Victor Chang Cardiac Research Institute, 405 Liverpool St., Darlinghurst, 2010, Australia.
St. Vincent's Clinical School, UNSW Sydney, Kensington, 2052, Australia.
Genome Biol. 2020 Jul 8;21(1):167. doi: 10.1186/s13059-020-02071-7.
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/VCCRI/Sierra .
高通量单细胞 RNA 测序 (scRNA-seq) 是研究单细胞中基因表达的强大工具。大多数当前的 scRNA-seq 生物信息学工具主要集中在分析整体表达水平上，在很大程度上忽略了 mRNA 异构体的表达。我们提出了一种计算管道 Sierra，它可以轻松地从常用的 polyA 捕获 scRNA-seq 技术生成的数据中检测差异转录本的使用。我们通过将心脏 scRNA-seq 细胞类型与匹配群体的批量 RNA-seq 进行比较，验证了 Sierra 的有效性，发现差异转录本具有显著的重叠。Sierra 可以检测人类外周血单核细胞和 Tabula Muris 中的差异转录本使用情况，以及心脏成纤维细胞中的 3'UTR 缩短。Sierra 可在 https://github.com/VCCRI/Sierra 上获得。
