Statistical inferences for isoform expression in RNA-Seq

Abstract

The development of RNA sequencing (RNA-Seq) makes it possible for us to measure transcription at an unprecedented precision and throughput. However, challenges remain in understanding the source and distribution of the reads, modeling the transcript abundance and developing efficient computational methods. In this article, we develop a method to deal with the isoform expression estimation problem. The count of reads falling into a locus on the genome annotated with multiple isoforms is modeled as a Poisson variable. The expression of each individual isoform is estimated by solving a convex optimization problem and statistical inferences about the parameters are obtained from the posterior distribution by importance sampling. Our results show that isoform expression inference in RNA-Seq is possible by employing appropriate statistical methods.

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

“…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). …”

“…High-throughput gene expression profile data in both array and sequencing have played a key role to address fundamental questions to arise in biomedical research (Anders and Huber, 2010;Anders et al, 2013;Aryee et al, 2009;Bar-Joseph et al, 2012;Bi and Davuluri, 2013;Bullard et al, 2010;Cumbie et al, 2011;Gao and Song, 2005;Hardcastle and Kelly, 2010;Hu et al, 2014;Jiang and Wong, 2009;Lee et al, 2011;Li and Jiang, 2012;Lin et al, 2003;Ma and Zhang, 2013;Marioni et al, 2008;Nariai et al, 2014;Nishiu et al, 2002;Oh et al, 2013;Oshlack et al, 2010;Pollier et al, 2013;Rehrauer et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Shi and Jiang, 2013;Skelly et al, 226 Sunghee Oh · Chul Soo Kim 2011; Stegle et al, 2010;Suo et al, 2014;Tarazona et al, 2011;Trapnell et al, 2012;Vardhanabhuti et al, 2013;Wang et al, 2013;Wang et al, 2010;Wu et al, 2011b;Zhao et al, 2008). Simultaneous statistical testing based on millions of transcripts and corresponding mRNA samples has made it possible to identify biomarkers that are crucially influencing the alteration of expression levels between disease and normal samples/conditions, classification of sub-groups of particular disease, therapeutic effects on biological external condition such as drug treatments and disease progression over a series of diffe...…”

Inference of Isoforms from Short Sequence Reads

“…Very recently, several methods have been proposed to characterize the expression level of each transcript (Lacroix et al, 2008;Jiang and Wong, 2009) using RNA-Seq data. In Lacroix et al (2008), the authors showed that short (single-end or paired-end) read sequences cannot theoretically guarantee a unique solution to the transcriptome reconstruction problem (i.e., the reconstruction of all expressed isoforms and their expression levels) in general even if the reads are sampled perfectly according to the length of each transcript (without random distortions and noise).…”

“…However, these results are mostly of theoretical interest because sequence read data are random in nature and may contain noise in practice. Jiang and Wong (2009) presented a more practical way to estimate the expression levels of known isoforms. The method uses maximum likelihood estimation followed by importance sampling from the posterior distribution.…”

“…The availability of all the isoforms is the basis of accurate estimation of isoform expression levels ( Jiang and Wong, 2009), which could then be used to calculate all the splicing variants quantitatively and qualitatively. Variations in isoform expression levels and splicing junctions could provide useful insight in many studies such as the study of diseases (Pagani and Baralle, 2004;Srebrow and Kornblihtt, 2006) and drug development (Williams, 2005).…”