Fig. 1

A deep residual neural network for predicting polyadenylation. A Core processing elements, auxiliary RBPs, and other determinants influence polyadenylation signal affinity. B Illustration of tandem 3′ UTR alternative polyadenylation (APA) in pre-mRNA. C Residual neural network architecture. A one-hot coded representation of the PAS is used to predict the 3′ cleavage distribution. D Predicted vs measured proximal isoform log odds of native human 3′ UTR PASs measured in an MPRA (\(n=1085\)). E Predicted logit score of all human PASs as a function of PAS # relative to the distal-most PAS. F Masked softmax regression (or a LSTM) for predicting multi-PAS isoform proportions given APARENT2 and Saluki scores as input. G Left: Comparison of correlation between predicted and measured distal isoform proportions from tissue-pooled native data (20-fold cross-validation). Each model predicts logit scores which are used to fit a multi-PAS regressor. LSTM performance shown as shaded bars. Right: Improvement in Spearman r when using Saluki scores in addition to APARENT2 as input; the improvement is shown separately for genes where the maximum distance between any adjacent pair of PASs is \({\le }250\mathrm{bp}\) and \({>}250\mathrm{bp}\) respectively