Wingender, Edgar

[["dc.bibliographiccitation.journal","European Journal of Cell Biology"],["dc.bibliographiccitation.volume","83"],["dc.contributor.author","Liebich, I."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:50:36Z"],["dc.date.available","2018-11-07T10:50:36Z"],["dc.date.issued","2004"],["dc.format.extent","61"],["dc.identifier.isi","000220823900130"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/48694"],["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 Deutschen-Gesellschaft-fur-Zellbiologie"],["dc.relation.eventlocation","Berlin, Germany"],["dc.relation.issn","0171-9335"],["dc.title","S/MARt DB: The database on chromatin organizing elements"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","D540"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","D545"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Potapov, Anatolij P."],["dc.contributor.author","Liebich, Ines"],["dc.contributor.author","Doenitz, Juergen"],["dc.contributor.author","Schwarzer, Knut"],["dc.contributor.author","Sasse, Nicole"],["dc.contributor.author","Schoeps, Torsten"],["dc.contributor.author","Crass, Torsten"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:39:42Z"],["dc.date.available","2018-11-07T10:39:42Z"],["dc.date.issued","2006"],["dc.description.abstract","EndoNet is a new database that provides information about the components of endocrine networks and their relations. It focuses on the endocrine cell-to-cell communication and enables the analysis of intercellular regulatory pathways in humans. In the EndoNet data model, two classes of components span a bipartite directed graph. One class represents the hormones ( in the broadest sense) secreted by defined donor cells. The other class consists of the acceptor or target cells expressing the corresponding hormone receptors. The identity and anatomical environment of cell types, tissues and organs is defined through references to the CYTOMER (R) ontology. With the EndoNet user interface, it is possible to query the database for hormones, receptors or tissues and to combine several items from different search rounds in one complex result set, from which a network can be reconstructed and visualized. For each entity, a detailed characteristics page is available. Some well-established endocrine pathways are offered as showcases in the form of predefined result sets. These sets can be used as a starting point for a more complex query or for obtaining a quick overview. The EndoNet database is accessible at http://endonet.bioinf.med.uni-goettingen.de/."],["dc.identifier.doi","10.1093/nar/gkj121"],["dc.identifier.isi","000239307700117"],["dc.identifier.pmid","16381928"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/46112"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1362-4962"],["dc.relation.issn","0305-1048"],["dc.title","EndoNet: an information resource about endocrine networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Naunyn-Schmiedeberg s Archives of Pharmacology"],["dc.bibliographiccitation.volume","389"],["dc.contributor.author","Schlick, S."],["dc.contributor.author","Tiburcy, Malte"],["dc.contributor.author","Zeidler, S."],["dc.contributor.author","Lutz, S."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Zimmermann, Wolfram-Hubertus"],["dc.date.accessioned","2018-11-07T10:19:01Z"],["dc.date.available","2018-11-07T10:19:01Z"],["dc.date.issued","2016"],["dc.format.extent","S12"],["dc.identifier.isi","000398368200040"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41573"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.publisher.place","New york"],["dc.relation.conference","82nd Annual Meeting of the German-Society-for-Exerimental-and-Clinical-Pharmacology-and-Toxicology (DGPT) / 18th Annual Meeting of the Network-Clinical-Pharmacology-Germany (VKliPha)"],["dc.relation.eventlocation","Berlin, GERMANY"],["dc.relation.issn","1432-1912"],["dc.relation.issn","0028-1298"],["dc.title","Hyaluronic acid deposition determines engineered heart muscle characteristics and can be pharmacologically targeted to enhance function"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","I509"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","I514"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Li, Jie"],["dc.contributor.author","Hua, X. U."],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Wang, J."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T09:05:52Z"],["dc.date.available","2018-11-07T09:05:52Z"],["dc.date.issued","2012"],["dc.description.abstract","The great variety of human cell types in morphology and function is due to the diverse gene expression profiles that are governed by the distinctive regulatory networks in different cell types. It is still a challenging task to explain how the regulatory networks achieve the diversity of different cell types. Here, we report on our studies of the design principles of the tissue regulatory system by constructing the regulatory networks of eight human tissues, which subsume the regulatory interactions between transcription factors (TFs), microRNAs (miRNAs) and non-TF target genes. The results show that there are in-/out-hubs of high in-/out-degrees in tissue networks. Some hubs (strong hubs) maintain the hub status in all the tissues where they are expressed, whereas others (weak hubs), in spite of their ubiquitous expression, are hubs only in some tissues. The network motifs are mostly feed-forward loops. Some of them having no miRNAs are the common motifs shared by all tissues, whereas the others containing miRNAs are the tissue-specific ones owned by one or several tissues, indicating that the transcriptional regulation is more conserved across tissues than the