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Related Experiment Video

Updated: Aug 11, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation

Venkata Soumith Jonnakuti1,2,3,4, Eric J Wagner5, Mirjana Maletić-Savatić1,2

  • 1Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA.

Biorxiv : the Preprint Server for Biology
|February 7, 2023
PubMed
Summary
This summary is machine-generated.

PolyAMiner-Bulk accurately identifies alternative polyadenylation (APA) dynamics from bulk RNA-seq data. This new tool reveals novel APA changes in diseases like scleroderma and Alzheimer's disease.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Alternative polyadenylation (APA) affects over half of human genes, generating mRNA transcripts with variable 3' untranslated regions (UTRs).
  • Existing computational methods struggle to accurately identify tissue- and disease-specific APA dynamics from bulk RNA-sequencing (RNA-seq) data.
  • Understanding APA is crucial for deciphering gene regulation and disease mechanisms.

Approach:

  • Developed PolyAMiner-Bulk, a next-generation bioinformatics algorithm utilizing an attention-based machine learning architecture and a vector projection engine.
  • The tool is designed for accurate inference of differential APA dynamics from bulk RNA-seq data.
  • PolyAMiner-Bulk enhances the identification of cleavage and polyadenylation sites (C/PASs) and quantifies 3'UTR length variations.

Key Points:

  • PolyAMiner-Bulk identified over twice the number of APA changes in an RBM17 knockdown dataset compared to current tools.
  • The application revealed novel APA dynamics and pathways in scleroderma pathology, identifying a key gene involved in the disease.
  • Analysis of Alzheimer's Disease RNA-seq data uncovered novel APA dynamics in post-mortem prefrontal cortexes.

Conclusions:

  • PolyAMiner-Bulk represents a significant advancement in analyzing APA from bulk RNA-seq data.
  • This method establishes a new paradigm for future APA research, offering improved accuracy and insights.
  • The tool's ability to detect disease-specific APA dynamics holds potential for biomarker discovery and therapeutic strategies.