Nucleic Acids Research, 2001, Vol. 29, No. 1 281-283
© 2001 Oxford University Press
The TRANSFAC system on gene expression regulation
1Gesellschaft für Biotechnologische Forschung mbH, Mascheroder Weg 1, D-38124 Braunschweig, Germany, 2The National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, People’s Republic of China, 3BIOBASE GmbH, Mascheroder Weg 1B, D-38124 Braunschweig, Germany, and 4Technische Universität Braunschweig, Institut für Genetik–Biozentrum, Spielmannstraße 7, D-38106 Braunschweig, Germany
Received September 28, 2000; Accepted October 12, 2000.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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The TRANSFAC database on transcription factors and their DNA-binding sites and profiles (http://www.gene-regulation.de/) has been quantitatively extended and supplemented by a number of modules. These modules give information about pathologically relevant mutations in regulatory regions and transcription factor genes (PathoDB), scaffold/matrix attached regions (S/MARt DB), signal transduction (TRANSPATH) and gene expression sources (CYTOMER). Altogether, these distinct database modules constitute the TRANSFAC system. They are accompanied by a number of program routines for identifying potential transcription factor binding sites or for localizing individual components in the regulatory network of a cell.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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Transcriptional control in eukaryotes is a highly complex subject and, to our present knowledge, is the major step on which the regulation of gene expression is governed. The increasing body of information makes it necessary to provide systematic access to the data about all factors and parameters that influence this process for each individual gene. Starting with our first compilation (1), we provide relevant data about eukaryotic transcription factors, and their genomic binding sites in our TRANSFAC database (2). The database was continuously extended by adding more data sets and by expanding the type of information provided (3–6). The latest step was to complement the TRANSFAC database by additional modules making up now the ‘TRANSFAC system’ (7). In this contribution, we shall describe the present status of the individual databases of the system as well as the progress of their integration.
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THE TRANSFAC DATABASE |
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TOPABSTRACTINTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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Content of TRANSFAC
From the internal relational database system of the TRANSFAC database, which comprises 80 tables, flat file releases are produced where the information is condensed to six files. By far the largest are SITE and FACTOR. They give information about individual transcription factor binding sites in eukaryotic genes and about transcription factors, respectively. Both of these files increased in volume during the last year to different extents (7 and 27%, respectively). The other four files (GENE, CELL, CLASS and MATRIX) provide additional information. Among them, MATRIX is of particular importance since its contents provide the basis for sequence scanning tools such as MatInspector (see below). It represents DNA binding profiles for individual or groups of transcription factors and, therefore, is linked to the FACTOR table.
The total number of entries in the individual tables is given in Table 1. In that table, we also list the distribution of FACTOR entries amongst different species groups. We made particular efforts to update pax (91 entries), homeo domain (457 entries), and forkhead/winged helix factors (92 entries) as well as bHLH-ZIP factors (such as those of the Myc-family; 107 entries). Moreover, yeast transcription factors have been updated and add up to 317 entries now (increase of 66%), and the realm of plant transcription factors has been extended further by 84% to 489 entries.
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Quality management
It is essential to standardize the process of data annotation to ensure the consistency and quality of the extracted data. Criteria for the quality of database entries are the absence of errors and redundancy, the completeness, unambigousness and high integration with other data sources and the commentary on existing discrepancies in the literature.
We set up a repository for all quality-relevant information, which comprises the internal database documentation, conventions, syntax rules and format statements. Each step of the annotation process is linked to informatory documents, which set the standards for the implementation of data into TRANSFAC. The checklists contain information for every single field of the entries. The whole internal documentation is being reviewed and updated continuously.
The internal documentation is one part of the quality assurance for TRANSFAC. Besides it, there are several consistency checks, for example automated SQL queries or the adjustment of site sequence data with EMBL.
Connected tools
TRANSFAC has been connected with BLAST to facilitate searching for homologies with transcription factor sequences. The user may apply a protein or a DNA sequence, the latter will be translated into a polypeptide sequence automatically. The tool has been made available as TfBlast and uses BLAST 2.0 from May 26, 2000 (8). In addition to the previously described programs PatSearch (9) and MatInspector (10), we provide the tool AliBaba2 developed by Niels Grabe (11).
