Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression.

ABSTRACT: Alternative polyadenylation (APA) is a major mechanism of post-transcriptional regulation in various cellular processes including cell proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an "APA-aware" manner. Here, we introduce dynamic analysis of APA from single-cell RNA-seq (scDaPars), a bioinformatics algorithm to accurately quantify APA events at both single-cell and single-gene resolution using either 3'-end (10x Chromium) or full-length (Smart-seq2) scRNA-seq data. Validations in both real and simulated data indicate that scDaPars can robustly recover missing

SUBMITTER: Gao Y 

PROVIDER: S-EPMC8494218 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature