vizAPA: visualizing dynamics of alternative polyadenylation from bulk and single-cell data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Alternative polyadenylation (APA) is a crucial post-transcriptional regulatory mechanism found in eukaryotes.
• The increasing availability of genome-wide APA sites, including single-cell resolution data, necessitates effective visualization tools for analysis.

Purpose of the Study:

• To develop an accessible R package, vizAPA, for visualizing alternative polyadenylation (APA) dynamics.
• To provide tools for identifying differential APA usage across various biological conditions and cell types.

Main Methods:

• Developed the vizAPA R package with unified data structures for APA data and genome annotations.
• Implemented four distinct modules for comprehensive visualization of APA dynamics.
• Enabled identification of genes with differential APA usage.

Main Results:

• vizAPA facilitates the visualization of APA dynamics from both bulk and single-cell datasets.
• The package supports the identification of genes exhibiting differential APA usage across samples and cell types.
• vizAPA integrates seamlessly as a plugin for existing APA analysis workflows.

Conclusions:

• vizAPA offers a user-friendly solution for visualizing and analyzing APA dynamics.
• The package enhances the utility of routine APA analysis pipelines by providing advanced visualization capabilities.
• vizAPA supports researchers in exploring APA regulation at both bulk and single-cell levels.