Sierra: discovery of differential transcript usage from polyA-captured single-cell RNA-seq data.

ABSTRACT: High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing overall expression levels, largely ignoring alternative mRNA isoform expression. We present a computational pipeline, Sierra, that readily detects differential transcript usage from data generated by commonly used polyA-captured scRNA-seq technology. We validate Sierra by comparing cardiac scRNA-seq cell types to bulk RNA-seq of matched populations, finding significant overlap in differential transcripts. Sierra detects differential transcript usage across human peripheral blood mononuclear cells and the Tabula Muris, and 3 ^'UTR shortening in cardiac fibroblasts. Sierra is available at https://github.com/V

SUBMITTER: Patrick R 

PROVIDER: S-EPMC7341584 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature