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      A statistically inferred microRNA network identifies breast cancer target miR-940 as an actin cytoskeleton regulator

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          Abstract

          MiRNAs are key regulators of gene expression. By binding to many genes, they create a complex network of gene co-regulation. Here, using a network-based approach, we identified miRNA hub groups by their close connections and common targets. In one cluster containing three miRNAs, miR-612, miR-661 and miR-940, the annotated functions of the co-regulated genes suggested a role in small GTPase signalling. Although the three members of this cluster targeted the same subset of predicted genes, we showed that their overexpression impacted cell fates differently. miR-661 demonstrated enhanced phosphorylation of myosin II and an increase in cell invasion, indicating a possible oncogenic miRNA. On the contrary, miR-612 and miR-940 inhibit phosphorylation of myosin II and cell invasion. Finally, expression profiling in human breast tissues showed that miR-940 was consistently downregulated in breast cancer tissues

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          Most cited references49

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              NIH Image to ImageJ: 25 years of image analysis.

              For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                12 February 2015
                2015
                : 5
                : 8336
                Affiliations
                [1 ]Univ. Grenoble Alpes , iRTSV-BGE, F-38000 Grenoble, France
                [2 ]CEA , iRTSV-BGE, F-38000 Grenoble, France
                [3 ]INSERM , BGE, F-38000 Grenoble, France
                [4 ]Center for Computational Biology - CBIO , Mines ParisTech, F-77300 Fontainebleau, France
                [5 ]Institut Curie , F-75248 Paris, France
                [6 ]INSERM , U900, F-75248 Paris, France
                Author notes
                [*]

                These two authors equally contributed to the work.

                Article
                srep08336
                10.1038/srep08336
                5389139
                25673565
                89e6bf40-5d1f-4908-80ea-0321f2e975a5
                Copyright © 2015, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 08 August 2014
                : 14 January 2015
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