Delineating yeast cleavage and polyadenylation signals using deep learning

Abstract

3′-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared with the well-defined regulatory elements in mammals. Here we address this issue by developing deep learning models to deconvolute degeneratecis-regulatory elements and quantify their positional importance in mediating yeast poly(A) site formation, cleavage heterogeneity, and strength. InS. cerevisiae, cleavage heterogeneity is promoted by the depletion of U-rich elements around poly(A) sites as well as multiple occurrences of upstream UA-rich elements. Sites with high cleavage heterogeneity show overall lower strength. The site strength and tandem site distances modulate alternative polyadenylation (APA) under the diauxic stress. Finally, we develop a deep learning model to reveal the distinct motif configuration ofS. pombepoly(A) sites, which show more precise cleavage thanS. cerevisiae. Altogether, our deep learning models provide unprecedented insights into poly(A) site formation of yeast species, and our results highlight divergent poly(A) signals across distantly related species.