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      ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor

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          Abstract

          Chromatin immunoprecipitation (ChIP) coupled with high-throughput techniques (ChIP-X), such as next generation sequencing (ChIP-Seq) and microarray (ChIP–chip), has been successfully used to map active transcription factor binding sites (TFBS) of a transcription factor (TF). The targeted genes can be activated or suppressed by the TF, or are unresponsive to the TF. Microarray technology has been used to measure the actual expression changes of thousands of genes under the perturbation of a TF, but is unable to determine if the affected genes are direct or indirect targets of the TF. Furthermore, both ChIP-X and microarray methods produce a large number of false positives. Combining microarray expression profiling and ChIP-X data allows more effective TFBS analysis for studying the function of a TF. However, current web servers only provide tools to analyze either ChIP-X or expression data, but not both. Here, we present ChIP-Array, a web server that integrates ChIP-X and expression data from human, mouse, yeast, fruit fly and Arabidopsis. This server will assist biologists to detect direct and indirect target genes regulated by a TF of interest and to aid in the functional characterization of the TF. ChIP-Array is available at http://jjwanglab.hku.hk/ChIP-Array, with free access to academic users.

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          Most cited references17

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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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            JASPAR: an open-access database for eukaryotic transcription factor binding profiles.

            The analysis of regulatory regions in genome sequences is strongly based on the detection of potential transcription factor binding sites. The preferred models for representation of transcription factor binding specificity have been termed position-specific scoring matrices. JASPAR is an open-access database of annotated, high-quality, matrix-based transcription factor binding site profiles for multicellular eukaryotes. The profiles were derived exclusively from sets of nucleotide sequences experimentally demonstrated to bind transcription factors. The database is complemented by a web interface for browsing, searching and subset selection, an online sequence analysis utility and a suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. JASPAR is available at http://jaspar. cgb.ki.se.
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              An improved map of conserved regulatory sites for Saccharomyces cerevisiae

              Background The regulatory map of a genome consists of the binding sites for proteins that determine the transcription of nearby genes. An initial regulatory map for S. cerevisiae was recently published using six motif discovery programs to analyze genome-wide chromatin immunoprecipitation data for 203 transcription factors. The programs were used to identify sequence motifs that were likely to correspond to the DNA-binding specificity of the immunoprecipitated proteins. We report improved versions of two conservation-based motif discovery algorithms, PhyloCon and Converge. Using these programs, we create a refined regulatory map for S. cerevisiae by reanalyzing the same chromatin immunoprecipitation data. Results Applying the same conservative criteria that were applied in the original study, we find that PhyloCon and Converge each separately discover more known specificities than the combination of all six programs in the previous study. Combining the results of PhyloCon and Converge, we discover significant sequence motifs for 36 transcription factors that were previously missed. The new set of motifs identifies 636 more regulatory interactions than the previous one. The new network contains 28% more regulatory interactions among transcription factors, evidence of greater cross-talk between regulators. Conclusion Combining two complementary computational strategies for conservation-based motif discovery improves the ability to identify the specificity of transcriptional regulators from genome-wide chromatin immunoprecipitation data. The increased sensitivity of these methods significantly expands the map of yeast regulatory sites without the need to alter any of the thresholds for statistical significance. The new map of regulatory sites reveals a more elaborate and complex view of the yeast genetic regulatory network than was observed previously.
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                1 July 2011
                1 July 2011
                17 May 2011
                17 May 2011
                : 39
                : Web Server issue , Web Server issue
                : W430-W436
                Affiliations
                1Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, 21 Sassoon Road, Hong Kong SAR, China, 2Bioinformatics Division, TNLIST, Tsinghua University, Beijing 100084, China and 3Department of Molecular and Cell Biology, Center for Systems Biology, The University of Texas at Dallas, Dallas, TX 75080, USA
                Author notes
                *To whom correspondence should be addressed. Tel: +852 2819 2809; Fax: +852 2855 1254; Email: junwen@ 123456uw.edu
                Article
                gkr332
                10.1093/nar/gkr332
                3125757
                21586587
                25434a33-e3a0-416e-a466-db644f670593
                © The Author(s) 2011. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 February 2011
                : 15 April 2011
                : 24 April 2011
                Page count
                Pages: 7
                Categories
                Articles

                Genetics
                Genetics

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