scDAPA: detection and visualization of dynamic alternative polyadenylation from single cell RNA-seq data.
Bioinformatics
; 36(4): 1262-1264, 2020 02 15.
Article
em En
| MEDLINE
| ID: mdl-31557285
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Poliadenilação
/
RNA-Seq
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
China