Abstract
Alternative polyadenylation (APA) and alternative splicing (AS) are essential post-transcriptional mechanisms that enhance transcriptome diversity and regulate gene expression across various biological contexts. APA modifies transcript stability, localization and translation efficiency by generating mRNA isoforms with distinct 3' untranslated regions or coding sequences, while AS alters protein diversity through exon inclusion or exclusion. The advent of high-throughput RNA sequencing has driven the development of computational methods to systematically identify, quantify and analyze APA and AS events, shedding light on their regulatory roles in normal physiology and disease. These methods can be broadly categorized based on their underlying methodologies and the data types they process, with specialized tools designed for both bulk and single-cell RNA sequencing. Here, in this Review, we provide a comprehensive overview of computational strategies for APA and AS detection and differential analysis, highlighting their advantages, limitations and applications. In addition, we explore techniques specifically tailored for single-cell RNA sequencing. We enhance our understanding of APA and AS regulation across diverse biological systems by summarizing recent advancements, offering new insights into gene regulation at both the population and single-cell levels.