Quantifying alternative splicing from paired-end RNA-sequencing data

Rossell, David; Attolini, Camille Stephan‐Otto; Kroiss, Manuel; Stöcker, Almond

doi:10.1214/13-aoas687

Cited by 40 publications

(41 citation statements)

References 31 publications

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“…Thus, the main alternative is to make a calculation of DS using isoform-level quantitations. A vast number of methods is available for gene isoform quantification, such as MISO 28 , BitSeq 36 , casper 37 , Cufflinks 38 , RSEM 39 , FlipFlop 40 and more recent, extremely fast pseudoalignment-based methods, such as Sailfish 41 , kallisto 42 and Salmon 43 . Additionally, Cufflinks , casper and FlipFlop allow for de novo transcriptome assembly.…”

Section: Approaches To Ds and Sqtl Analysesmentioning

confidence: 99%

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

2016

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There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.

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Section: Approaches To Ds and Sqtl Analysesmentioning

confidence: 99%

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

2016

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“…These tools allow for the analysis of RNA-seq data in terms of sub-exon paths, as in casper (Rossell et al 2014). …”

Section: Sub-exon Path Calculationsmentioning

confidence: 99%

The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing

et al. 2015

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RNA-seq by poly(A) selection is currently the most common protocol for whole transcriptome sequencing as it provides a broad, detailed, and accurate view of the RNA landscape. Unfortunately, the utility of poly(A) libraries is greatly limited when the input RNA is degraded, which is the norm for research tissues and clinical samples, especially when specimens are formalin-fixed. To facilitate the use of RNA sequencing beyond cell lines and in the clinical setting, we developed an exomecapture transcriptome protocol with greatly improved performance on degraded RNA. Capture transcriptome libraries enable measuring absolute and differential gene expression, calling genetic variants, and detecting gene fusions. Through validation against gold-standard poly(A) and Ribo-Zero libraries from intact RNA, we show that capture RNA-seq provides accurate and unbiased estimates of RNA abundance, uniform transcript coverage, and broad dynamic range. Unlike poly(A) selection and Ribo-Zero depletion, capture libraries retain these qualities regardless of RNA quality and provide excellent data from clinical specimens including formalin-fixed paraffin-embedded (FFPE) blocks. Systematic improvements across key applications of RNA-seq are shown on a cohort of prostate cancer patients and a set of clinical FFPE samples. Further, we demonstrate the utility of capture RNA-seq libraries in a patient with a highly malignant solitary fibrous tumor (SFT) enrolled in our clinical sequencing program called MI-ONCOSEQ. Capture transcriptome profiling from FFPE revealed two oncogenic fusions: the pathognomonic NAB2-STAT6 inversion and a therapeutically actionable BRAF fusion, which may drive this specific cancer's aggressive phenotype.

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“…To use the read information, an efficient data summary is needed to preserve the most relevant information for isoform quantification while limiting the computational complexity to a manageable level (Rossell et al, 2014). We write each read as…”

Section: Methodsmentioning

confidence: 99%

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

Zhao

Zhang

2018

Ann. Appl. Stat.

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Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call “joint modeling of multiple RNA-seq samples for accurate isoform quantification” (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ’s advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

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Quantifying alternative splicing from paired-end RNA-sequencing data

Cited by 40 publications

References 31 publications

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics

The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

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