miRNA-target prediction based on transcriptional regulation

Abstract

BackgroundmicroRNAs (miRNAs) are tiny endogenous RNAs that have been discovered in animals and plants, and direct the post-transcriptional regulation of target mRNAs for degradation or translational repression via binding to the 3'UTRs and the coding exons. To gain insight into the biological role of miRNAs, it is essential to identify the full repertoire of mRNA targets (target genes). A number of computer programs have been developed for miRNA-target prediction. These programs essentially focus on potential binding sites in 3'UTRs, which are recognized by miRNAs according to specific base-pairing rules.ResultsHere, we introduce a novel method for miRNA-target prediction that is entirely independent of existing approaches. The method is based on the hypothesis that transcription of a miRNA and its target genes tend to be co-regulated by common transcription factors. This hypothesis predicts the frequent occurrence of common cis-elements between promoters of a miRNA and its target genes. That is, our proposed method first identifies putative cis-elements in a promoter of a given miRNA, and then identifies genes that contain common putative cis-elements in their promoters. In this paper, we show that a significant number of common cis-elements occur in ~28% of experimentally supported human miRNA-target data. Moreover, we show that the prediction of human miRNA-targets based on our method is statistically significant. Further, we discuss the random incidence of common cis-elements, their consensus sequences, and the advantages and disadvantages of our method.ConclusionsThis is the first report indicating prevalence of transcriptional regulation of a miRNA and its target genes by common transcription factors and the predictive ability of miRNA-targets based on this property.

Microarray expression profile and bioinformatic analyses of circular RNA in human esophageal carcinoma

“…From the circRNA-miRNA coexpression network, we observed that the four axes were combined with the four circRNAs (hsa_circRNA_100375, hsa_circRNA_400082, hsa_circRNA_102034 and hsa_circRNA_101319, respectively). MiRNAs always negatively regulate their target genes [45][46][47]. To identify the key pathway in the four miRNA axes, we used the miRTarBase software implemented in CluePedia of Cytoscape to predict the miRNA target genes [48] and used ClueGo to predict the KEGG pathway.…”

MicroRNAs in neutrophils: potential next generation therapeutics for inflammatory ailments

“…Current target prediction algorithms rely on canonical binding through seed (nt 2–8 from the 5′ end) complementarity (reviewed in ). The experimentally verified success rate of these algorithms ranges from 18% to 70%, depending on predicted target function, illustrating a desperate need for improved miR target isolation and validation methods . In addition, the predictions do not take into account the relative amount of each target gene and their degree of suppression or other non‐canonical targets, such as the open reading frame and 5′ ends of protein coding genes and non‐coding RNAs.…”

“…The experimentally verified success rate of these algorithms ranges from 18% to 70%, depending on predicted target function, illustrating a desperate need for improved miR target isolation and validation methods. 38 In addition, the predictions do not take into account the relative amount of each target gene and their degree of suppression or other non-canonical targets,…”

Summary of talks and papers at ISCB-Asia/SCCG 2012

“…As described in article S3 of this supplement [32], Toyofumi Fujiwara (INTEC) introduced a novel method for predicting miRNA targets, based on the hypothesis that promoters of miRNAs and their targets tend to share (predicted) cis-elements [32]. In a highlights talk, Michal Linial (The Hebrew University of Jerusalem) discussed a novel algorithm, miRror2.0, which enables the discovery of combinatorial regulation of transcription by several miRNAs [33].…”