{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:40:13Z","timestamp":1776372013366,"version":"3.51.2"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016077","name":"EU","doi-asserted-by":"publisher","award":["634789"],"award-info":[{"award-number":["634789"]}],"id":[{"id":"10.13039\/100016077","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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\u2019s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell-type identification.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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 \u2018classical\u2019 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package DTUrtle is available at https:\/\/github.com\/TobiTekath\/DTUrtle with extensive vignettes and documentation at https:\/\/tobitekath.github.io\/DTUrtle\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab629","type":"journal-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T17:39:00Z","timestamp":1630517940000},"page":"3781-3787","source":"Crossref","is-referenced-by-count":29,"title":["Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9315-5452","authenticated-orcid":false,"given":"Tobias","family":"Tekath","sequence":"first","affiliation":[{"name":"Institute of Medical Informatics, University Hospital of M\u00fcnster , M\u00fcnster 48149, Germany"}]},{"given":"Martin","family":"Dugas","sequence":"additional","affiliation":[{"name":"Institute of Medical Informatics, Heidelberg University Hospital , Heidelberg 69120, Germany"}]}],"member":"286","published-online":{"date-parts":[[2021,9,1]]},"reference":[{"key":"2023051607352391400_btab629-B1","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1038\/nature24277","article-title":"Genetic effects on gene expression across human tissues","volume":"550","author":"Aguet","year":"2017","journal-title":"Nature"},{"key":"2023051607352391400_btab629-B2","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1038\/s41467-020-18158-5","article-title":"Single cell transcriptomics comes of age","volume":"11","author":"Aldridge","year":"2020","journal-title":"Nat. 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