Delineating yeast cleavage and polyadenylation signals using deep learning

Abstract

ABSTRACT3’-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 to well-defined regulatory elements in mammals. Especially, recent deep sequencing experiments showed extensive cleavage heterogeneity for some mRNAs inSaccharomyces cerevisiaeand uncovered the polyA motif differences betweenS. cerevisiaevs.Schizosaccharomyces pombe. The findings raised the fundamental question of how polyadenylation signals are formed in yeast. Here we addressed this question by developing deep learning models to deconvolute degeneratecis-regulatory elements and quantify their positional importance in mediating yeast polyA site formation, cleavage heterogeneity, and strength. InS. cerevisiae, cleavage heterogeneity is promoted by the depletion of U-rich elements around polyA 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 developed a deep learning model to reveal the distinct motif configuration ofS. pombepolyA sites which show more precise cleavage thanS. cerevisiae. Altogether, our deep learning models provide unprecedented insights into polyA site formation across yeast species.