PathoDB
A lot of diseases are known to be caused by defects in gene regulation. PathoDB is a new relational TRANSFAC module that provides information about pathologically relevant mutations in transcription factor genes or in their binding sites. Currently, the database stores data about 10 530 transcription factor mutations and about 19 mutated binding sites. Linked to these are tables that hold information about the corresponding phenotypes and diagnostic methods for the underlying genotypes. We have made version 1.0 publicly available. It consists of four flat files (Genotype, MutatedFactor, MutatedSite, Phenotype) that exemplarily provide data about 10 239 p53 mutations, as represented in the p53 database (12), as well as all 19 mutated binding sites.
S/MARt DB
Regulatory domains in a eukaryotic genome are delimited or, at least, functionally influenced by scaffold/matrix attached regions (S/MARs), relatively long sequences in the genomic DNA through which the chromatin is attached to the nuclear matrix structure. The relational S/MARt DB (S/MAR transaction database) presently comprises 313 S/MARs and 61 S/MAR-binding proteins. Thus, the data content of this database module increased by ~38 (S/MARs) or 20% (S/MAR binders), respectively, since its first introduction to the public in October 1999. The number of S/MAR entries that include a DNA sequence has also been enhanced (from 64 to 78% of all S/MARs). Both tables (S/MAR and S/MARBinder) are connected to the TRANSFAC tables GENE and REFERENCES. The data that almost equally refer to animal and plant genomes were extracted mainly from original research reports.
The two flat files that are publicly available provide data about S/MARs and S/MAR binders have the references integrated and can be accessed through a search engine that is similar to that of the TRANSFAC database. SbBlast is a routine connected to S/MARt DB that allows to blast the table of S/MAR binders for similar protein sequences. A detailed description of S/MARt DB and its contents is the subject of another publication (I.Liebich et al., in preparation).
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TRANSPATH |
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TOPABSTRACTINTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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Since many transcription factors are regulated in response to extracellular signaling molecules, the signal transduction database TRANSPATH was initiated in 1996. Although it was originally developed as an object-oriented database, it has recently been re-engineered to a relational dabasase management system. Currently, it stores information about 2373 molecules and 2684 reactions taken from 405 references. Thus, its content has been enhanced by a factor of about 20 (molecules) or 30 (reactions) compared to the status reported previously (7).
TRANSPATH is connected with a PathwayBuilder, a routine that exhibits the regulatory cascade aiming at or starting from a user-defined molecule. The depth and cross-connectivity of these pathways can be defined by the user.
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CYTOMER® |
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TOPABSTRACTINTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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CYTOMER is a relational database that comprises tables for organs, cell types, physiological systems and developmental stages. The organ table is in itself hierarchically structured, linking anatomical structures to their corresponding sub- and superstructures. The whole organ table comprises presently 5236 entries, 4533 of which are incorporated into the hierarchical system. The other tables list 693 cells, 54 physiological systems and 71 developmental stages. These figures refer mainly to human resources, with a few murine objects attached which are adapted from the mouse vocabulary established by Kaufman (13) and implemented with the Gene Expression Database (GXD; 14). All these terms can be searched, and the transcription factors that are expressed in the corresponding tissues/organs/cells can be displayed. The complete vocabulary of the organ table will be made available to the public.
As a more recent extension, a corresponding compilation of Caenorhabditis elegans terms has been initiated. Up to now, it comprises 168 organ entries, all 959 somatic cells including the complete cell lineage, 13 developmental stages and 10 physiological systems. Additionally, we started to establish a plant CYTOMER first for Arabidopsis thaliana. Due to the distinct architecture of plants, the structure of this database is slightly different in having an extra tissue table. In summary, Arabidopsis CYTOMER currently comprises entries for 148 organs, 98 tissues, 30 cells, 7 physiological systems and 45 developmental stages.
In each case, a central ‘Hub’ table is designed to link all biologically existing entries of the other tables. For a given organism, it therefore represents a comprehensive list of all gene expression sources in a spatio-temporal coordinate system and provides a general framework to map expression patterns (15). It is used here to represent the expression patterns of human transcription factors, and to assemble expression profiles for selected organs with regard to the transcription factors they express.
