53
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes

      product-review

      Read this article at

      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

          Background

          The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size, resulting in large numbers of false positives and false negatives in current approaches. Computational methods to reduce false positives are to look for over-representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments.

          Results

          We have developed a novel tool, "CORE_TF" (Conserved and Over-REpresented Transcription Factor binding sites) that identifies common transcription factor binding sites in promoters of co-regulated genes. To improve upon existing binding site predictions, the tool searches for position weight matrices from the TRANSFAC R database that are over-represented in an experimental set compared to a random set of promoters and identifies cross-species conservation of the predicted transcription factor binding sites. The algorithm has been evaluated with expression and chromatin-immunoprecipitation on microarray data. We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content, which is a unique feature of our tool.

          Conclusion

          The program CORE_TF is accessible in a user friendly web interface at http://www.LGTC.nl/CORE_TF. It provides a table of over-represented transcription factor binding sites in the users input genes' promoters and a graphical view of evolutionary conserved transcription factor binding sites. In our test data sets it successfully predicts target transcription factors and their binding sites.

          Related collections

          Most cited references23

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

          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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

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

              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.
                Bookmark

                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                26 November 2008
                : 9
                : 495
                Affiliations
                [1 ]The Center for Human and Clinical Genetics, Leiden University Medical Center, Postzone S4-0P, PO Box 9600, 2300 RC Leiden, The Netherlands
                Article
                1471-2105-9-495
                10.1186/1471-2105-9-495
                2613159
                19036135
                d471631c-4884-4d6c-95ef-cb101f0e29e5
                Copyright © 2008 Hestand 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.

                History
                : 13 March 2008
                : 26 November 2008
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article