Journal Article

Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation

Lou, Qian, Fahmi, Naima Ahmed, Saha, Sourav, Yong, Jeongsik, Song, Qianqian, Zhang, Wei

2025

Overview

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. Exploring post-transcriptional regulation via alternative polyadenylation and splicing This article explores how genes in complex organisms, such as humans, generate different protein isoforms through post-transcriptional mechanisms such as alternative polyadenylation and alternative splicing. The authors discuss the complexity of these regulatory processes and how they are analyzed using high-throughput RNA sequencing technologies. The study reviews various computational methods developed to detect alternative polyadenylation and alternative splicing events from RNA sequencing data. These methods help identify alternative transcript isoforms by detecting variable polyadenylation sites and splicing patterns in various conditions. The research highlights the importance of these methods in understanding gene regulation and their potential role in diseases such as cancer. While considerable progress has been made, challenges remain due to sequencing biases and technical limitations. They suggest future research should focus on integrating new technologies and data types to improve our understanding of post-transcriptional gene regulation. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

• Publisher: Nature Publishing Group UK,Springer Nature B.V,Nature Publishing Group,생화학분자생물학회
• Subject: 3' Untranslated regions / 38/39 / 631/114/2397 / 631/337/458 / Alternative Splicing / Animals / Annotations / Artificial intelligence / Biomedical and Life Sciences / Biomedicine / Computational Biology - methods / Computer applications / Deep learning / Gene Expression Regulation / Gene regulation / Genes / Genomes / High-Throughput Nucleotide Sequencing / Humans / Identification / Isoforms / Language / Localization / Machine learning / Medical Biochemistry / Molecular Medicine / Polyadenylation / Polyadenylation - genetics / Post-transcription / Proteins / Python / Review Article / RNA Processing, Post-Transcriptional / RNA, Messenger - genetics / Sequence Analysis, RNA - methods / Single-Cell Analysis / Stem Cells / Transcriptomes / 생화학