scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Alternative polyadenylation (APA) is a crucial post-transcriptional regulatory mechanism affecting mRNA stability and function in eukaryotes.
• Single-cell RNA sequencing (scRNA-seq) offers high resolution for studying cellular heterogeneity.
• Existing scRNA-seq protocols, like 10x Genomics, are 3' end biased, making them suitable for APA analysis at the single-cell level.
• A computational gap exists for analyzing APA profiles within scRNA-seq data.

Purpose of the Study:

• To introduce scDAPA, a novel computational package for detecting and visualizing dynamic APA from scRNA-seq data.
• To enable the investigation of APA at single-cell resolution, addressing the current lack of specialized tools.

Main Methods:

• scDAPA utilizes bam/sam files and cell cluster labels as input.
• It employs a histogram-based method combined with the Wilcoxon rank-sum test to identify APA dynamics.
• The package visualizes candidate genes exhibiting dynamic APA patterns.

Main Results:

• Benchmarking confirmed scDAPA's effectiveness in identifying genes with dynamic APA across distinct cell populations.
• The tool successfully detects and visualizes APA variations at the single-cell level.

Conclusions:

• scDAPA provides a valuable computational solution for exploring dynamic APA in scRNA-seq datasets.
• This tool enhances the understanding of gene regulation and cellular heterogeneity through single-cell APA profiling.