Bioinformatics. 2021 Sep 1:btab629. doi: 10.1093/bioinformatics/btab629. Online ahead of print.
ABSTRACT
MOTIVATION: Each year, the number of published bulk and single-cell RNA-seq data sets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell type identification.
RESULTS: We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq data sets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. Additionally, we present novel potential DTU applications like the identification of cell type specific transcript isoforms as biomarkers.
AVAILABILITY: The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:34469510 | DOI:10.1093/bioinformatics/btab629
