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 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.
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- Software
- Single-Cell Analysis
- Sequence Analysis, RNA
- RNA-Seq
- Polyadenylation
- Gene Expression Profiling
- Bioinformatics
- 49 Mathematical sciences
- 46 Information and computing sciences
- 31 Biological sciences
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Software
- Single-Cell Analysis
- Sequence Analysis, RNA
- RNA-Seq
- Polyadenylation
- Gene Expression Profiling
- Bioinformatics
- 49 Mathematical sciences
- 46 Information and computing sciences
- 31 Biological sciences