Wingender, Edgar

[["dc.bibliographiccitation.firstpage","332"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","334"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Reuter, Ingmar"],["dc.contributor.author","Deineko, Igor V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-10T08:12:51Z"],["dc.date.available","2019-07-10T08:12:51Z"],["dc.date.issued","2002"],["dc.description.abstract","Originating from COMPEL, the TRANSCompel® database emphasizes the key role of specific interactions between transcription factors binding to their target sites providing specific features of gene regulation in a particular cellular content. Composite regulatory elements contain two closely situated binding sites for distinct transcription factors and represent minimal functional units providing combinatorial transcriptional regulation. Both specific factor–DNA and factor–factor interactions contribute to the function of composite elements (CEs). Information about the structure of known CEs and specific gene regulation achieved through such CEs appears to be extremely useful for promoter prediction, for gene function prediction and for applied gene engineering as well. Each database entry corresponds to an individual CE within a particular gene and contains information about two binding sites, two corresponding transcription factors and experiments confirming cooperative action between transcription factors. The COMPEL database, equipped with the search and browse tools, is available at http://www.gene-regulation.com/pub/databases.html#transcompel. Moreover, we have developed the program CATCHTM for searching potential CEs in DNA sequences. It is freely available as CompelPatternSearch at http://compel.bionet.nsc.ru/FunSite/CompelPatternSearch.html"],["dc.identifier.fs","12179"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4106"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61059"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1362-4962"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","TRANSCompel: a database on composite regulatory elements in eukaryotic genes."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","241"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Deyneko, Igor V."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Deineko, Elena V."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Weiss, Siegfried"],["dc.date.accessioned","2018-11-07T09:21:22Z"],["dc.date.available","2018-11-07T09:21:22Z"],["dc.date.issued","2013"],["dc.description.abstract","Background: Accurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed. Results: We developed a novel method to identify composite regulatory elements in promoters using a library of known examples. In depth investigation of regularities encoded in known composite elements allowed us to introduce a new characteristic measure and to improve the specificity compared with other methods. Tests on an established benchmark and real genomic data show that our method outperforms other available methods based either on known examples or statistical evaluations. In addition to better recognition, a practical advantage of this method is first the ability to detect a high number of different types of composite elements, and second direct biological interpretation of the identified results. The program is available at http://gnaweb.helmholtz-hzi.de/cgi-bin/MCatch/MatrixCatch.pl and includes an option to extend the provided library by user supplied data. Conclusions: The novel algorithm for the identification of composite regulatory elements presented in this paper was proved to be superior to existing methods. Its application to tissue specific promoters identified several highly specific composite elements with relevance to their biological function. This approach together with other methods will further advance the understanding of transcriptional regulation of genes."],["dc.identifier.doi","10.1186/1471-2105-14-241"],["dc.identifier.isi","000323123400001"],["dc.identifier.pmid","23924163"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10417"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29088"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2105"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","MatrixCatch - a novel tool for the recognition of composite regulatory elements in promoters"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","169"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of biosciences"],["dc.bibliographiccitation.lastpage","180"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Crass, Torsten"],["dc.contributor.author","Hogan, Jennifer D."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Potapov, Anatolij P."],["dc.date.accessioned","2019-07-10T08:11:49Z"],["dc.date.available","2019-07-10T08:11:49Z"],["dc.date.issued","2007"],["dc.description.abstract","Bioinformatics has delivered great contributions to genome and genomics research, without which the world-wide success of this and other global ('omics') approaches would not have been possible. More recently, it has developed further towards the analysis of different kinds of networks thus laying the foundation for comprehensive description, analysis and manipulation of whole living systems in modern \"systems biology\". The next step which is necessary for developing a systems biology that deals with systemic phenomena is to expand the existing and develop new methodologies that are appropriate to characterize intercellular processes and interactions without omitting the causal underlying molecular mechanisms. Modelling the processes on the different levels of complexity involved requires a comprehensive integration of information on gene regulatory events, signal transduction pathways, protein interaction and metabolic networks as well as cellular functions in the respective tissues / organs."],["dc.identifier.pmid","17426389"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11190"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60805"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","0250-5991"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","Goescholar"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Cell Communication"],["dc.subject.mesh","Cell Physiological Phenomena"],["dc.subject.mesh","Computational Biology"],["dc.subject.mesh","Databases, Genetic"],["dc.subject.mesh","Gene Regulatory Networks"],["dc.subject.mesh","Genomics"],["dc.subject.mesh","Hormones"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Metabolic Networks and Pathways"],["dc.subject.mesh","Signal Transduction"],["dc.subject.mesh","Systems Biology"],["dc.title","Integrative content-driven concepts for bioinformatics \"beyond the cell\"."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","163"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","COMPARATIVE AND FUNCTIONAL GENOMICS"],["dc.bibliographiccitation.lastpage","168"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Choi, C."],["dc.contributor.author","Krull, M."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Pistor, S."],["dc.contributor.author","Potapov, Anatolij P."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:50:32Z"],["dc.date.available","2018-11-07T10:50:32Z"],["dc.date.issued","2004"],["dc.description.abstract","TRANSPATH(R) can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyser. Therefore, three modules have been created: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder(TM), which provides several different types of network visualization and hence faciliates understanding; the third is ArrayAnalyzer(TM), which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH(R) to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH(R) is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC(R), a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes. More information is available at www.biobase.de/pages/products/databases.html. Copyright (C) 2004 John Wiley Sons, Ltd."],["dc.identifier.doi","10.1002/cfg.386"],["dc.identifier.isi","000221783700006"],["dc.identifier.pmid","18629064"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/4431"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/48676"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","John Wiley & Sons Ltd"],["dc.relation.issn","1531-6912"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","TRANSPATH (R) - a high quality database focused on signal transduction"],["dc.type","review"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","W541"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","W545"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Waleev, T."],["dc.contributor.author","Shtokalo, D."],["dc.contributor.author","Konovalova, T."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Cheremushkin, E."],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Kel, Alexander E."],["dc.date.accessioned","2018-11-07T09:39:04Z"],["dc.date.available","2018-11-07T09:39:04Z"],["dc.date.issued","2006"],["dc.description.abstract","Composite Module Analyst (CMA) is a novel software tool aiming to identify promoter-enhancer models based on the composition of transcription factor (TF) binding sites and their pairs. CMA is closely interconnected with the TRANSFAC (R) database. In particular, CMA uses the positional weight matrix (PWM) library collected in TRANSFAC (R) and therefore provides the possibility to search for a large variety of different TF binding sites. We model the structure of the long gene regulatory regions by a Boolean function that joins several local modules, each consisting of co-localized TF binding sites. Having as an input a set of co-regulated genes, CMA builds the promoter model and optimizes the parameters of the model automatically by applying a genetic-regression algorithm. We use a multicomponent fitness function of the algorithm which includes several statistical criteria in a weighted linear function. We show examples of successful application of CMA to a microarray data on transcription profiling of TNF-alpha stimulated primary human endothelial cells. The CMA web server is freely accessible at http://www.gene-regulation.com/pub/programs/cma/CMA.html. An advanced version of CMA is also a part of the commercial system ExPlain (TM) (www.biobase.de) designed for causal analysis of gene expression data."],["dc.identifier.doi","10.1093/nar/gkl342"],["dc.identifier.isi","000245650200108"],["dc.identifier.pmid","16845066"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4123"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33202"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","0305-1048"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Composite Module Analyst: identification of transcription factor binding site combinations using genetic algorithm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]