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AVAILABILITY |
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TOPABSTRACTINTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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TRANSFAC as well as the other data resources mentioned in this paper are freely available to users from non-profit organizations at http://www.gene-regulation.de/, and some are available at a number of mirror sites. Users from commercial organizations are requested to license database versions either for online use or for inhouse installation. These licensed versions comprise user interfaces of enhanced functionality and data sets that are more frequently updated and extended by proprietary data generated by BIOBASE GmbH. Of course, academic institutions can also license this version.
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ACKNOWLEDGEMENTS |
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The authors are indebted to H. Hermjakob (EBI) for his help in establishing links between TRANSFAC, S/MARt DB and TRANSPATH. We also acknowledge the generous help granted by T. Takai-Igarashi and T. Kaminuma (National Institutes of Health Sciences, Tokyo) in supplying the data set of CSNDB. Finally, we express our gratitude to Mrs A. Bischoff for the technical help in nearly all of the above-mentioned fields. Parts of this work was supported by the German Ministry of Education and Research (BMBF, grant nos 01 KW 9629/7, 01 KW 9906 and 01SF9988/4), by a Scientific-Technical cooperation grant from BMBF (X224.6) and by a grant from the European Commission (QLRI-CT1999-01333).
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FOOTNOTES |
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* To whom correspondence should be addressed. Tel: +49 531 6181 427; Fax: +49 531 6181 266; Email: ewi{at}gbf.de
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REFERENCES |
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TOPABSTRACTINTRODUCTIONTHE TRANSFAC DATABASETRANSPATHCYTOMER(R)AVAILABILITYREFERENCES |
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C. L. Warren, N. C. S. Kratochvil, K. E. Hauschild, S. Foister, M. L. Brezinski, P. B. Dervan, G. N. Phillips Jr., and A. Z. Ansari Defining the sequence-recognition profile of DNA-binding molecules PNAS, January 24, 2006; 103(4): 867 - 872. [Abstract] [Full Text] [PDF]
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L. Narlikar and A. J. Hartemink Sequence features of DNA binding sites reveal structural class of associated transcription factor Bioinformatics, January 15, 2006; 22(2): 157 - 163. [Abstract] [Full Text] [PDF]
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X. Chen, J.-m. Wu, K. Hornischer, A. Kel, and E. Wingender TiProD: the Tissue-specific Promoter Database Nucleic Acids Res., January 1, 2006; 34(suppl_1): D104 - D107. [Abstract] [Full Text] [PDF]
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M. C. Teixeira, P. Monteiro, P. Jain, S. Tenreiro, A. R. Fernandes, N. P. Mira, M. Alenquer, A. T. Freitas, A. L. Oliveira, and I. Sa-Correia The YEASTRACT database: a tool for the analysis of transcription regulatory associations in Saccharomyces cerevisiae Nucleic Acids Res., January 1, 2006; 34(suppl_1): D446 - D451. [Abstract] [Full Text] [PDF]
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A. D. Recklies, H. Ling, C. White, and S. M. Bernier Inflammatory Cytokines Induce Production of CHI3L1 by Articular Chondrocytes J. Biol. Chem., December 16, 2005; 280(50): 41213 - 41221. [Abstract] [Full Text] [PDF]
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Z. Zhao, L. Fang, N. Chen, R. C. Johnsen, L. Stein, and D. L. Baillie Distinct Regulatory Elements Mediate Similar Expression Patterns in the Excretory Cell of Caenorhabditis elegans J. Biol. Chem., November 18, 2005; 280(46): 38787 - 38794. [Abstract] [Full Text] [PDF]
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T. Yu and K.-C. Li Inference of transcriptional regulatory network by two-stage constrained space factor analysis Bioinformatics, November 1, 2005; 21(21): 4033 - 4038. [Abstract] [Full Text] [PDF]
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A. V. Morozov, J. J. Havranek, D. Baker, and E. D. Siggia Protein-DNA binding specificity predictions with structural models Nucleic Acids Res., October 24, 2005; 33(18): 5781 - 5798. [Abstract] [Full Text] [PDF]