post-transcriptional regulation. In particular, a common bow-tie framework was found that underlies the motif instances and shows diverse patterns in different tissues. Such bow-tie framework reflects the utilization efficiency of the regulatory system as well as its high variability in different tissues, and could serve as the model to further understand the structural adaptation of the regulatory system to the specific requirements of different cell functions."],["dc.identifier.doi","10.1093/bioinformatics/bts387"],["dc.identifier.isi","000308532300030"],["dc.identifier.pmid","22962474"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25421"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.publisher.place","Oxford"],["dc.relation.conference","11th European Conference on Computational Biology (ECCB) / Conference of the Intelligent Systems in Molecular Biology (ISMB)"],["dc.relation.eventlocation","Basel, SWITZERLAND"],["dc.relation.issn","1367-4803"],["dc.title","The architecture of the gene regulatory networks of different tissues"],["dc.type","conference_paper"],["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.firstpage","2403"],["dc.bibliographiccitation.issue","16"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","2410"],["dc.bibliographiccitation.volume","32"],["dc.contributor.author","Hua, X. U."],["dc.contributor.author","Chen, Luxiao"],["dc.contributor.author","Wang, J."],["dc.contributor.author","Li, Jie"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:10:09Z"],["dc.date.available","2018-11-07T10:10:09Z"],["dc.date.issued","2016"],["dc.description.abstract","Motivation: Identification of microRNA (miRNA) transcriptional start sites (TSSs) is crucial to understand the transcriptional regulation of miRNA. As miRNA expression is highly cell specific, an automatic and systematic method that could identify miRNA TSSs accurately and cell specifically is in urgent requirement. Results: A workflow to identify the TSSs of miRNAs was built by integrating the data of H3K4me3 and DNase I hypersensitive sites as well as combining the conservation level and sequence feature. By applying the workflow to the data for 54 cell lines from the ENCODE project, we successfully identified TSSs for 663 intragenic miRNAs and 620 intergenic miRNAs, which cover 84.2% (1283/1523) of all miRNAs recorded in miRBase 18. For these cell lines, we found 4042 alternative TSSs for intragenic miRNAs and 3186 alternative TSSs for intergenic miRNAs. Our method achieved a better performance than the previous non-cell-specific methods on miRNA TSSs. The cell-specific method developed by Georgakilas et al. gives 158 TSSs of higher accuracy in two cell lines, benefitting from the employment of deep-sequencing technique. In contrast, our method provided a much higher number of miRNA TSSs (7228) for a broader range of cell lines without the limitation of costly deep-sequencing data, thus being more applicable for various experimental cases. Analysis showed that upstream promoters at -2 kb to -200 bp of TSS are more conserved for independently transcribed miRNAs, while for miRNAs transcribed with host genes, their core promoters (-200 bp to 200 bp of TSS) are significantly conserved."],["dc.identifier.doi","10.1093/bioinformatics/btw171"],["dc.identifier.isi","000383682900001"],["dc.identifier.pmid","27153609"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39799"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Oxford Univ Press"],["dc.relation.issn","1460-2059"],["dc.relation.issn","1367-4803"],["dc.title","Identifying cell-specific microRNA transcriptional start sites"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","S4"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Kel, Alexander"],["dc.contributor.author","Boyarskikh, Ulyana"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Leskov, Leonid S."],["dc.contributor.author","Sokolov, Andrey V."],["dc.contributor.author","Yevshin, Ivan"],["dc.contributor.author","Mandrik, Nikita"],["dc.contributor.author","Stelmashenko, Daria"],["dc.contributor.author","Koschmann, Jeannette"],["dc.contributor.author","Kel-Margoulis, Olga"],["dc.contributor.author","Krull, Mathias"],["dc.contributor.author","Martínez-Cardús, Anna"],["dc.contributor.author","Moran, Sebastian"],["dc.contributor.author","Esteller, Manel"],["dc.contributor.author","Kolpakov, Fedor"],["dc.contributor.author","Filipenko, Maxim"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2020-12-10T18:38:50Z"],["dc.date.available","2020-12-10T18:38:50Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1186/s12859-019-2687-7"],["dc.identifier.eissn","1471-2105"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16259"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77451"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","European Journal of Cell Biology"],["dc.bibliographiccitation.volume","83"],["dc.contributor.author","Pistor, S."],["dc.contributor.author","Choi, C."],["dc.contributor.author","Krull, M."],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Potapov, Anatolij P."],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:50:35Z"],["dc.date.available","2018-11-07T10:50:35Z"],["dc.date.issued","2004"],["dc.format.extent","33"],["dc.identifier.isi","000220823900046"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/48689"],["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 Deutschen-Gesellschaft-fur-Zellbiologie"],["dc.relation.eventlocation","Berlin, Germany"],["dc.relation.issn","0171-9335"],["dc.title","Analysis of the regulatory network of APP, amyloid beta protein precursor, with the signal transcluction database TRANSPATH (R) Professional"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]