Termin(A) n tor: Polyadenylation Site Prediction Using Deep Learning Models

As a widespread RNA processing machinery, alternative polyadenylation plays a crucial role in gene regulation. To help decipher its underlying mechanism and understand its impact, it is desirable to comprehensively profile 3’-untranslated region cleavage and associated polyadenylation sites. State-of-the-art polyadenylation site detection tools are influenced either by library preparation or manually selected features. Here we present Termin(A)tor, a deep neural network-based profiling pipeline to predict polyadenylation sites from RNA-seq data. We show how Termin(A)tor outperforms competing tools in sensitivity and precision on experimental transcriptome sequence data. We also demonstrate applications of Termin(A)tor with both short-read and long-read sequencing technologies.