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A novel PCR-based approach to discover miRNA target genes.

ABSTRACT: MiRNAs are potent regulators of gene expression, and most miRNAs have from several to several thousands of gene targets. Validating the numerous gene targets of a given miRNA remains challenging despite the existence of various tools and databases that predict candidate gene-miRNA pairs. In the present study, we present a high-throughput but flexible method that applies a PCR-based application to simulate the binding of miRNAs to their gene targets. Using hsa-miR-377 as an illustrative example, our method was able to identify 13 potential targets of hsa-miR-377. Moreover, our results include 2 genes (SOD2 and PPM1A) that have already been verified as targets of hsa-miR-377. Our method may provide an alternative way of identifying the gene targets of miRNAs for future research.

SUBMITTER: Duan S

PROVIDER: S-EPMC4196129 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A novel PCR-based approach to discover miRNA target genes.

Duan Shiwei S Wang Yunliang Y Wang Hongwei H Wang Shufei S Ji Lindan L Dai Dongjun D Jiang Danjie D Zhang Xiaoxi X Wang Qiang Q

International journal of medical sciences 20141011 12

MiRNAs are potent regulators of gene expression, and most miRNAs have from several to several thousands of gene targets. Validating the numerous gene targets of a given miRNA remains challenging despite the existence of various tools and databases that predict candidate gene-miRNA pairs. In the present study, we present a high-throughput but flexible method that applies a PCR-based application to simulate the binding of miRNAs to their gene targets. Using hsa-miR-377 as an illustrative example, ...[more]

PMID: 25317074

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Project description:BackgroundMachine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level.Methodology/principal findingIn the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM.Conclusions/significanceMultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Dataset Information

A novel PCR-based approach to discover miRNA target genes.

Publications

A novel PCR-based approach to discover miRNA target genes.

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