iReckon: Simultaneous isoform discovery and abundance estimation from RNA-seq data

Abstract

High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r 2 = 0.94) with the predicted abundances.

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). …”

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

“…In addition, the sample size in RNA-seq is much fewer than the number of variables (genes) (Anders et al, 2012;Beretta et al, 2014;Bernard et al, 2014;Bi and Davuluri, 2013;Bullard et al, 2010;Deng et al, 2011;Glaus et al, 2012;Han and Jiang, 2014;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Kaur et al, 2012;Kim et al, 2012;Kimes et al, 2014;Kumar et al, 2012;Lee et al, 2011;Leon-Novelo et al, 2014;Lerch et al, 2012;Li et al, 2014;Li et al, 2011;Li and Jiang, 2012;Ma and Zhang, 2013;Marioni et al, 2008;Mezlini et al, 2013;Mills et al, 2013;Mortazavi et al, 2008;Nariai et al, 2013;Nariai et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Oh et al, 2013;Oshlack et al, 2010;Pandey et al, 2013;Patro et al, 2014;Pollier et al, 2013;Rehrauer et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Ryan et al, 2012;Safikhani et al, 2013;<...>…”

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

“…The relationship between the expression level of a gene and its corresponding isoforms has sparse specificity. While methods for detecting novel isoforms take this prior biological information into consideration (Behr et al, 2013;Mezlini et al, 2013), most current isoform expression estimation methods do not take this into account.…”