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H.-K. Tsai, H. H.-S. Lu, and W.-H. Li Statistical methods for identifying yeast cell cycle transcription factors PNAS, September 20, 2005; 102(38): 13532 - 13537. [Abstract] [Full Text] [PDF]
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D. C. King, J. Taylor, L. Elnitski, F. Chiaromonte, W. Miller, and R. C. Hardison Evaluation of regulatory potential and conservation scores for detecting cis-regulatory modules in aligned mammalian genome sequences Genome Res., August 1, 2005; 15(8): 1051 - 1060. [Abstract] [Full Text] [PDF]
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R. Pudimat, E.-G. Schukat-Talamazzini, and R. Backofen A multiple-feature framework for modelling and predicting transcription factor binding sites Bioinformatics, July 15, 2005; 21(14): 3082 - 3088. [Abstract] [Full Text] [PDF]
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A. Hsiao, T. Ideker, J. M. Olefsky, and S. Subramaniam VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data Nucleic Acids Res., July 1, 2005; 33(suppl_2): W627 - W632. [Abstract] [Full Text] [PDF]
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J. Qian, N. Esumi, Y. Chen, Q. Wang, I. Chowers, and D. J. Zack Identification of regulatory targets of tissue-specific transcription factors: application to retina-specific gene regulation Nucleic Acids Res., June 20, 2005; 33(11): 3479 - 3491. [Abstract] [Full Text] [PDF]
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A. C. Zambon, L. Zhang, S. Minovitsky, J. R. Kanter, S. Prabhakar, N. Salomonis, K. Vranizan, I. Dubchak, B. R. Conklin, and P. A. Insel Gene expression patterns define key transcriptional events in cell-cycle regulation by cAMP and protein kinase A PNAS, June 14, 2005; 102(24): 8561 - 8566. [Abstract] [Full Text] [PDF]
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S. Kamalakaran, S. K. Radhakrishnan, and W. T. Beck Identification of Estrogen-responsive Genes Using a Genome-wide Analysis of Promoter Elements for Transcription Factor Binding Sites J. Biol. Chem., June 3, 2005; 280(22): 21491 - 21497. [Abstract] [Full Text] [PDF]
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I. Ben-Gal, A. Shani, A. Gohr, J. Grau, S. Arviv, A. Shmilovici, S. Posch, and I. Grosse Identification of transcription factor binding sites with variable-order Bayesian networks Bioinformatics, June 1, 2005; 21(11): 2657 - 2666. [Abstract] [Full Text] [PDF]
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R. A. O'Flanagan, G. Paillard, R. Lavery, and A. M. Sengupta Non-additivity in protein-DNA binding Bioinformatics, May 15, 2005; 21(10): 2254 - 2263. [Abstract] [Full Text] [PDF]
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C. M. Bergman, J. W. Carlson, and S. E. Celniker Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster Bioinformatics, April 15, 2005; 21(8): 1747 - 1749. [Abstract] [Full Text] [PDF]
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E. A. Maier, M. R. Dusing, and D. A. Wiginton Cdx Binding Determines the Timing of Enhancer Activation in Postnatal Duodenum J. Biol. Chem., April 1, 2005; 280(13): 13195 - 13202. [Abstract] [Full Text] [PDF]
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A. Kotani, I.-m. Okazaki, M. Muramatsu, K. Kinoshita, N. A. Begum, T. Nakajima, H. Saito, and T. Honjo A target selection of somatic hypermutations is regulated similarly between T and B cells upon activation-induced cytidine deaminase expression PNAS, March 22, 2005; 102(12): 4506 - 4511. [Abstract] [Full Text] [PDF]
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Y. Barash, G. Elidan, T. Kaplan, and N. Friedman CIS: compound importance sampling method for protein-DNA binding site p-value estimation Bioinformatics, March 1, 2005; 21(5): 596 - 600. [Abstract] [Full Text] [PDF]
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C. A. Wiwi and D. J. Waxman Role of Hepatocyte Nuclear Factors in Transcriptional Regulation of Male-specific CYP2A2 J. Biol. Chem., February 4, 2005; 280(5): 3259 - 3268. [Abstract] [Full Text] [PDF]
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E. Barta, E. Sebestyen, T. B. Palfy, G. Toth, C. P. Ortutay, and L. Patthy DoOP: Databases of Orthologous Promoters, collections of clusters of orthologous upstream sequences from chordates and plants Nucleic Acids Res., January 1, 2005; 33(suppl_1): D86 - D90. [Abstract] [Full Text] [PDF]
