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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 transcrip...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570804/ https://www.ncbi.nlm.nih.gov/pubmed/34469510 http://dx.doi.org/10.1093/bioinformatics/btab629 |
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author | Tekath, Tobias Dugas, Martin |
author_facet | Tekath, Tobias Dugas, Martin |
author_sort | Tekath, Tobias |
collection | PubMed |
description | 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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8570804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-85708042021-11-08 Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle Tekath, Tobias Dugas, Martin Bioinformatics Original Papers 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/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-09-01 /pmc/articles/PMC8570804/ /pubmed/34469510 http://dx.doi.org/10.1093/bioinformatics/btab629 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Tekath, Tobias Dugas, Martin Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle |
title | Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle |
title_full | Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle |
title_fullStr | Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle |
title_full_unstemmed | Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle |
title_short | Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle |
title_sort | differential transcript usage analysis of bulk and single-cell rna-seq data with dturtle |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570804/ https://www.ncbi.nlm.nih.gov/pubmed/34469510 http://dx.doi.org/10.1093/bioinformatics/btab629 |
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