Isoform-level microRNA-155 target prediction using RNA-seq

Abstract

Computational prediction of microRNA targets remains a challenging problem. The existing rule-based, data-driven and expression profiling approaches to target prediction are mostly approached from the gene-level. The increasing availability of RNA-seq data provides a new perspective for microRNA target prediction on the isoform-level. We hypothesize that the splicing isoform is the ultimate effector in microRNA targeting and that the proposed isoform-level approach is capable of predicting non-dominant isoform targets as well as their targeting regions that are otherwise invisible to many existing approaches. To test the hypothesis, we used an iterative expectation maximization (EM) algorithm to quantify transcriptomes at the isoform-level. The performance of the EM algorithm in transcriptome quantification was examined in simulation studies using FluxSimulator. We used joint evidence from isoform-level down-regulation and seed enrichment to predict microRNA-155 targets. We validated our computational approach using results from 149 in-house performed in vitro 3′-UTR assays. We also augmented the splicing database using exon–exon junction evidence, and applied the EM algorithm to predict and quantify 1572 cell line specific novel isoforms. Combined with seed enrichment analysis, we predicted 51 novel microRNA-155 isoform targets. Our work is among the first computational studies advocating the isoform-level microRNA target prediction.

Detecting Splicing Variants in Idiopathic Pulmonary Fibrosis from Non-Differentially Expressed Genes

“…SAMMate (http://sammate.sourceforge.net/), free Graphical User Interface (GUI) software, was employed for gene and isoform quantification. For isoform quantification, we applied the method, RAEM (Reads Assign by Expectation Maximization), reported in [24] and implemented in SAMMate. The output results of SAMMate contain not only expression abundance level measured by RPKM (Reads Per Kilobase of exon/transcript model per Million mapped reads) [68], but also aligned read counts for each gene and isoform, which were used as the input for differential expression analysis using edgeR [69].…”

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;<...>…”

“…And for an extreme case of gene, a gene has ∼ 1,000 isoform splicing events, suggesting that those isoforms with variability might be more closely related with disease and developmental processes as they generate different protein structure and functionalities, respectively (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;Lerch et al, 2012;Li et al, 2011;Li and Jiang, 2012;Ma and Zhang, 2013;Mezlini et al, 2013;Mills et al, 2013;Nariai et al, 2013;Nariai et al, 2014;Nicolae et al, 2011;Niu et al, 2014;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, 2010b;Wang et al, 2010c;Wu et al, 2011;Yalamanchili et al, 2014;Zhang et al, 2014).…”