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.firstpage","1190"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","1197"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Konovalova, T."],["dc.contributor.author","Waleev, T."],["dc.contributor.author","Cheremushkin, E."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T09:48:57Z"],["dc.date.available","2018-11-07T09:48:57Z"],["dc.date.issued","2006"],["dc.description.abstract","Motivation: Functionally related genes involved in the same molecular-genetic, biochemical or physiological process are often regulated coordinately. Such regulation is provided by precisely organized binding of a multiplicity of special proteins [transcription factors (TFs)] to their target sites (cis-elements) in regulatory regions of genes. Cis-element combinations provide a structural basis for the generation of unique patterns of gene expression. Results: Here we present a new approach for defining promoter models based on the composition of TF binding sites and their pairs. We utilize a multicomponent fitness function for selection of the promoter model that fits best to the observed gene expression profile. We demonstrate examples of successful application of the fitness function with the help of a genetic algorithm for the analysis of functionally related or co-expressed genes as well as testing on simulated and permutated data."],["dc.identifier.doi","10.1093/bioinformatics/btl041"],["dc.identifier.isi","000237319300007"],["dc.identifier.pmid","16473870"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35413"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1367-4803"],["dc.title","Composite Module Analyst: a fitness-based tool for identification of transcription factor binding site combinations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","European Journal of Cell Biology"],["dc.bibliographiccitation.volume","84"],["dc.contributor.author","Choi, C."],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Krull, M."],["dc.contributor.author","Pistor, S."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T11:19:06Z"],["dc.date.available","2018-11-07T11:19:06Z"],["dc.date.issued","2005"],["dc.format.extent","23"],["dc.identifier.isi","000228527700027"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55192"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Urban & Fischer Verlag"],["dc.publisher.place","Jena"],["dc.relation.conference","Annual Meeting of the Deutsche-Gesellschaft-fur-Zellbiologie"],["dc.relation.eventlocation","Heidelberg, GERMANY"],["dc.relation.issn","0171-9335"],["dc.title","The TRANSPATH((R)) database: Identifying candidate pathways regulating the ubiquitin ligase parkin, a marker of Parkinson disease"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","481"],["dc.bibliographiccitation.issue","5-6"],["dc.bibliographiccitation.journal","SAR and QSAR in Environmental Research"],["dc.bibliographiccitation.lastpage","494"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Valeev, T."],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T11:20:44Z"],["dc.date.available","2018-11-07T11:20:44Z"],["dc.date.issued","2008"],["dc.description.abstract","Different signal transduction pathways leading to the activation of transcription factors (TFs) converge at key molecules that master the regulation of many cellular processes. Such crossroads of signalling networks often appear as Achilles Heels causing a disease when not functioning properly. Novel computational tools are needed for analysis of the gene expression data in the context of signal transduction and gene regulatory pathways and for identification of the key nodes in the networks. An integrated computational system, ExPlain (TM) (www.biobase.de) was developed for causal interpretation of gene expression data and identification of key signalling molecules. The system utilizes data from two databases (TRANSFAC (R) and TRANSPATH (R)) and integrates two programs: (1) Composite Module Analyst (CMA) analyses 5'-upstream regions of co-expressed genes and applies a genetic algorithm to reveal composite modules (CMs) consisting of co-occurring single TF binding sites and composite elements; (2) ArrayAnalyzer (TM) is a fast network search engine that analyses signal transduction networks controlling the activities of the corresponding TFs and seeks key molecules responsible for the observed concerted gene activation. ExPlain (TM) system was applied to microarray data on inflammatory bowel diseases (IBD). The results obtained suggest a number of highly interesting biological hypotheses about molecular mechanisms of pathological genetic disregulation."],["dc.identifier.doi","10.1080/10629360802083806"],["dc.identifier.isi","000259995500004"],["dc.identifier.pmid","18853298"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55611"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Taylor & Francis Ltd"],["dc.relation.issn","1062-936X"],["dc.title","ExPlain (TM): finding upstream drug targets in disease gene regulatory networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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.artnumber","e1002958"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Borlak, Juergen"],["dc.date.accessioned","2018-11-07T09:27:25Z"],["dc.date.available","2018-11-07T09:27:25Z"],["dc.date.issued","2013"],["dc.description.abstract","Algorithmic comparison of DNA sequence motifs is a problem in bioinformatics that has received increased attention during the last years. Its main applications concern characterization of potentially novel motifs and clustering of a motif collection in order to remove redundancy. Despite growing interest in motif clustering, the question which motif clusters to aim at has so far not been systematically addressed. Here we analyzed motif similarities in a comprehensive set of vertebrate transcription factor classes. For this we developed enhanced similarity scores by inclusion of the information coverage (IC) criterion, which evaluates the fraction of information an alignment covers in aligned motifs. A network-based method enabled us to identify motif clusters with high correspondence to DNA-binding domain phylogenies and prior experimental findings. Based on this analysis we derived a set of motif families representing distinct binding specificities. These motif families were used to train a classifier which was further integrated into a novel algorithm for unsupervised motif clustering. Application of the new algorithm demonstrated its superiority to previously published methods and its ability to reproduce entrained motif families. As a result, our work proposes a probabilistic approach to decide whether two motifs represent common or distinct binding specificities."],["dc.identifier.doi","10.1371/journal.pcbi.1002958"],["dc.identifier.isi","000316864200030"],["dc.identifier.pmid","23555204"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8739"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30532"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","A Discriminative Approach for Unsupervised Clustering of DNA Sequence Motifs"],["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","1"],["dc.bibliographiccitation.journal","EuPA open proteomics"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Valeev, Tagir"],["dc.contributor.author","Koschmann, Jeannette"],["dc.contributor.author","Poroikov, Vladimir"],["dc.contributor.author","Kel-Margoulis, Olga V."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-26T14:05:50Z"],["dc.date.available","2019-07-26T14:05:50Z"],["dc.date.issued","2016-12"],["dc.description.abstract","We present an \"upstream analysis\" strategy for causal analysis of multiple \"-omics\" data. It analyzes promoters using the TRANSFAC database, combines it with an analysis of the upstream signal transduction pathways and identifies master regulators as potential drug targets for a pathological process. We applied this approach to a complex multi-omics data set that contains transcriptomics, proteomics and epigenomics data. We identified the following potential drug targets against induced resistance of cancer cells towards chemotherapy by methotrexate (MTX): TGFalpha, IGFBP7, alpha9-integrin, and the following chemical compounds: zardaverine and divalproex as well as human metabolites such as nicotinamide N-oxide."],["dc.identifier.doi","10.1016/j.euprot.2016.09.002"],["dc.identifier.pmid","29900117"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62103"],["dc.language.iso","en"],["dc.relation.eissn","2212-9685"],["dc.relation.issn","2212-9685"],["dc.title","Multi-omics \"upstream analysis\" of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","412"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","The Journal of Open Source Software"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel, Alexander E."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-26T14:03:43Z"],["dc.date.available","2019-07-26T14:03:43Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.21105/joss.00412"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/62102"],["dc.language.iso","en"],["dc.relation.issn","2475-9066"],["dc.title","geneXplainR: An R interface for the geneXplain platform"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["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"]]