Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle

Motivation: Each year, the number of published bulk and single-cell RNA-seq datasets 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 datasets, 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. In addition, we present novel potential DTU applications like the identification of cell-type specific transcript isoforms as biomarkers. Availability and implementation: The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. Contact: tobias.tekath@wwu.de Supplementary information: Supplementary data are available at Bioinformatics online.

Automated summary

DTUrtle is a software package for analyzing bulk and single-cell RNA-seq datasets, allowing for transcript-level differential analysis with various result aggregation, visualization options, and a novel detection probability score. Successfully applied to human and mouse data, potential applications include identifying cell-type specific transcript isoforms.