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      DIANA-miRGen v3.0: accurate characterization of microRNA promoters and their regulators

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          Abstract

          microRNAs (miRNAs) are small non-coding RNAs that actively fine-tune gene expression. The accurate characterization of the mechanisms underlying miRNA transcription regulation will further expand our knowledge regarding their implication in homeostatic and pathobiological networks. Aim of DIANA-miRGen v3.0 ( http://www.microrna.gr/mirgen) is to provide for the first time accurate cell-line-specific miRNA gene transcription start sites (TSSs), coupled with genome-wide maps of transcription factor (TF) binding sites in order to unveil the mechanisms of miRNA transcription regulation. To this end, more than 7.3 billion RNA-, ChIP- and DNase-Seq next generation sequencing reads were analyzed/assembled and combined with state-of-the-art miRNA TSS prediction and TF binding site identification algorithms. The new database schema and web interface facilitates user interaction, provides advanced queries and innate connection with other DIANA resources for miRNA target identification and pathway analysis. The database currently supports 276 miRNA TSSs that correspond to 428 precursors and >19M binding sites of 202 TFs on a genome-wide scale in nine cell-lines and six tissues of Homo sapiens and Mus musculus.

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          Most cited references23

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          miRecords: an integrated resource for microRNA–target interactions

          MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords.
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            TRANSFAC: transcriptional regulation, from patterns to profiles.

            The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER database on anatomical structures and developmental stages. The functionality of Match, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with Match Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER free download versions are available at http://www.biobase.de:8080/index.html.
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              Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells.

              MicroRNAs (miRNAs) are crucial for normal embryonic stem (ES) cell self-renewal and cellular differentiation, but how miRNA gene expression is controlled by the key transcriptional regulators of ES cells has not been established. We describe here the transcriptional regulatory circuitry of ES cells that incorporates protein-coding and miRNA genes based on high-resolution ChIP-seq data, systematic identification of miRNA promoters, and quantitative sequencing of short transcripts in multiple cell types. We find that the key ES cell transcription factors are associated with promoters for miRNAs that are preferentially expressed in ES cells and with promoters for a set of silent miRNA genes. This silent set of miRNA genes is co-occupied by Polycomb group proteins in ES cells and shows tissue-specific expression in differentiated cells. These data reveal how key ES cell transcription factors promote the ES cell miRNA expression program and integrate miRNAs into the regulatory circuitry controlling ES cell identity.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                04 January 2016
                19 November 2015
                19 November 2015
                : 44
                : Database issue , Database issue
                : D190-D195
                Affiliations
                [1 ]DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, 382 21 Volos, Greece
                [2 ]Hellenic Pasteur Institute, 115 21 Athens, Greece
                [3 ]Laboratory for Experimental Surgery and Surgical Research ‘N.S. Christeas’, Medical School of Athens, University of Athens, 11527 Athens, Greece
                [4 ]‘Athena’ Research and Innovation Center, 11524 Athens, Greece
                [5 ]University of Peloponnese, Department of Informatics and Telecommunications, 22100 Tripoli, Greece
                [6 ]School of Electrical and Computer Engineering, NTUA, 15773 Zografou, Greece
                [7 ]Greek Research and Technology Network (GRNET), Athens 11527, Greece
                [8 ]Department of Electrical & Computer Engineering, University of Thessaly, 382 21 Volos, Greece
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +30 24210 74758; Fax: +30 24210 74997; Email: arhatzig@ 123456inf.uth.gr
                Correspondence may also be addressed to Artemis G. Hatzigeorgiou. Tel: +30 24210 74758; Fax: +30 24210 74997; Email: georgakilas@ 123456inf.uth.gr
                []These authors contributed equally to the paper as first authors.
                Article
                10.1093/nar/gkv1254
                4702888
                26586797
                edea6edc-afe1-469f-89f1-9a8865931df9
                © The Author(s) 2015. 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
                : 02 November 2015
                : 01 November 2015
                : 30 September 2015
                Page count
                Pages: 6
                Categories
                Database Issue
                Custom metadata
                04 January 2016

                Genetics
                Genetics

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