Functional microRNA targets in protein coding sequences (original) (raw)
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Chimia, 2014
MicroRNAs form one of the groups of small noncoding RNA molecules that have completely changed our understanding of gene regulatory networks. Because microRNAs have been discovered only relatively recently, most of their functions remain unknown, providing a challenge to both experiment and theory. I review several computational approaches pursued in our group to answer this challenge. In particular, I show that a few rather simple ideas can go a long way in predicting accurately genes regulated by microRNAs via binding sites both in the coding and 3' untranslated regions (3'UTRs). Finally, I mention briefly several applications, including two collaborations with experimental groups, which have shed new light on the latency and reactivation of herpesviruses, and on the maturation of red blood cells.
In-Silico Algorithms for the Screening of Possible microRNA Binding Sites and Their Interactions
Current Genomics, 2013
MicroRNAs (miRNAs) comprise a recently discovered class of small, non-coding RNA molecules of 21-25 nucleotides in length that regulate the gene expression by base-pairing with the transcripts of their targets i.e. proteincoding genes, leading to down-regulation or repression of the target genes. However, target gene activation has also been described. miRNAs are involved in diverse regulatory pathways, including control of developmental timing, apoptosis, cell proliferation, cell differentiation, modulation of immune response to macrophages, and organ development and are associated with many diseases, such as cancer. Computational prediction of miRNA targets is much more challenging in animals than in plants, because animal miRNAs often perform imperfect base-pairing with their target sites, unlike plant miRNAs which almost always bind their targets with near perfect complementarity. In the past years, a large number of target prediction programs and databases on experimentally validated information have been developed for animal miR-NAs to fulfil the need of experimental scientists conducting miRNA research. In this review we first succinctly describe the prediction criteria (rules or principles) adapted by prediction algorithms to generate possible miRNA binding site interactions and introduce most relevant algorithms, and databases. We then summarize their applications with the help of some previously published studies. We further provide experimentally validated functional binding sites outside 3'-UTR region of target mRNAs and the resources which offer such predictions. Finally, the issue of experimental validation of miRNA binding sites will be briefly discussed.
Journal of Biomedical Informatics, 2011
MicroRNAs are small, non-coding RNA molecules that can complementarily bind to the mRNA 3 0 -UTR region to regulate the gene expression by transcriptional repression or induction of mRNA degradation. Increasing evidence suggests a new mechanism by which miRNAs may regulate target gene expression by binding in promoter and amino acid coding regions. Most of the existing databases on miRNAs are restricted to mRNA 3 0 -UTR region. To address this issue, we present miRWalk, a comprehensive database on miRNAs, which hosts predicted as well as validated miRNA binding sites, information on all known genes of human, mouse and rat.
Journal of Biomedical Informatics, 2013
Traditional biology was forced to restate some of its principles when the microRNA (miRNA) genes and their regulatory role were firstly discovered. Typically, miRNAs are small non-coding RNA molecules which have the ability to bind to the 3 0 untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. Existing experimental techniques for their identification and the prediction of the target genes share some important limitations such as low coverage, time consuming experiments and high cost reagents. Hence, many computational methods have been proposed for these tasks to overcome these limitations. Recently, many researchers emphasized on the development of computational approaches to predict the participation of miRNA genes in regulatory networks and to analyze their transcription mechanisms. All these approaches have certain advantages and disadvantages which are going to be described in the present survey. Our work is differentiated from existing review papers by updating the methodologies list and emphasizing on the computational issues that arise from the miRNA data analysis. Furthermore, in the present survey, the various miRNA data analysis steps are treated as an integrated procedure whose aims and scope is to uncover the regulatory role and mechanisms of the miRNA genes. This integrated view of the miRNA data analysis steps may be extremely useful for all researchers even if they work on just a single step.
STarMirDB: a database of microRNA binding sites
RNA biology, 2016
microRNAs (miRNAs) are an abundant class of small endogenous non-coding RNAs (ncRNAs) of ∼22 nucleotides (nts) in length. These small regulatory molecules are involved in diverse developmental, physiological and pathological processes. miRNAs target messenger RNAs (mRNAs) for translational repression and/or mRNA degradation. Predictions of miRNA binding sites facilitate experimental validation of miRNA targets. Models developed with data from CLIP studies have been used for predictions of miRNA binding sites in the whole transcriptomes of human, mouse and worm. The prediction results have been assembled into STarMirDB, a new database of miRNA binding sites available at .http://sfold.wadsworth.org/starmirDB.php STarMirDB can be searched by miRNAs or mRNAs separately or in combination. The search results are categorized into seed and seedless sites in 3' UTR, CDS and 5' UTR. For each predicted site, STarMirDB provides a comprehensive list of sequence, thermodynamic and target ...
Flanking region sequence information to refine microRNA target predictions
Journal of Biosciences, 2010
The non-coding elements of a genome, with many of them considered as junk earlier, have now started gaining long due respectability, with microRNAs as the best current example. MicroRNAs bind preferentially to the 3′ untranslated regions (UTRs) of the target genes and negatively regulate their expression most of the time. Several microRNA:target prediction softwares have been developed based upon various assumptions and the majority of them consider the free energy of binding of a target to its microRNA and seed conservation. However, the average concordance between the predictions made by these softwares is limited and compounded by a large number of false-positive results. In this study, we describe a methodology developed by us to refine microRNA:target prediction by target prediction softwares through observations made from a comprehensive study. We incorporated the information obtained from dinucleotide content variation patterns recorded for flanking regions around the target sites using support vector machines (SVMs) trained over two different major sources of experimental data, besides other sources. We assessed the performance of our methodology with rigorous tests over four different dataset models and also compared it with a recently published refinement tool, MirTif. Our methodology attained a higher average accuracy of 0.88, average sensitivity and specificity of 0.81 and 0.94, respectively, and areas under the curves (AUCs) for all the four models scored above 0.9, suggesting better performance by our methodology and a possible role of flanking regions in microRNA targeting control. We used our methodology over genes of three different pathways — toll-like receptor (TLR), apoptosis and insulin — to finally predict the most probable targets. We also investigated their possible regulatory associations, and identified a hsa-miR-23a regulatory module.