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

Publication ,  Journal Article
Ye, C; Zhou, Q; Wu, X; Yu, C; Ji, G; Saban, DR; Li, QQ
Published in: Bioinformatics
February 15, 2020

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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Published In

Bioinformatics

DOI

EISSN

1367-4811

Publication Date

February 15, 2020

Volume

36

Issue

4

Start / End Page

1262 / 1264

Location

England

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
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ye, C., Zhou, Q., Wu, X., Yu, C., Ji, G., Saban, D. R., & Li, Q. Q. (2020). scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data. Bioinformatics, 36(4), 1262–1264. https://doi.org/10.1093/bioinformatics/btz701
Ye, Congting, Qian Zhou, Xiaohui Wu, Chen Yu, Guoli Ji, Daniel R. Saban, and Qingshun Q. Li. “scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data.Bioinformatics 36, no. 4 (February 15, 2020): 1262–64. https://doi.org/10.1093/bioinformatics/btz701.
Ye C, Zhou Q, Wu X, Yu C, Ji G, Saban DR, et al. scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data. Bioinformatics. 2020 Feb 15;36(4):1262–4.
Ye, Congting, et al. “scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data.Bioinformatics, vol. 36, no. 4, Feb. 2020, pp. 1262–64. Pubmed, doi:10.1093/bioinformatics/btz701.
Ye C, Zhou Q, Wu X, Yu C, Ji G, Saban DR, Li QQ. scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data. Bioinformatics. 2020 Feb 15;36(4):1262–1264.

Published In

Bioinformatics

DOI

EISSN

1367-4811

Publication Date

February 15, 2020

Volume

36

Issue

4

Start / End Page

1262 / 1264

Location

England

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