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      Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues

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          Abstract

          Tissue-specific miRNAs (TS miRNA) specifically expressed in particular tissues play an important role in tissue identity, differentiation and function. However, transcription factor (TF) and TS miRNA regulatory networks across multiple tissues have not been systematically studied. Here, we manually extracted 116 TS miRNAs and systematically investigated the regulatory network of TF-TS miRNA in 12 human tissues. We identified 2,347 TF-TS miRNA regulatory relations and revealed that most TF binding sites tend to enrich close to the transcription start site of TS miRNAs. Furthermore, we found TS miRNAs were regulated widely by non-tissue specific TFs and the tissue-specific expression level of TF have a close relationship with TF-genes regulation. Finally, we describe TSmiR ( http://bioeng.swjtu.edu.cn/TSmiR), a novel and web-searchable database that houses interaction maps of TF-TS miRNA in 12 tissues. Taken together, these observations provide a new suggestion to better understand the regulatory network and mechanisms of TF-TS miRNAs underlying different tissues.

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          Most cited references35

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          Transcriptional regulatory networks in Saccharomyces cerevisiae.

          We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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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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              Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes.

              MicroRNAs (miRNAs) are short endogenous RNAs known to post-transcriptionally repress gene expression in animals and plants. A microarray profiling survey revealed the expression patterns of 175 human miRNAs across 24 different human organs. Our results show that proximal pairs of miRNAs are generally coexpressed. In addition, an abrupt transition in the correlation between pairs of expressed miRNAs occurs at a distance of 50 kb, implying that miRNAs separated by <50 kb typically derive from a common transcript. Some microRNAs are within the introns of host genes. Intronic miRNAs are usually coordinately expressed with their host gene mRNA, implying that they also generally derive from a common transcript, and that in situ analyses of host gene expression can be used to probe the spatial and temporal localization of intronic miRNAs.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                03 June 2014
                2014
                : 4
                : 5150
                Affiliations
                [1 ]School of Life Sciences and Bioengineering, Southwest Jiaotong University , Chengdu, 610031, P.R. China
                [2 ]Department of Biology, Lakehead University , Oliver Road, Thunder Bay, Ontario
                Author notes
                Article
                srep05150
                10.1038/srep05150
                5381490
                24889152
                bce9e4ee-2891-4f15-87a6-5bb8dcb30b3e
                Copyright © 2014, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution 3.0 Unported License. The images in this article are included in the article's Creative Commons license, unless indicated otherwise in the image credit; if the image is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the image. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/

                History
                : 29 January 2014
                : 08 May 2014
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