A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2

Oulas, Anastasis; Karathanasis, Nestoras; Louloupi, Annita; Iliopoulos, Ioannis; Kalantidis, Kriton; Poirazi, Panayiota

doi:10.4161/rna.21725

Cited by 24 publications

(14 citation statements)

References 51 publications

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“…Calculations of linear (positive) or inverse (negative) correlation (Pearson correlation) for each miRNA−mRNA pair across samples and the prediction of miRNA and mRNA target relationship were implemented in C language . All three databases including TargetProfiler [20] , TargetScan [21] , and miRanda [22] were precompiled for the search of targeting relationship between miRNA and mRNA. We claimed the existence of the targeting relationship if a target prediction outcome is supported by at least one of the three databases mentioned above.…”

Section: Methodsmentioning

confidence: 99%

Correlating Bladder Cancer Risk Genes with their Targeting MicroRNAs using MMiRNA-Tar

Liu

Baker

Jiang

et al. 2015

Genomics, Proteomics &Amp; Bioinformatics

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The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov) is a valuable data resource focused on an increasing number of well-characterized cancer genomes. In part, TCGA provides detailed information about cancer-dependent gene expression changes, including changes in the expression of transcription-regulating microRNAs. We developed a web interface tool MMiRNA-Tar (http://bioinf1.indstate.edu/MMiRNA-Tar) that can calculate and plot the correlation of expression for mRNA−microRNA pairs across samples or over a time course for a list of pairs under different prediction confidence cutoff criteria. Prediction confidence was established by requiring that the proposed mRNA−microRNA pair appears in at least one of three target prediction databases: TargetProfiler, TargetScan, or miRanda. We have tested our MMiRNA-Tar tool through analyzing 53 tumor and 11 normal samples of bladder urothelial carcinoma (BLCA) datasets obtained from TCGA and identified 204 microRNAs. These microRNAs were correlated with the mRNAs of five previously-reported bladder cancer risk genes and these selected pairs exhibited correlations in opposite direction between the tumor and normal samples based on the customized cutoff criterion of prediction. Furthermore, we have identified additional 496 genes (830 pairs) potentially targeted by 79 significant microRNAs out of 204 using three cutoff criteria, i.e., false discovery rate (FDR) < 0.1, opposite correlation coefficient between the tumor and normal samples, and predicted by at least one of three target prediction databases. Therefore, MMiRNA-Tar provides researchers a convenient tool to visualize the co-relationship between microRNAs and mRNAs and to predict their targeting relationship. We believe that correlating expression profiles for microRNAs and mRNAs offers a complementary approach for elucidating their interactions.

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Section: Methodsmentioning

confidence: 99%

Correlating Bladder Cancer Risk Genes with their Targeting MicroRNAs using MMiRNA-Tar

Liu

Baker

Jiang

et al. 2015

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

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“…Similar efforts have been reported in a number of other studies, where new miRNA genes were computationally predicted [ 26 , 28 ]. In order to verify that a predicted miRNA gene/hairpin produces a functional miRNA however, a number of wet-lab experiments must be performed, requiring significant amount of time, money and effort [ 29 ]. MiRduplexSVM can provide reliable predictions about the most likely sequence of the miRNA molecule in these cases, thus guiding experimental efforts and ultimately reducing working hours and costs.…”

Section: Discussionmentioning

confidence: 99%

MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology

2015

Self Cite

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We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.

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“…The prediction of miR-155 target sequences on the Tnfα locus was performed using the microRNA target prediction tool Targetprofiler , a hidden Markov type model trained on experimentally verified miRNA targets, as developed and described by A.Oulas et al [ 58 ].…”

Section: Methodsmentioning

confidence: 99%

Long non-coding RNA SeT and miR-155 regulate the Tnfα gene allelic expression profile

et al. 2017

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It is becoming increasingly appreciated that the non-coding genome may have a great impact on the regulation of chromatin structure and gene expression. The innate immune response can be mediated upon lipopolysaccharide stimulation of macrophages which leads to immediate transcriptional activation of early responsive genes including tumor necrosis factor alpha (Tnfα). The functional role of non-coding RNAs, such as lncRNAs and microRNAs, on the transcriptional activation of proinflammatory genes and the subsequent regulation of the innate immune response is still lacking mechanistic insights. In this study we wanted to unravel the functional role of the lncRNA SeT, which is encoded from the murine Tnfα gene locus, and miR-155 on the transcriptional regulation of the Tnfα gene. We utilized genetically modified mice harboring either a deletion of the SeT promoter elements or the mature miR-155 and studied the response of macrophages to lipopolysaccharide (LPS) stimulation. We found that decreased expression of the lncRNA SeT in murine primary macrophages resulted in increased mortality of mice challenged with LPS, which was corroborated by increased Tnfα steady state mRNA levels and a higher frequency of biallelically expressing macrophages. On the contrary, miR-155 deletion resulted in reduced Tnfα mRNA levels supported by a lower frequency of biallelically expressing macrophages upon stimulation with LPS. In both cases, in the absence of either lncRNA SeT or miR-155 we observed a deregulation of the Tnfα allele homologous pairing, previously shown to regulate the switch from mono- to bi-allelic gene expression. Although lncRNA SeT was not found to be a direct target of miR-155 its stability was increased upon miR-155 deletion. This study suggests a role of the non-coding genome in mediating Tnfα mRNA dosage control based on the regulation of homologous pairing of gene alleles and their subsequent biallelic expression.

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A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2

Cited by 24 publications

References 51 publications

Correlating Bladder Cancer Risk Genes with their Targeting MicroRNAs using MMiRNA-Tar

Correlating Bladder Cancer Risk Genes with their Targeting MicroRNAs using MMiRNA-Tar

MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology

Long non-coding RNA SeT and miR-155 regulate the Tnfα gene allelic expression profile

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