WemIQ: an accurate and robust isoform quantification method for RNA-seq data

Zhang, Jing; Kuo, C.-C. Jay; Chen, Liang

doi:10.1093/bioinformatics/btu757

Cited by 25 publications

(20 citation statements)

References 32 publications

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“…Compared to microarrays, RNA expression profile has two majorly advantageous features to enable to identify complexity of isoform variability on a single gene to produce various splicing events on transcripts and another nature, allelic specific expression and allelic imbalance resulted from genetic differences in transcriptional rates (Beretta et al, 2014;Bernard et al, 2014;Deng et al, 2011;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Katz et al, 2010;Kaur et al, 2012;Kimes et al, 2014;Leon-Novelo et al, 2014;Lerch et al, 2012;Li and Jiang, 2012;Ma and Zhang, 2013;Mezlini et al, 2013;Mills et al, 2013;Nariai et al, 2013;Nariai et al, 2014;Ng et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Pandey et al, 2013;Patro et al, 2014;Rehrauer et al, 2013;Safikhani et al, 2013;Shi and Jiang, 2013;Suo et al, 2014;Trapnell et al, 2010;Vardhanabhuti et al, 2013;Wang et al, 2010;Wu et al, 2011b;Yalamanchili et al, 2014;Zhang et al, 2014;Zheng and Chen, 2009). …”

Section: Discussionmentioning

confidence: 99%

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Oh¹,

Kim

2016

Journal of the Korean Data and Information Science Society

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Thanks to recent advance of next generation sequencing techniques, RNA-seq enabled to have an unprecedented opportunity to identify transcript variants with isoform diversity and allelic imbalance (Anders et al., 2012) by different transcriptional rates. To date, it is well known that those features might be associated with the aberrant patterns of disease complexity such as tissue (Anders and Huber, 2010;Anders et al., 2012;Nariai et al., 2014) specific differential expression at isoform levels or tissue specific allelic imbalance in mal-functionality of disease processes, etc. Nevertheless, the knowledge of post-transcriptional modification and AI in transcriptomic and genomic areas has been little known in the traditional platforms due to the limitation of technology and insufficient resolution. We here stress the potential of isoform variability and allelic specific expression that are relevant to the abnormality of disease mechanisms in transcriptional genetic regulatory networks. In addition, we systematically review how robust Bayesian approaches in RNA-seq have been developed and utilized in this regard in the field.

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Section: Discussionmentioning

confidence: 99%

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Oh¹,

Kim

2016

Journal of the Korean Data and Information Science Society

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“…Due to the presence of numerous isoforms in existing annotations, inference on their abundance from RNA-seq reads has been an active field of research since 2009 (Jiang and Wong, 2009; Trapnell et al, 2010; Li et al, 2011; Zhang et al, 2014). A necessary step is to first map (or align) reads to reference genomes so that researchers know the numbers of reads generated from each exon.…”

Section: Introductionmentioning

confidence: 99%

“…Then, a common approach to summarize RNA-seq reads is to categorize the reads by the genomic regions to which they map so that the number of reads in different genomic regions can be used to distinguish the abundance of various isoforms. As different isoforms may consist of overlapping but not identical exons, many methods divide exons into subexons , which are defined as transcribed regions between every two adjacent splicing sites in annotations (Li et al, 2011; Zhang et al, 2014; Ye and Li, 2016). By this definition, every gene is composed of non-overlapping subexons and introns (i.e., non-transcribed genomic regions).…”

Section: Introductionmentioning

confidence: 99%

“…There are other isoform quantification methods with different features (Pachter, 2011). For example, SLIDE (Li et al, 2011) uses a linear model and can be used with various data types; iReckon (Mezlini et al, 2013) utilizes a regularized Expectation-Maximization algorithm; WemIQ (Zhang et al, 2014) replaces the Poisson distribution with a more general and realistic generalized Poisson distribution; eXpress (Roberts and Pachter, 2013) is an efficient streaming method based on an online-EM algorithm and is considered to be a faster version of Cufflinks with comparable performance; and Sailfish (Patro et al, 2014) is a fast alignment-free method that saves the read mapping step.…”

Section: Introductionmentioning

confidence: 99%

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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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“…POME incorporates the base-specific variation and between-base dependence that affect the read coverage profile throughout each transcript (Hu et al, 2012). WemIQ assigns different weights to reads from different gene regions when calculating the weighted log-likelihood (Zhang et al, 2015). All these aforementioned methods use the Poisson distribution to model the read counts and treat the bias values as weight factors to the Poisson rate.…”

Section: Introductionmentioning

confidence: 99%

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data

Zhang

2016

Genet. Mol. Res.

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ABSTRACT. With the rapid development of next-generation highthroughput sequencing technology, RNA-seq has become a standard and important technique for transcriptome analysis. For multi-sample RNA-seq data, the existing expression estimation methods usually deal with each single-RNA-seq sample, and ignore that the read distributions are consistent across multiple samples. In the current study, we propose a structured sparse regression method, SSRSeq, to estimate isoform expression using multi-sample RNA-seq data. SSRSeq uses a non-parameter model to capture the general tendency of non-uniformity read distribution for all genes across multiple samples. Additionally, our method adds a structured sparse regularization, which not only incorporates the sparse specificity between a gene and its corresponding isoform expression levels, but also reduces the effects of noisy reads, especially for lowly expressed genes and isoforms. Four real datasets were used to evaluate our method on isoform expression estimation. Compared with other popular methods, SSRSeq reduced the variance between multiple samples, and produced more accurate isoform expression estimations, and thus more meaningful biological interpretations.

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WemIQ: an accurate and robust isoform quantification method for RNA-seq data

Cited by 25 publications

References 32 publications

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

Beyond gene expression level: How are Bayesian methods doing a great job in quantification of isoform diversity and allelic imbalance?

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

A structured sparse regression method for estimating isoform expression level from multi-sample RNA-seq data

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