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      A novel two-layer SVM model in miRNA Drosha processing site detection

      research-article
      1 , 2 , 1 ,
      BMC Systems Biology
      BioMed Central
      IEEE International Conference on Bioinformatics and Biomedicine 2012
      4-7 October 2012

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          Abstract

          Background

          MicroRNAs (miRNAs) are a large class of non-coding RNAs with important functions wide spread in animals, plants and viruses. Studies showed that an RNase III family member called Drosha recognizes most miRNAs, initiates their processing and determines the mature miRNAs. The Drosha processing sites identification will shed some light on both miRNA identification and understanding the mechanism of Drosha processing.

          Methods

          We developed a computational method for Drosha processing site predicting, named as DroshaPSP, which employs a two-layer mathematical model to integrate structure feature in the first layer and sequence features in the second layer. The performance of DroshaPSP was estimated by 5-fold cross-validation and measured by ACC (accuracy), Sn (sensitivity), Sp (specificity), P (precision) and MCC (Matthews correlation coefficient).

          Results

          The results of testing DroshaPSP on the miRNA data of Drosophila melanogaster indicated that the Sn, Sp, and MCC thereof reach to 0.86, 0.99 and 0.86 respectively.

          Conclusions

          We found the Shannon entropy, a chemical kinetics feature, is a significant feature in telling the true sites among the nearby sites and improving the performance.

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          Most cited references12

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          Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.

          MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression in plants and animals. To investigate the influence of miRNAs on transcript levels, we transfected miRNAs into human cells and used microarrays to examine changes in the messenger RNA profile. Here we show that delivering miR-124 causes the expression profile to shift towards that of brain, the organ in which miR-124 is preferentially expressed, whereas delivering miR-1 shifts the profile towards that of muscle, where miR-1 is preferentially expressed. In each case, about 100 messages were downregulated after 12 h. The 3' untranslated regions of these messages had a significant propensity to pair to the 5' region of the miRNA, as expected if many of these messages are the direct targets of the miRNAs. Our results suggest that metazoan miRNAs can reduce the levels of many of their target transcripts, not just the amount of protein deriving from these transcripts. Moreover, miR-1 and miR-124, and presumably other tissue-specific miRNAs, seem to downregulate a far greater number of targets than previously appreciated, thereby helping to define tissue-specific gene expression in humans.
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            Switching from repression to activation: microRNAs can up-regulate translation.

            AU-rich elements (AREs) and microRNA target sites are conserved sequences in messenger RNA (mRNA) 3' untranslated regions (3'UTRs) that control gene expression posttranscriptionally. Upon cell cycle arrest, the ARE in tumor necrosis factor-alpha (TNFalpha) mRNA is transformed into a translation activation signal, recruiting Argonaute (AGO) and fragile X mental retardation-related protein 1 (FXR1), factors associated with micro-ribonucleoproteins (microRNPs). We show that human microRNA miR369-3 directs association of these proteins with the AREs to activate translation. Furthermore, we document that two well-studied microRNAs-Let-7 and the synthetic microRNA miRcxcr4-likewise induce translation up-regulation of target mRNAs on cell cycle arrest, yet they repress translation in proliferating cells. Thus, activation is a common function of microRNPs on cell cycle arrest. We propose that translation regulation by microRNPs oscillates between repression and activation during the cell cycle.
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              Fast folding and comparison of RNA secondary structures

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                Author and article information

                Contributors
                Conference
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central
                1752-0509
                2013
                23 October 2013
                : 7
                : Suppl 4
                : S4
                Affiliations
                [1 ]Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
                [2 ]School of Plant Sciences, University of Arizona, Tucson, AZ, USA
                Article
                1752-0509-7-S4-S4
                10.1186/1752-0509-7-S4-S4
                3854652
                0e4023ca-5ab9-42aa-8444-c655ae3b9263
                Copyright © 2013 Hu et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                IEEE International Conference on Bioinformatics and Biomedicine 2012
                Philadelphia, PA, USA
                4-7 October 2012
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                Research

                Quantitative & Systems biology
                Quantitative & Systems biology

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