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Version 3. F1000Res. 2018 Jun 27 [revised 2018 Oct 1];7:952. doi: 10.12688/f1000research.15398.3. eCollection 2018.

Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification.

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Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA.
Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27516, USA.
Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
SIB Swiss Institute of Bioinformatics, Zurich, Switzerland.
Department of Computer Science, Stony Brook University, Stony Brook, NY, 11794, USA.


Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.


DEXSeq; DRIMSeq; RNA-seq; Salmon; differential transcript usage; stageR; tximport; workflow

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