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

Congting Ye; Qian Zhou; Xiaohui Wu; Chen Yu; Guoli Ji; Daniel R Saban; Qingshun Q Li

doi:10.1093/bioinformatics/btz701

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

Bioinformatics. 2020 Feb 15;36(4):1262-1264. doi: 10.1093/bioinformatics/btz701.

Authors

Congting Ye¹, Qian Zhou¹, Xiaohui Wu^{2

3}, Chen Yu⁴, Guoli Ji^{2

3}, Daniel R Saban^{4

5}, Qingshun Q Li^{1

6}

Affiliations

¹ Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China.
² Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.
³ National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China.
⁴ Department of Ophthalmology, Duke University, Durham, NC 27710, USA.
⁵ Department of Immunology, Duke University, Durham, NC 27710, USA.
⁶ Graduate College of Biomedical Sciences, Western University of Health Sciences, Pomona, CA 91766, USA.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Expression Profiling
Polyadenylation*
RNA-Seq*
Sequence Analysis, RNA
Single-Cell Analysis
Software

Grants and funding

R01 EY030906/EY/NEI NIH HHS/United States