Bayesian inference of MicroRNA targets from sequence and expression data.

MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation.     

Human microRNA target identification by RRSM

MicroRNAs (miRNAs) are small endogenously expressed non-coding RNAs that regulate target messenger RNAs in various biological processes. In recent years, there have been many studies concentrated on the discovery of new miRNAs and identification of their mRNA targets. Although researchers have identified many miRNAs, few miRNA targets have been identified by actual experimental methods. To expedite the identification of miRNA targets for experimental verification, in the literature approaches based on the sequence or microarray expression analysis have been established to discover the potential miRNA targets. In this study, we focus on the human miRNA target prediction and propose a generalized relative R2 method (RRSM) to find many high-confidence targets. Many targets have been confirmed from previous studies. The targets for several miRNAs discovered by the HITS-CLIP method in a recent study have also been selected by our study.     

miRTar Hunter: A prediction system for identifying human microRNA target sites

MicroRNAs (miRNAs) are important regulators of gene expression and play crucial roles in many biological processes including apoptosis, differentiation, development, and tumorigenesis. Recent estimates suggest that more than 50% of human protein coding genes may be regulated by miRNAs and that each miRNA may bind to 300–400 target genes. Approximately 1,000 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs. However, the targets for a majority of these miRNAs have not been identified due to the lack of large-scale experimental detection techniques. Experimental detection of miRNA target sites is a costly and time-consuming process, even though identification of miRNA targets is critical to unraveling their functions in various biological processes. To identify miRNA targets, we developed miRTar Hunter, a novel computational approach for predicting target sites regardless of the presence or absence of a seed match or evolutionary sequence conservation. Our approach is based on a dynamic programming algorithm that incorporates more sequence-specific features and reflects the properties of various types of target sites that determine diverse aspects of complementarities between miRNAs and their targets. We evaluated the performance of our algorithm on 532 known human miRNA:target pairs and 59 experimentally-verified negative miRNA:target pairs, and also compared our method with three popular programs for 481 miRNA:target pairs. miRTar Hunter outperformed three popular existing algorithms in terms of recall and precision, indicating that our unique scheme to quantify the determinants of complementary sites is effective at detecting miRNA targets. miRTar Hunter is now available at http://203.230.194.162/~kbkim.     

Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos

MicroRNAs (miRNAs) are non-coding small RNAs of ~22 nt that regulate the gene expression by base pairing with target mRNAs, leading to mRNA cleavage or translational repression. It is currently estimated that miRNAs account for ~1% of predicted genes in higher eukaryotic genomes and that up to 30% of genes might be regulated by miRNAs. However, only very few miRNAs have been functionally characterized and the general functions of miRNAs are not globally studied. In this study, we systematically analyzed the expression patterns of miRNA targets using several public microarray profiles. We found that the expression levels of miRNA targets are lower in all mouse and Drosophila tissues than in the embryos. We also found miRNAs more preferentially target ubiquitously expressed genes than tissue-specifically expressed genes. These results support the current suggestion that miRNAs are likely to be largely involved in embryo development and maintaining of tissue identity.     

Tissue and Process Specific microRNA–mRNA Co-Expression in Mammalian Development and Malignancy

An association between enrichment and depletion of microRNA (miRNA) binding sites, 3′ UTR length, and mRNA expression has been demonstrated in various developing tissues and tissues from different mature organs; but functional, context-dependent miRNA regulations have yet to be elucidated. Towards that goal, we examined miRNA–mRNA interactions by measuring miRNA and mRNA in the same tissue during development and also in malignant conditions. We identified significant miRNA-mediated biological process categories in developing mouse cerebellum and lung using non-targeted mRNA expression as the negative control. Although miRNAs in general suppress target mRNA messages, many predicted miRNA targets demonstrate a significantly higher level of co-expression than non-target genes in developing cerebellum. This phenomenon is tissue specific since it is not observed in developing lungs. Comparison of mouse cerebellar development and medulloblastoma demonstrates a shared miRNA–mRNA co-expression program for brain-specific neurologic processes such as synaptic transmission and exocytosis, in which miRNA target expression increases with the accumulation of multiple miRNAs in developing cerebellum and decreases with the loss of these miRNAs in brain tumors. These findings demonstrate the context-dependence of miRNA–mRNA co-expression.     

Experimental validation of miRNA targets

MicroRNAs are natural, single-stranded, small RNA molecules that regulate gene expression by binding to target mRNAs and suppress its translation or initiate its degradation. In contrast to the identification and validation of many miRNA genes is the lack of experimental evidence identifying their corresponding mRNA targets. The most fundamental challenge in miRNA biology is to define the rules of miRNA target recognition. This is critical since the biological role of individual miRNAs will be dictated by the mRNAs that they regulate. Therefore, only as target mRNAs are validated will it be possible to establish commonalities that will enable more precise predictions of miRNA/mRNA interactions. Currently there is no clear agreement as to what experimental procedures should be followed to demonstrate that a given mRNA is a target of a specific miRNA. Therefore, this review outlines several methods by which to validate miRNA targets. Additionally, we propose that multiple criteria should be met before miRNA target validation should be considered "confirmed."     

The microRNAs in an Ancient Protist Repress the Variant-Specific Surface Protein Expression by Targeting the Entire Coding Sequence

microRNAs (miRNA) have been detected in the deeply branched protist, Giardia lamblia, and shown to repress expression of the family of variant-specific surface proteins (VSPs), only one of which is expressed in Giardia trophozoite at a given time. Three next-generation sequencing libraries of Giardia Argonaute-associated small RNAs were constructed and analyzed. Analysis of the libraries identified a total of 99 new putative miRNAs with a size primarily in the 26 nt range similar to the size previously predicted by the Giardia Dicer crystal structure and identified by our own studies. Bioinformatic analysis identified multiple putative miRNA target sites in the mRNAs of all 73 VSPs. The effect of miRNA target sites within a defined 3′-region were tested on two vsp mRNAs. All the miRNAs showed partial repression of the corresponding vsp expression and were additive when the targeting sites were separately located. But the combined repression still falls short of 100%. Two other relatively short vsp mRNAs with 15 and 11 putative miRNA target sites identified throughout their ORFs were tested with their corresponding miRNAs. The results indicate that; (1) near 100% repression of vsp mRNA expression can be achieved through the combined action of multiple miRNAs on target sites located throughout the ORF; (2) the miRNA machinery could be instrumental in repressing the expression of vsp genes in Giardia; (3) this is the first time that all the miRNA target sites in the entire ORF of a mRNA have been tested and shown to be functional.