Elena A. Ananko

Databases on transcriptional regulation: TRANSFAC, TRRD and COMPEL

TRANSFAC, TRRD (Transcription Regulatory Region Database) and COMPEL are databases which store information about transcriptional regulation in eukaryotic cells. The three databases provide distinct views on the components involved in transcription: transcription factors and their binding sites and binding profiles (TRANSFAC), the regulatory hierarchy of whole genes (TRRD), and the structural and functional properties of composite elements (COMPEL). The quantitative and qualitative changes of all three databases and connected programs are described. The databases are accessible via WWW:http://transfac.gbf.de/TRANSFAC orhttp://www.bionet.nsc.ru/TRRD

Transcription Regulatory Regions Database (TRRD): its status in 2002

Transcription Regulatory Regions Database (TRRD) is an informational resource containing an integrated description of the gene transcription regulation. An entry of the database corresponds to a gene and contains the data on localization and functions of the transcription regulatory regions as well as gene expression patterns. TRRD contains only experimental data that are inputted into the database through annotating scientific publication. TRRD release 6.0 comprises the information on 1167 genes, 5537 transcription factor binding sites, 1714 regulatory regions, 14 locus control regions and 5335 expression patterns obtained through annotating 3898 scientific papers. This information is arranged in seven databases: TRRDGENES (general gene description), TRRDLCR (locus control regions); TRRDUNITS (regulatory regions: promoters, enhancers, silencers, etc.), TRRDSITES (transcription factor binding sites), TRRDFACTORS (transcription factors), TRRDEXP (expression patterns) and TRRDBIB (experimental publications). Sequence Retrieval System (SRS) is used as a basic tool for navigating and searching TRRD and integrating it with external informational and software resources. The visualization tool, TRRD Viewer, provides the information representation in a form of maps of gene regulatory regions. The option allowing nucleotide sequences to be searched for according to their homology using BLAST is also included. TRRD is available at http://www.bionet.nsc.ru/trrd/.

GeneNet: a gene network database and its automated visualization.

MOTIVATION Gene networks that provide the regulation of physiological processes are the basic feature of organisms. Information regarding the regulation of gene expression and signal transduction pathways is increasing rapidly. However, the information is hard to formalize and systematize. Ways and means for automated visualization of the gene networks based on their formalized description are needed. RESULTS The object-oriented database GeneNet and the software for its automated visualization have been developed. The main principles of a formalized description of the gene network have been worked out. Antiviral response and erythropoiesis are provided as examples to show how this is achieved. The GeneNet graphical user interface written in Java provides automated generation of the gene network diagrams and allows visualization and exploration of the GeneNet database through the Internet. A system of filters allows the selection of particular components of the network for visualization. AVAILABILITY The GeneNet database and its graphical user interface are available at http://wwwmgs.bionet.nsc.ru/systems/MGL/GeneN et/ CONTACT fedor@bionet.nsc.ru

Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions

BackgroundReliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.ResultsTo improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-κB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies.To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA.Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced false positives at least at higher stringencies.ConclusionBased on this analysis, SiteGA adds substantial specificity even to optimized PWMs and may be considered for large-scale genome analysis. It adds to the range of techniques available for TFBS prediction, and EPD analysis has led to a list of genes which appear to be regulated by the above TFs.

GeneNet in 2005

The GeneNet system is designed for collection and analysis of the data on gene and metabolic networks, signal transduction pathways and kinetic characteristics of elementary processes. In the past 2 years, the GeneNet structure was considerably improved: (i) the current version of the database is now implemented using ORACLE9i; (ii) the capacities to describe the structure of the protein complexes and the interactions between the units are increased; (iii) two tables with kinetic constants and more detailed descriptions of certain reactions were added; and (iv) a module for kinetic modeling was supplemented. The current SRS release of the GeneNet database contains 37 graphical maps of gene networks, as well as descriptions of 1766 proteins, 1006 genes, 241 small molecules and 3254 relationships between gene network units, and 552 kinetic constants. Information distributed between 16 interlinked tables was obtained by annotating 1980 journal publications. SRS release of the GeneNet database, the graphical viewer and the modeling section are available at http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet/.

