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Modeling RNA degradation for RNA-Seq with applications.

ABSTRACT: RNA-Seq is widely used in biological and biomedical studies. Methods for the estimation of the transcript's abundance using RNA-Seq data have been intensively studied, many of which are based on the assumption that the short-reads of RNA-Seq are uniformly distributed along the transcripts. However, the short-reads are found to be nonuniformly distributed along the transcripts, which can greatly reduce the accuracies of these methods based on the uniform assumption. Several methods are developed to adjust the biases induced by this nonuniformity, utilizing the short-read's empirical distribution in transcript. As an alternative, we found that RNA degradation plays a major role in the formation of the short-read's nonuniform distribution and thus developed a new approach that quantifies the short-read's nonuniform distribution by precisely modeling RNA degradation. Our model of RNA degradation fits RNA-Seq data quite well, and based on this model, a new statistical method was further developed to estimate transcript expression level, as well as the RNA degradation rate, for individual genes and their isoforms. We showed that our method can improve the accuracy of transcript isoform expression estimation. The RNA degradation rate of individual transcript we estimated is consistent across samples and/or experiments/platforms. In addition, the RNA degradation rate from our model is independent of the RNA length, consistent with previous studies on RNA decay rate.

SUBMITTER: Wan L

PROVIDER: S-EPMC3616752 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Modeling RNA degradation for RNA-Seq with applications.

Wan Lin L Yan Xiting X Chen Ting T Sun Fengzhu F

Biostatistics (Oxford, England) 20120221 4

RNA-Seq is widely used in biological and biomedical studies. Methods for the estimation of the transcript's abundance using RNA-Seq data have been intensively studied, many of which are based on the assumption that the short-reads of RNA-Seq are uniformly distributed along the transcripts. However, the short-reads are found to be nonuniformly distributed along the transcripts, which can greatly reduce the accuracies of these methods based on the uniform assumption. Several methods are developed ...[more]

PMID: 22353193

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Project description: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.

Dataset Information

Modeling RNA degradation for RNA-Seq with applications.

Publications

Modeling RNA degradation for RNA-Seq with applications.

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