PolyAMiner-Bulk Is a Deep Learning-Based Algorithm That Decodes Alternative Polyadenylation Dynamics from Bulk RNA-Seq Data

Authors

Venkata Soumith Jonnakuti
Eric J Wagner
Mirjana Maletić-Savatić
Zhandong Liu
Hari Krishna Yalamanchili

Publication Date

2-26-2024

Journal

Cell Reports Methods

DOI

10.1016/j.crmeth.2024.100707

PMID

38325383

PMCID

PMC10921021

PubMedCentral® Posted Date

2-6-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Polyadenylation, RNA-Seq, RNA, Deep Learning, Sequence Analysis, RNA, Algorithms, alternative polyadenylation (APA), post-transcriptional regulation, deep learning, large language model (LLM), bioinformatics, computational biology, gene regulation

Abstract

Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3' UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.