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V. D. Marinescu, I. S. Kohane, and A. Riva The MAPPER database: a multi-genome catalog of putative transcription factor binding sites Nucleic Acids Res., January 1, 2005; 33(suppl_1): D91 - D97. [Abstract] [Full Text] [PDF]
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L. Elnitski, B. Giardine, P. Shah, Y. Zhang, C. Riemer, M. Weirauch, R. Burhans, W. Miller, and R. C. Hardison Improvements to GALA and dbERGE II: databases featuring genomic sequence alignment, annotation and experimental results Nucleic Acids Res., January 1, 2005; 33(suppl_1): D466 - D470. [Abstract] [Full Text] [PDF]
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J. H. Kim and M. R. Stallcup Role of the Coiled-coil Coactivator (CoCoA) in Aryl Hydrocarbon Receptor-mediated Transcription J. Biol. Chem., November 26, 2004; 279(48): 49842 - 49848. [Abstract] [Full Text] [PDF]
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J. J. Welch, J. A. Watts, C. R. Vakoc, Y. Yao, H. Wang, R. C. Hardison, G. A. Blobel, L. A. Chodosh, and M. J. Weiss Global regulation of erythroid gene expression by transcription factor GATA-1 Blood, November 15, 2004; 104(10): 3136 - 3147. [Abstract] [Full Text] [PDF]
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L. Wang, A. Klopot, J.-N. Freund, L. N. Dowling, S. D. Krasinski, and J. C. Fleet Control of differentiation-induced calbindin-D9k gene expression in Caco-2 cells by cdx-2 and HNF-1{alpha} Am J Physiol Gastrointest Liver Physiol, November 1, 2004; 287(5): G943 - G953. [Abstract] [Full Text] [PDF]
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J. K. Divine, L. J. Staloch, H. Haveri, C. M. Jacobsen, D. B. Wilson, M. Heikinheimo, and T. C. Simon GATA-4, GATA-5, and GATA-6 activate the rat liver fatty acid binding protein gene in concert with HNF-1{alpha} Am J Physiol Gastrointest Liver Physiol, November 1, 2004; 287(5): G1086 - G1099. [Abstract] [Full Text] [PDF]
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M. Kankainen and L. Holm POBO, transcription factor binding site verification with bootstrapping Nucleic Acids Res., July 1, 2004; 32(suppl_2): W222 - W229. [Abstract] [Full Text] [PDF]
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H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J.-D. J. Han, N. Bertin, S. Chung, M. Vidal, and M. Gerstein Annotation Transfer Between Genomes: Protein-Protein Interologs and Protein-DNA Regulogs Genome Res., June 1, 2004; 14(6): 1107 - 1118. [Abstract] [Full Text] [PDF]
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J. W. Tullai, M. E. Schaffer, S. Mullenbrock, S. Kasif, and G. M. Cooper Identification of Transcription Factor Binding Sites Upstream of Human Genes Regulated by the Phosphatidylinositol 3-Kinase and MEK/ERK Signaling Pathways J. Biol. Chem., May 7, 2004; 279(19): 20167 - 20177. [Abstract] [Full Text] [PDF]
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S. Jorajuria, N. Dereuddre-Bosquet, K. Naissant-Storck, D. Dormont, and P. Clayette Differential Expression Levels of MRP1, MRP4, and MRP5 in Response to Human Immunodeficiency Virus Infection in Human Macrophages Antimicrob. Agents Chemother., May 1, 2004; 48(5): 1889 - 1891. [Abstract] [Full Text] [PDF]
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H.-D. Huang, J.-T. Horng, Y.-M. Sun, A.-P. Tsou, and S.-L. Huang Identifying transcriptional regulatory sites in the human genome using an integrated system Nucleic Acids Res., March 29, 2004; 32(6): 1948 - 1956. [Abstract] [Full Text] [PDF]
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H. R. Ueda, S. Hayashi, S. Matsuyama, T. Yomo, S. Hashimoto, S. A. Kay, J. B. Hogenesch, and M. Iino Universality and flexibility in gene expression from bacteria to human PNAS, March 16, 2004; 101(11): 3765 - 3769. [Abstract] [Full Text] [PDF]
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K. N. Maclean, E. Kraus, and J. P. Kraus The Dominant Role of Sp1 in Regulating the Cystathionine {beta}-Synthase -1a and -1b Promoters Facilitates Potential Tissue-specific Regulation by Kruppel-like Factors J. Biol. Chem., March 5, 2004; 279(10): 8558 - 8566. [Abstract] [Full Text] [PDF]
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