373
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      An integrated system CisGenome for analyzing ChIP-chip and ChIP-seq data

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          CisGenome is a software system for analyzing genome-wide chromatin immunoprecipitation (ChIP) data. It is designed to meet all basic needs of ChIP data analyses, including visualization, data normalization, peak detection, false discovery rate (FDR) computation, gene-peak association, and sequence and motif analysis. In addition to implementing previously published ChIP-chip analysis methods, the software contains new statistical methods designed specifically for ChIP-seq data. CisGenome has a modular design so that it supports interactive analyses through a graphic user interface as well as customized batch-mode computation for advanced data mining. A built-in browser allows visualization of array images, signals, gene structure, conservation, and DNA sequence and motif information. We illustrate the use of these tools by a comparative analysis of ChIP-chip and ChIP-seq data for the transcription factor NRSF/REST, a study of ChIP-seq analysis without negative control sample, and an analysis of a novel motif in Nanog- and Sox2-binding regions.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Genome-wide analysis of estrogen receptor binding sites.

            The estrogen receptor is the master transcriptional regulator of breast cancer phenotype and the archetype of a molecular therapeutic target. We mapped all estrogen receptor and RNA polymerase II binding sites on a genome-wide scale, identifying the authentic cis binding sites and target genes, in breast cancer cells. Combining this unique resource with gene expression data demonstrates distinct temporal mechanisms of estrogen-mediated gene regulation, particularly in the case of estrogen-suppressed genes. Furthermore, this resource has allowed the identification of cis-regulatory sites in previously unexplored regions of the genome and the cooperating transcription factors underlying estrogen signaling in breast cancer.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Database resources of the National Center for Biotechnology Information

              In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data available through NCBI's web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link, Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace, Assembly, and Short Read Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus, Entrez Probe, GENSAT, Database of Genotype and Phenotype, Online Mendelian Inheritance in Man, Online Mendelian Inheritance in Animals, the Molecular Modeling Database, the Conserved Domain Database, the Conserved Domain Architecture Retrieval Tool and the PubChem suite of small molecule databases. Augmenting the web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.
                Bookmark

                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nature biotechnology
                1087-0156
                1546-1696
                6 October 2008
                2 November 2008
                November 2008
                1 May 2009
                : 26
                : 11
                : 1293-1300
                Affiliations
                [1 ]Department of Biostatistics Johns Hopkins Bloomberg School of Public Health 615 North Wolfe Street Baltimore, MD 21205, USA
                [2 ]Institute for Computational and Mathematical Engineering Stanford University Durand Building, 496 Lomita Mall Stanford, CA 94305, USA
                [3 ]Department of Computer Science Stanford University 353 Serra Mall Stanford, CA 94305, USA
                [4 ]Department of Genetics Stanford University School of Medicine 300 Pasteur Drive Stanford, CA 94305, USA
                [5 ]HudsonAlpha Institute for Biotechnology 601 Genome Way Huntsville, AL 35806, USA
                [6 ]Department of Statistics Stanford University Sequoia Hall, 390 Serra Mall Stanford, CA 94305, USA
                [7 ]Department of Health Research and Policy Stanford University Sequoia Hall, 390 Serra Mall Stanford, CA 94305, USA
                Author notes

                Current Address: Gene Security Network, Inc. 1442 Cortland Avenue San Francisco, CA 94110, USA

                [*]

                To whom correspondence should be addressed. Email: whwong@ 123456stanford.edu .

                AUTHOR CONTRIBUTIONS

                H.K.J., conceived the study, developed CisGenome GUI and data analysis algorithms, performed data analyses and drafted the manuscript. H.J., developed CisGenome browser. W.X.M., participated in algorithm development and performed data analyses. D.S.J. and R.M.M., generated NRSF ChIP-chip data. W.H.W., conceived the study and drafted the manuscript. All authors read and revised the manuscript.

                Article
                nihpa72493
                10.1038/nbt.1505
                2596672
                18978777
                0206beb4-ffa5-4c12-8624-8062861d66f6
                History
                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG003903-03 ||HG
                Categories
                Article

                Biotechnology
                Biotechnology

                Comments

                Comment on this article