Application of Deep Learning Models to MicroRNA Transcription Start Site Identification

Abstract

MicroRNAs (miRNA) are ~22 base pair long RNAs that play important roles in regulating gene expression. Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.