Abstract
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.
Highlights
RNA-seq experiments can be analyzed to detect differences across groups of samples in total gene expression – the total expression produced by all isoforms of a gene – and differences in transcript isoform usage within a gene
In the Methods, we describe the simulation dataset, the quantification data generated by Salmon and imported via tximport, and the two statistical models for differential transcript usage (DTU), DRIMSeq and DEXSeq, that are highlighted in this workflow
We demonstrate how stageR can be used with the output of DRIMSeq or DEXSeq to control the overall false discovery rate (OFDR) across genes and transcripts
Summary
RNA-seq experiments can be analyzed to detect differences across groups of samples in total gene expression – the total expression produced by all isoforms of a gene – and differences in transcript isoform usage within a gene. If the amount of expression switches among two or more isoforms of a gene, the total gene expression may not change by a detectable amount, but the differential transcript usage (DTU) is biologically relevant. Reyes and Huber[2] found that alternative usage of transcription start and termination sites was a more common event than alternative splicing when examining DTU events across tissues in GTEx. Specific patterns of DTU have been identified in a number of diseases, including cancer, retinal diseases, and neurological disorders, among others[3]. We have a separate sentence describing stageR and its connection to the DTU methods, and SGSeq (and we mention its leveraging of DEXSeq or limma)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
