ANOSVA: a statistical method for detecting splice variation from expression data

Cline, Melissa; Blume, John E.; Cawley, Simon; Clark, Tyson A.; Hu, Jing-Shan; Lü, Gang; Salomonis, Nathan; Wang, Hui; Williams, Alan J.

doi:10.1093/bioinformatics/bti1010

Cited by 52 publications

(48 citation statements)

References 19 publications

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“…Robinson et al (2010a) extended edgeR with generalized linear models (GLMs) and the Cox-Reid dispersion estimator, discussed below. The basic approach of using exoncondition interactions in linear or generalized linear models to detect differential exon usage has been explored before by Cline et al (2005) and Purdom et al (2008) for exon microarrays and by Blekhman et al (2010) for RNA-seq data. Our method can be seen as a further development of these approaches that also incorporated ideas from DESeq (Anders and Huber 2010).…”

Section: Biological Variabilitymentioning

confidence: 99%

Detecting differential usage of exons from RNA-seq data

2012

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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.

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Section: Biological Variabilitymentioning

confidence: 99%

Detecting differential usage of exons from RNA-seq data

2012

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“…Robinson et al (2010a) extended edgeR with generalized linear models (GLMs) and the Cox-Reid dispersion estimator, discussed later. The basic approach of using exon-condition interactions in linear or generalized linear models to detect differential exon usage has been explored before by Cline et al (2005) and Purdom et al (2008) for exon microarrays and by Blekhman et al (2010) for RNA-Seq data. Our approach can be seen as a further development of these approaches that also incorporated ideas from DESeq (Anders and Huber, 2010).…”

Section: Introductionmentioning

confidence: 99%

Detecting differential usage of exons from RNA-Seq data

Anders¹,

Reyes²,

Huber³

2012

Nature Precedings

326

454

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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 employs generalized linear models and offers reliable control of false discoveries by taking biological variation into account. DEXSeq detect genes, and in many cases specific exons, that are subject to differential exon usage with high sensitivity. 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.

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“…Several computational methods have been proposed for analysis of alternative splicing from Exon array data (Affymetrix 2005a;Cline et al 2005;Clark et al 2007;Yeo et al 2007). Most notably, Affymetrix has developed a tool, ExACT, which compares the ''splicing index'' metric across different sample groups to identify differentially used exons (Gardina et al 2006;Clark et al 2007).…”

Section: Introductionmentioning

confidence: 99%

MADS: A new and improved method for analysis of differential alternative splicing by exon-tiling microarrays

Xing¹,

Stoilov²,

Kapur³

et al. 2008

RNA

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We describe a method, microarray analysis of differential splicing (MADS), for discovery of differential alternative splicing from exon-tiling microarray data. MADS incorporates a series of low-level analysis algorithms motivated by the ''probe-rich'' design of exon arrays, including background correction, iterative probe selection, and removal of sequence-specific cross-hybridization to off-target transcripts. We used MADS to analyze Affymetrix Exon 1.0 array data on a mouse neuroblastoma cell line after shRNA-mediated knockdown of the splicing factor polypyrimidine tract binding protein (PTB). From a list of exons with predetermined inclusion/exclusion profiles in response to PTB depletion, MADS recognized all exons known to have large changes in transcript inclusion levels and offered improvement over Affymetrix's analysis procedure. We also identified numerous novel PTB-dependent splicing events. Thirty novel events were tested by RT-PCR and 27 were confirmed. This work demonstrates that the exon-tiling microarray design is an efficient and powerful approach for global, unbiased analysis of pre-mRNA splicing.

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ANOSVA: a statistical method for detecting splice variation from expression data

Abstract: The results are available at the supplementary information site https://bioinfo.affymetrix.com/Papers/ANOSVA/

Cited by 52 publications

References 19 publications

Detecting differential usage of exons from RNA-seq data

Detecting differential usage of exons from RNA-seq data

Detecting differential usage of exons from RNA-Seq data

MADS: A new and improved method for analysis of differential alternative splicing by exon-tiling microarrays

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