Journal article
Sierra: Discovery of differential transcript usage from polyA-captured single-cell RNA-seq data
R Patrick, DT Humphreys, V Janbandhu, A Oshlack, JWK Ho, RP Harvey, KK Lo
Genome Biology | BMC | Published : 2020
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, ..
View full abstractGrants
Awarded by National Health and Medical Research Council of Australia (NHMRC)
Awarded by Australian Research Council (ARC) Special Research Initiative in Stem Cell Science
Awarded by Foundation Leducq Transatlantic Networks of Excellence in Cardiovascular Research
Awarded by Career Development Fellowship by the National Health and Medical Research Council
Awarded by National Heart Foundation of Australia
Funding Acknowledgements
RPH acknowledges research support from the National Health and Medical Research Council of Australia (NHMRC; APP1118576, 1074386), the Australian Research Council (ARC) Special Research Initiative in Stem Cell Science (SR110001002), Foundation Leducq Transatlantic Networks of Excellence in Cardiovascular Research (15 CVD 03; 13 CVD 01), and the New South Wales Government Department of Health. JWKH is supported by a Career Development Fellowship by the National Health and Medical Research Council (1105271) and a Future Leader Fellowship by the National Heart Foundation of Australia (100848), and the HKU-USydney Strategic Partnership Fund.