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Updated: May 24, 2025
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
Published on: June 24, 2021
Protocol for unlocking alternative polyadenylation insights from bulk RNA-seq data with PolyAMiner-Bulk
Venkata Jonnakuti1, Sriya Jonnakuti2, Hari Krishna Yalamanchili3
1Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Program in Quantitative and Computational Biology, Baylor College of Medicine, Houston, TX 77030, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA.
PolyAMiner-Bulk decodes alternative polyadenylation (APA) dynamics using deep learning on bulk RNA sequencing data. This tool helps identify and quantify APA events for studying post-transcriptional regulation.
Area of Science:
- Bioinformatics
- Molecular Biology
- Genomics
Background:
- Alternative polyadenylation (APA) is a key post-transcriptional regulation mechanism influencing mRNA diversity and function.
- Analyzing APA dynamics traditionally requires specialized sequencing data, limiting its application in bulk RNA sequencing (RNA-seq).
Purpose of the Study:
- To introduce PolyAMiner-Bulk, a novel deep-learning algorithm for identifying and quantifying APA events from standard bulk RNA-seq data.
- To enable researchers to investigate differential APA usage between experimental conditions.
Main Methods:
- Development of a deep-learning model (PolyAMiner-Bulk) to process pre-processed bulk RNA-seq data.
- Implementation of a protocol involving data preparation, algorithm execution, and results interpretation.
- Utilizing command-line tools for efficient data analysis.
Main Results:
- PolyAMiner-Bulk successfully identifies and quantifies APA events from bulk RNA-seq datasets.
- The algorithm facilitates the exploration of differential APA usage, providing insights into regulatory mechanisms.
Conclusions:
- PolyAMiner-Bulk offers a powerful and accessible method for studying APA dynamics using widely available bulk RNA-seq data.
- This tool enhances the understanding of post-transcriptional regulation and its impact on gene expression.
