Leveraging long read RNA-seq to decipher neuronal regulation of alternative polyadenylation

Alternative polyadenylation (APA) is a pervasive co/post-transcriptional process affecting >70% of human genes which greatly enhances transcriptome diversity. Here we introduce APALORD (Alternative Polyadenylation Analysis of LOng-ReaDs), a computational framework for APA analysis of long-read (LR) RNA-seq data. For gene level APA analysis, we developed a novel method by comparing distributions of cleavage sites (CSs) between conditions. Applying APALORD to direct RNA-seq (DRS) data from human embryonic stem cells (hESCs) and derived neurons, we identified annotated and novel PASs with high positional accuracy. The transcriptome-wide APA lengthening in neurons was associated with increased usage of stronger PASs enriched for the canonical AAUAAA polyA signal, upstream UGUA and downstream GU/U-rich motifs. PAS strength was negatively correlated with the number of PASs per gene and positively correlated with gene expression levels. Application to Drosophila embryo samples led to a substantial expansion of PAS annotations and uncovered conserved features of APA regulation. A novel, low-abundance class of transcripts that do not map directly to identified PAS was identified in both species and found to display APA regulation trends. Together, APALORD offers a robust framework for high-resolution APA analysis using LR RNA-seq data across species and biological contexts. Long-read RNA sequencing enables precise mapping of RNA 3′ ends. Here, the authors develop APALORD, a computational framework for high-resolution alternative polyadenylation analysis that uncovers a preference for stronger polyadenylation site selection during neural differentiation.