MAAPER: model-based analysis of alternative polyadenylation using 3' end-linked reads

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Most eukaryotic genes exhibit alternative polyadenylation (APA), producing diverse RNA isoforms.
• Advances in RNA sequencing, particularly for single-cell analysis, generate reads near polyadenylation sites (PAS), offering insights into APA.
• Existing methods may not fully leverage this near-site read information for comprehensive APA analysis.

Purpose of the Study:

• To introduce MAAPER, a probabilistic model-based method for accurate APA analysis using near-site RNA sequencing reads.
• To enable sensitive prediction of PAS and robust statistical examination of various APA events.
• To demonstrate MAAPER's utility across different experimental designs and data types, including single-cell transcriptomics.

Main Methods:

• Development of a probabilistic model for inferring APA isoform abundance from near-site reads.
• Implementation of algorithms for high-accuracy PAS prediction and APA event quantification.
• Validation of the method using both bulk and single-cell RNA sequencing datasets.

Main Results:

• MAAPER achieves high accuracy and sensitivity in predicting PAS.
• The method robustly quantifies different types of APA events.
• MAAPER demonstrates effective performance on both bulk and single-cell RNA sequencing data.
• The tool is applicable to unpaired and paired experimental designs.

Conclusions:

• MAAPER provides a powerful and versatile tool for APA analysis, particularly leveraging near-site RNA sequencing data.
• The method enhances the ability to study APA isoform diversity in various biological contexts.
• MAAPER facilitates robust and sensitive investigation of gene expression regulation through APA.