatory networks. Genomes of several species have been sequenced, and their gene content has been established to a large extent. Some bioinformatics algorithms have been developed for predicting promoters with high universality for all kinds of plants; however, few studies have been conducted to identify promoters in rice, which might affect the practical applications. Here, we present a rice promoter prediction tool, Cr-Prom. This predictor has been established using a series of sequence-based features and datasets extracted from the PlantProm and RAP-DB databases. We applied a convolutional neural network (CNN)-based strategy to construct a predictor with robust classification performance. To demonstrate our dominance, we ran experiments on a benchmark dataset using 5-fold cross-validation and compared our results with existing techniques using four figure of merits. In addition, CR-Prom was analyzed on an independent dataset. Based on the results, Cr-Prom outperformed the existing rice-specific promoter predictors.

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