Journal
BIOINFORMATICS
Volume 36, Issue 4, Pages 1262-1264Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz701
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Funding
- Fundamental Research Funds for the Central Universities in China [Xiamen University] [20720170076, 20720190106]
- National Natural Science Foundation of China [61802323, 31801268, 61573296]
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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-10x 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.
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