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      mirDIP 4.1—integrative database of human microRNA target predictions

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          Abstract

          MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA–target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA–target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA–target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

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          Most cited references77

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          miRDB: an online resource for microRNA target prediction and functional annotations

          MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes regulated by miRNAs. To this end, we have developed an online resource, miRDB (http://mirdb.org), for miRNA target prediction and functional annotations. Here, we describe recently updated features of miRDB, including 2.1 million predicted gene targets regulated by 6709 miRNAs. In addition to presenting precompiled prediction data, a new feature is the web server interface that allows submission of user-provided sequences for miRNA target prediction. In this way, users have the flexibility to study any custom miRNAs or target genes of interest. Another major update of miRDB is related to functional miRNA annotations. Although thousands of miRNAs have been identified, many of the reported miRNAs are not likely to play active functional roles or may even have been falsely identified as miRNAs from high-throughput studies. To address this issue, we have performed combined computational analyses and literature mining, and identified 568 and 452 functional miRNAs in humans and mice, respectively. These miRNAs, as well as associated functional annotations, are presented in the FuncMir Collection in miRDB.
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            Gene silencing by microRNAs: contributions of translational repression and mRNA decay.

            Despite their widespread roles as regulators of gene expression, important questions remain about target regulation by microRNAs. Animal microRNAs were originally thought to repress target translation, with little or no influence on mRNA abundance, whereas the reverse was thought to be true in plants. Now, however, it is clear that microRNAs can induce mRNA degradation in animals and, conversely, translational repression in plants. Recent studies have made important advances in elucidating the relative contributions of these two different modes of target regulation by microRNAs. They have also shed light on the specific mechanisms of target silencing, which, although it differs fundamentally between plants and animals, shares some common features between the two kingdoms.
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              Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

              mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                04 January 2018
                29 November 2017
                29 November 2017
                : 46
                : Database issue , Database issue
                : D360-D370
                Affiliations
                Krembil Research Institute, University Health Network, Toronto, Ontario M5T 2S8, Canada
                Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
                Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada
                Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, 845 10, Slovakia
                Author notes
                To whom correspondence should be addressed. Tel: +1 416 581 7437; Fax: +1 416 603 6274; Email: juris@ 123456ai.utoronto.ca
                Article
                gkx1144
                10.1093/nar/gkx1144
                5753284
                29194489
                96e7aa43-6694-4ee7-ba90-b869889038d2
                © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 30 October 2017
                : 27 October 2017
                : 17 September 2017
                Page count
                Pages: 11
                Categories
                Database Issue

                Genetics
                Genetics

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