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

• DOI: 10.1093/bioinformatics/btz701
• Publication Date: 2019-09-04T19:28:55Z
• Authors: Congting Ye; Qian Zhou; Xiaohui Wu; Chen Yu; Guoli Ji; Daniel R Saban; Qingshun Q Li
• Keywords: 0303 health sciences; 03 medical and health sciences; Sequence Analysis, RNA; Gene Expression Profiling; RNA-Seq; Single-Cell Analysis; Polyadenylation; Software

Abstract

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 3′ 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.

Availability and implementation The scDAPA package is implemented in Shell and R, and is freely available at https://scdapa.sourceforge.io.

Supplementary information Supplementary data are available at Bioinformatics online.