Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage

Genome Biology
Charlotte SonesonMark D Robinson

Abstract

RNA-seq has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percentage spliced in, or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, and to suggest improvements to commonly used work flows. We assess the performance of representative work flows using synthetic data and explore the effect of using non-standard counting bin definitions as input to DEXSeq, a state-of-the-art inference engine. Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the a...Continue Reading

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Citations

Apr 25, 2016·Genome Biology·Mingxiang TengRafael A Irizarry
Sep 20, 2017·F1000Research·Anthony R ColomboGiridharan Ramsingh
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Datasets Mentioned

BETA
E-MTAB-3766

Methods Mentioned

BETA
RNA-seq

Software Mentioned

cufflinks
limma R package
Ensembl
cuffdiff
noaggreg
default
ArrayExpress
kallisto
DEXSeq package
Shiny

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