ArticleCell reports methods2024
PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Alternative Polyadenylation Signatures Distinguish Maladaptive Right Ventricular Remodeling in Pulmonary Hypertension: Implications for RNA-Based Diagnostics and Therapeutics.British journal of biomedical science · 2026Article
- Alternative cleavage and polyadenylation: key regulatory mechanisms in health and disease.RNA biology · 2025Review
- Exploring transcriptome plasticity at the intersection of cell signaling, metabolism and computational biology.Experimental & molecular medicine · 2025Article
- Computational methods for alternative polyadenylation and splicing in post-transcriptional gene regulation.Experimental & molecular medicine · 2025Review
- Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes.NAR genomics and bioinformatics · 2025Review
- vizAPA: visualizing dynamics of alternative polyadenylation from bulk and single-cell data.Bioinformatics (Oxford, England) · 2024Article
- CPSF3 inhibition blocks pancreatic cancer cell proliferation through disruption of core histone mRNA processing.RNA (New York, N.Y.) · 2024Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.