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

Motivation: Alternative polyadenylation (APA) plays a key post-transcriptional regulatory role in mRNA stability and functions in eukaryotes. Single cell RNA-seq (scRNA-seq) is a powerful tool to discover cellular heterogeneity at gene expression level. Given 30 enriched strategy in library construction, the most commonly used scRNA-seq protocol—10 Genomics enables us to improve the study resolution of APA to the single cell level. However, currently there is no computational tool available for investigating APA profiles from scRNA-seq data. Results: Here, we present a package scDAPA for detecting and visualizing dynamic APA from scRNA-seq data. Taking bam/sam files and cell cluster labels as inputs, scDAPA detects APA dynamics using a histogram-based method and the Wilcoxon rank-sum test, and visualizes candidate genes with dynamic APA. Benchmarking results demonstrated that scDAPA can effectively identify genes with dynamic APA among different cell groups from scRNA-seq data.This research was supported in part by the Fundamental Research Funds for the Central Universities in China [Xiamen University: 20720170076 and 20720190106], and the National Natural Science Foundation of China [61802323, 31801268 and 61573296]