PmiProPred: A novel method towards plant miRNA promoter prediction based on CNN-Transformer network and convolutional block attention mechanism.

Abstract

It is crucial to understand the transcription mechanisms of miRNAs, especially considering the presence of peptides encoded by miRNAs. Since promoters function as the switch for gene transcription, precisely identifying these regions is essential for fully annotating miRNA transcripts. Nonetheless, existing computational methods still have room for improvement in the characterization of promoter regions. Here, we present PmiProPred, an advanced tool designed for predicting plant miRNA promoters from a wide spectrum of genomes. It consists of two core components: multi-stream deep feature extraction (MsDFE) and multi-stream deep feature refinement (MsDFR). The MsDFE utilizes Transformer and CNN to gather multi-view features, while the MsDFR focuses on aligning and refining them using channel and spatial attention mechanisms. Ultimately, a multi-layer perceptron is employed to accomplish the miRNA promoter identification task. Across three independent testing datasets, PmiProPred achieves accuracies of 94.630%, 96.659%, and 92.480%, respectively, substantially surpassing the latest methods. Additionally, PmiProPred is employed to identify potential core promoters in the upstream 2-kb regions of intergenic miRNAs in five plant species. We further conduct cis-regulatory elements mining on the predicted promoters and perform an in-depth analysis of the identified motifs. Altogether, PmiProPred is a robust and effective tool for discovering plant miRNA promoters.