GeneNet: a database on structure and functional organisation of gene networks.

The GeneNet database is designed for accumulation of information on gene networks. Original technology applied in GeneNet enables description of not only a gene network structure and functional relationships between components, but also metabolic and signal transduction pathways. Specialised software, GeneNet Viewer, automatically displays the graphical diagram of gene networks described in the database. Current release 3.0 of GeneNet database contains descriptions of 25 gene networks, 945 proteins, 567 genes, 151 other substances and 1364 relationships between components of gene networks. Information distributed between 14 interlinked tables was obtained by annotating 968 scientific publications. The SRS-version of GeneNet database is freely available (http://wwwmgs.bionet.nsc.ru/mgs/systems/genenet/).

Recognition of interferon-inducible sites, promoters, and enhancers

BackgroundComputational analysis of gene regulatory regions is important for prediction of functions of many uncharacterized genes. With this in mind, search of the target genes for interferon (IFN) induction appears of interest. IFNs are multi-functional cytokines. Their effects are immunomodulatory, antiviral, antibacterial, and antitumor. The interaction of the IFNs with their cell surface receptors produces an activation of several transcription factors. Four regulatory factors, ISGF3, STAT1, IRF1, and NF-κB, are essential for the function of the IFN system. The aim of this work is the development of computational approaches for the recognition of DNA binding sites for these factors and computer programs for the prediction of the IFN-inducible regions.ResultsWe developed computational approaches to the recognition of the binding sites for ISGF3, STAT1, IRF1, and NF-κB. Analysis of the distribution of these binding sites demonstrated that the regions -500 upstream of the transcription start site in IFN-inducible genes are enriched in putative binding sites for these transcription factors. Based on selected combinations of the sites whose frequencies were significantly higher than in the other functional gene groups, we developed methods for the prediction of the IFN-inducible promoters and enhancers. We analyzed 1004 sequences of the IFN-inducible genes compiled using microarray data analyses and also about 10,000 human gene sequences from the EPD and RefSeq databases; 74 of 1,664 human genes annotated in EPD were significantly IFN-inducible.ConclusionAnalyses of several control datasets demonstrated that the developed methods have a high accuracy of prediction of the IFN-inducible genes. Application of these methods to several datasets suggested that the number of the IFN-inducible genes is approximately 1500–2000 in the human genome.

Bioinformatical and experimental approaches to investigation of transcription factor binding sites in vertebrate genes

The development of computer-assisted methods for transcription factor binding sites (TFBS) recognition is necessary for study the DNA regulatory transcription code. There are a great number of experimental methods that enable TFBS identification in genome sequences. The experimental data can be used to elaborate multiple computer approaches to recognition of TFBS, each of which has its own advantages and limitations. A short review of the characteristics of computer methods of TFBS prediction based on various principles is presented. Methods used for experimental monitoring of predicted sites are analyzed. Data concerning DNA regulatory potential and its realization at the chromatin level, obtained using these methods, are discussed along with approaches to recognition of target genes of certain transcription factors in the genome sequences.

Transcription regulatory codes of eukaryotic genomes

The key aspects of transcription regulation in multicellular organisms were discussed in the paper, including characteristics of promoters, transcription factor binding sites and composite elements. The functional roles of transcriptional regulators (GTFs and transcription factors) were described together with mechanisms, which regulate its activity. The importance of DNA-encoded nucleosome organization and chromatin modifications for the process of transcription regulation have been declared. Also the significance of mechanisms which regulate activity of transcription factors within Gene Networks have been stressed. In light of recent data transcriptional regulatory codes of a eukaryotic genome were discussed.

Artsite Database: Comparison of In Vitro Selected and Natural Binding Sites of Eukaryotic Transcription Factors

The ArtSite database was developed; the database compiles the information on the structures of eukaryotic transcription factor binding sites and/or their DNA-binding domains obtained from in vitro selected sequences. Current release of the database comprises 420 matrices describing specific features of binding sites or their DNA-binding domains for over 200 transcription factors. The matrices were constructed basing on alignments of representative samples of transcription factor binding sites, totally containing over 10 thousand sequences.