Dataset Information

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

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.

SUBMITTER: Tekath T

PROVIDER: S-EPMC8570804 | biostudies-literature |

REPOSITORIES: biostudies-literature

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