Alternative polyadenylation(APA)is a crucial step in post-transcriptional regulation.Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification problem.Recently,computa-tional methods for predicting the usage level of alternative PASs in the same gene have been pro-posed.However,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into account.To address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given gene.To accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length target.Based on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among com-peting PASs,and outputs percentage scores representing the usage levels of all PASs of a gene.In addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and rank-ing task.Finally,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA regulation.

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts.To fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico pre-diction of TSSs solely based on genomic sequences.Most of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome scale.Here,we present DeeReCT-TSS,a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing data.We show that by effectively incorporating these two sources of information,DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types.Furthermore,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific TSSs.Finally,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states.The source code for DeeReCT-TSS is available at https://github.-com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.