REPAC: analysis of alternative polyadenylation from RNA-sequencing data

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism impacting biological processes and disease.
• Existing APA detection methods often rely on specialized sequencing, limiting data availability.
• RNA-sequencing data is more abundant, presenting an opportunity for APA analysis.

Purpose of the Study:

• To develop a computational framework, REPAC, for analyzing APA directly from standard RNA-sequencing data.
• To investigate the APA landscape during B cell activation using the developed framework.
• To evaluate the efficiency and scalability of REPAC compared to existing methods.

Main Methods:

• Development of REPAC, a novel bioinformatics framework for APA analysis from RNA-seq data.
• Application of REPAC to analyze APA changes in activated B cells.
• Benchmarking REPAC's speed and scalability against alternative APA analysis tools.

Main Results:

• REPAC enables the exploration of APA landscapes using readily available RNA-sequencing data.
• The study identified APA changes associated with B cell activation.
• REPAC demonstrated a 7-fold increase in speed and excellent scalability for large datasets.

Conclusions:

• REPAC provides an accurate, user-friendly, and efficient method for APA exploration from RNA-seq.
• This framework expands the utility of existing RNA-seq data for studying APA.
• REPAC facilitates deeper insights into the role of APA in biological systems and diseases.