Detecting differential usage of exons from RNA-seq data

By Simon Anders, Alejandro Reyes, Wolfgang Huber. Genome Research, 2012

1,588 citations recorded by the source corpus.

RNA-seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specific detection of differential isoform abundance in comparisons between conditions, cell types, or tissues. We present DEXSeq, a statistical method to test for differential exon usage in RNA-seq data. DEXSeq uses generalized linear models and offers reliable control of false discoveries by taking biological variation into account. DEXSeq detects with high sensitivity genes, and in many cases exons, that are subject to differential exon usage. We demonstrate the versatility of DEXSeq by applying it to several data sets. The method facilitates the study of regulation and function of alternative exon usage on a genome-wide scale. An implementation of DEXSeq is available as an R/Bioconductor package.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9053e46b27880b291ebdc44bc978c82c6a6ea29e

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Citations in the corpus

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “REAGENT or RESOURCE SOURCE IDENTIFIER DEXSeq Anders et al., 2012 http://www.bioconductor.org/packages/release/ bioc/html/DEXSeq.html deFuse McPherson et al., 2011 http://compbio.bccrc.ca/software/defuse/ EGC.tools (v1.”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “As in studies of differential expression, tools for identifying differentially expressed exons 73,74 can be applied in this case.”
Cross-talk between PRMT1-mediated methylation and ubiquitylation on RBM15 controls RNA splicing (2015, methodology): “(A) TAL1 (aka SCL) skips exon 2 986 and 3 as detected by DEXSeq and use alternative short 3’UTR after RBM15 was knocked down.”
rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data (2014, positive): “DEXSeq adopts a different approach of testing for the deviation of read counts on individual exons from the counts of the whole gene (21), but the statistical model does not estimate isoform proportion or use the information about alternative splicing patterns in splice junction reads.”
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data (2013, background): “: Cuffdiff had an inflated number of false positive predictions in their null model comparison [32].”
Annotation-free quantification of RNA splicing using LeafCutter (2017, positive): “The implicit normalization of the multinomial likelihood avoids the estimation of library size parameters required by methods such as DEXSEQ 10 .”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, background): “In previous work, we and others have provided examples of how isoform-level differential analysis can highlight interesting splicing and differential promoter usage between conditions 24,28 but there has been debate about the significance and reliability of such results 16,20 .”
N 6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein (2017, methodology): “Differential splicing was determined using DEXSeq (35) based on Cufflinks-predicted, non-overlapping exons.”
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor (2013, background): “These provide a large body of information that is helpful for tackling tasks that go beyond the single-standard workflow presented here.”
TCC: an R package for comparing tag count data with robust normalization strategies (2013, methodology): “Several R [7] packages have been developed for this purpose [8-14].”
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis (2013, methodology): “An exception can be made for the DEXseq Bioconductor package [46], which proposes a detection of differential exon usage based on read counts per exon and applying the DESeq normalization.”