A study of miRNAs targets prediction and experimental validation.
ABSTRACT microRNAs (miRNAs) are 20-24 nucleotide (nt) RNAs that regulate eukaryotic gene expression post-transcriptionally by the degradation or translational inhibition of their target messenger RNAs (mRNAs). To identify miRNA target genes will help a lot by understanding their biological functions. Sophisticated computational approaches for miRNA target prediction, and effective biological techniques for validating these targets now play a central role in elucidating their functions. Owing to the imperfect complementarity of animal miRNAs with their targets, it is difficult to judge the accuracy of the prediction. Complexity of regulation by miRNA-mediated targets at protein and mRNAs levels has made it more challenging to identify the targets. To date, only a few miRNAs targets are confirmed. In this article, we review the methods of miRNA target prediction and the experimental validation for their corresponding mRNA targets in animals.
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ABSTRACT: MicroRNA (miRNA) control has emerged as a critical regulatory principle in the mammalian immune system. Genetic ablation of the miRNA machinery, as well as loss or deregulation of certain individual miRNAs, severely compromises immune development and response and can lead to immune disorders like autoimmunity and cancer. Although individual miRNAs modulate protein output from hundreds of target genes, they may impact physiological processes by regulating the concentrations of just a few key cellular proteins that may be components of a single or of functionally interrelated pathways in a given cellular context.Cell 02/2009; 136(1):26-36. · 31.96 Impact Factor
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ABSTRACT: MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.Nature Genetics 06/2005; 37(5):495-500. · 35.21 Impact Factor
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ABSTRACT: MicroRNAs are key regulators of gene expression, but the precise mechanisms underlying their interaction with their mRNA targets are still poorly understood. Here, we systematically investigate the role of target-site accessibility, as determined by base-pairing interactions within the mRNA, in microRNA target recognition. We experimentally show that mutations diminishing target accessibility substantially reduce microRNA-mediated translational repression, with effects comparable to those of mutations that disrupt sequence complementarity. We devise a parameter-free model for microRNA-target interaction that computes the difference between the free energy gained from the formation of the microRNA-target duplex and the energetic cost of unpairing the target to make it accessible to the microRNA. This model explains the variability in our experiments, predicts validated targets more accurately than existing algorithms, and shows that genomes accommodate site accessibility by preferentially positioning targets in highly accessible regions. Our study thus demonstrates that target accessibility is a critical factor in microRNA function.Nature Genetics 11/2007; 39(10):1278-84. · 35.21 Impact Factor