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CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse

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      Abstract

      BackgroundTranscription Factors (TFs) and microRNAs (miRNAs) are key players for gene expression regulation in higher eukaryotes. In the last years, a large amount of bioinformatic studies were devoted to the elucidation of transcriptional and post-transcriptional (mostly miRNA-mediated) regulatory interactions, but little is known about the interplay between them.DescriptionHere we describe a dynamic web-accessible database, CircuitsDB, supporting a genome-wide transcriptional and post-transcriptional regulatory network integration, for the human and mouse genomes, based on a bioinformatic sequence-analysis approach. In particular, CircuitsDB is currently focused on the study of mixed miRNA/TF Feed-Forward regulatory Loops (FFLs), i.e. elementary circuits in which a master TF regulates an miRNA and together with it a set of Joint Target protein-coding genes. The database was constructed using an ab-initio oligo analysis procedure for the identification of the transcriptional and post-transcriptional interactions. Several external sources of information were then pooled together to obtain the functional annotation of the proposed interactions. Results for human and mouse genomes are presented in an integrated web tool, that allows users to explore the circuits, investigate their sequence and functional properties and thus suggest possible biological experiments.ConclusionsWe present CircuitsDB, a web-server devoted to the study of human and mouse mixed miRNA/TF Feed-Forward regulatory circuits, freely available at: http://biocluster.di.unito.it/circuits/

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      Most cited references 50

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      Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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        MicroRNAs: target recognition and regulatory functions.

         David Bartel (2009)
        MicroRNAs (miRNAs) are endogenous approximately 23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.
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          Most mammalian mRNAs are conserved targets of microRNAs.

          MicroRNAs (miRNAs) are small endogenous RNAs that pair to sites in mRNAs to direct post-transcriptional repression. Many sites that match the miRNA seed (nucleotides 2-7), particularly those in 3' untranslated regions (3'UTRs), are preferentially conserved. Here, we overhauled our tool for finding preferential conservation of sequence motifs and applied it to the analysis of human 3'UTRs, increasing by nearly threefold the detected number of preferentially conserved miRNA target sites. The new tool more efficiently incorporates new genomes and more completely controls for background conservation by accounting for mutational biases, dinucleotide conservation rates, and the conservation rates of individual UTRs. The improved background model enabled preferential conservation of a new site type, the "offset 6mer," to be detected. In total, >45,000 miRNA target sites within human 3'UTRs are conserved above background levels, and >60% of human protein-coding genes have been under selective pressure to maintain pairing to miRNAs. Mammalian-specific miRNAs have far fewer conserved targets than do the more broadly conserved miRNAs, even when considering only more recently emerged targets. Although pairing to the 3' end of miRNAs can compensate for seed mismatches, this class of sites constitutes less than 2% of all preferentially conserved sites detected. The new tool enables statistically powerful analysis of individual miRNA target sites, with the probability of preferentially conserved targeting (P(CT)) correlating with experimental measurements of repression. Our expanded set of target predictions (including conserved 3'-compensatory sites), are available at the TargetScan website, which displays the P(CT) for each site and each predicted target.
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            Author and article information

            Affiliations
            [1 ]Center for Molecular Systems Biology, University of Torino, Via Accademia Albertina, 13 - I-10123 Torino, Italy
            [2 ]CIBIO-Centre for Integrative Biology, University of Trento, Via delle Regole 101, I-38100 Trento, Italy
            [3 ]Department of Oncological Sciences, c/o Institute for Cancer Research and Treatment (IRCC), School of Medicine, University of Torino, Str. Prov. 142 Km. 3.95, I-10060 Candiolo, Italy
            [4 ]Molecular Biotechnology Center, University of Torino, Via Nizza 52, I-10126 Torino, Italy
            [5 ]Systems Biology Lab, Institute for Cancer Research and Treatment (IRCC), School of Medicine, University of Torino, Str. Prov. 142 Km. 3.95, I-10060 Candiolo, Italy
            Contributors
            Journal
            BMC Bioinformatics
            BMC Bioinformatics
            BioMed Central
            1471-2105
            2010
            23 August 2010
            : 11
            : 435
            2936401
            1471-2105-11-435
            20731828
            10.1186/1471-2105-11-435
            Copyright ©2010 Friard 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.

            Categories
            Database

            Bioinformatics & Computational biology

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