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","3433"],["dc.bibliographiccitation.issue","15"],["dc.bibliographiccitation.journal","NUCLEIC ACIDS RESEARCH"],["dc.bibliographiccitation.lastpage","3442"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Liebich, I."],["dc.contributor.author","Bode, J."],["dc.contributor.author","Reuter, I."],["dc.contributor.author","Wingender, E."],["dc.date.accessioned","2019-07-10T08:12:49Z"],["dc.date.available","2019-07-10T08:12:49Z"],["dc.date.issued","2002"],["dc.description.abstract","Based on the contents of the database S/MARt DB, the most comprehensive data collection of scaffold/matrix-attached regions (S/MARs) publicly available thus far, we initiated a systematic evaluation of the stored data. By analyzing the 245 S/MAR sequences presently described in this database, we found that the S/MARs contained in this collection are generally AT-rich, with certain significant exceptions. Comparative analyses showed that most of the AT-rich motifs which were found to be enriched in S/MARs are also enriched in randomized S/MAR sequences of the same AT content. Some sequence patterns previously suggested to be characteristic for S/MARs were also investigated, among them potential binding sites for homeodomain transcription factors. Even though hexanucleotides containing the core motif of homeodomain factors were frequently observed in S/MARs, only a few potential binding sites for these factors were found enriched when compared with regulatory regions or exon sequences. All our analyses indicated that, on average, the observed frequency of motifs in S/MAR elements is largely influenced by the AT content. Our results can serve as a guideline for further improvements in the definition of S/MARs, which are now believed to constitute the functional coordinate system for genomic regulatory regions."],["dc.identifier.fs","12175"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4015"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61045"],["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","Evaluation of sequence motifs found in scaffold/matrix-attached regions (S/MARs)."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","97"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","100"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Krull, Mathias"],["dc.contributor.author","Voss, Nico"],["dc.contributor.author","Choi, Claudia"],["dc.contributor.author","Pistor, Susanne"],["dc.contributor.author","Potapov, Anatolij"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-10T08:12:52Z"],["dc.date.available","2019-07-10T08:12:52Z"],["dc.date.issued","2003"],["dc.description.abstract","TRANSPATH® is a database system about gene regulatory networks that combines encyclopedic information on signal transduction with tools for visualization and analysis. The integration with TRANSFAC®, a database about transcription factors and their DNA binding sites, provides the possibility to obtain complete signaling pathways from ligand to target genes and their products, which may themselves be involved in regulatory action. As of July 2002, the TRANSPATH Professional release 3.2 contains about 9800 molecules, >1800 genes and >11 400 reactions collected from ~5000 references. With the ArrayAnalyzerTM, an integrated tool has been developed for evaluation of microarray data. It uses the TRANSPATH data set to identify key regulators in pathways connected with up- or down-regulated genes of the respective array. The key molecules and their surrounding networks can be viewed with the PathwayBuilderTM, a tool that offers four different modes of visualization. More information on TRANSPATH is available at http://www.biobase.de/pages/products/databases.html."],["dc.identifier.fs","12173"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4112"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61065"],["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","TRANSPATH (R): an integrated database on signal transduction and a tool for array analysis"],["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","270"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Microarrays"],["dc.bibliographiccitation.lastpage","286"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Koschmann, Jeannette"],["dc.contributor.author","Bhar, Anirban"],["dc.contributor.author","Stegmaier, Philip"],["dc.contributor.author","Kel, Alexander"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2019-07-09T11:41:23Z"],["dc.date.available","2019-07-09T11:41:23Z"],["dc.date.issued","2015"],["dc.description.abstract","A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators."],["dc.identifier.doi","10.3390/microarrays4020270"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12010"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58417"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/258236/EU//SYSCOL"],["dc.relation.euproject","SYSCOL"],["dc.relation.issn","2076-3905"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","“Upstream Analysis”: An Integrated Promoter-Pathway Analysis Approach to Causal Interpretation of Microarray Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","400"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Meckbach, Cornelia"],["dc.contributor.author","Tacke, Rebecca"],["dc.contributor.author","Hua, X. U."],["dc.contributor.author","Waack, Stephan"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Gueltas, Mehmet"],["dc.date.accessioned","2018-11-07T09:48:28Z"],["dc.date.available","2018-11-07T09:48:28Z"],["dc.date.issued","2015"],["dc.description.abstract","Background: Transcription factors (TFs) are important regulatory proteins that govern transcriptional regulation. Today, it is known that in higher organisms different TFs have to cooperate rather than acting individually in order to control complex genetic programs. The identification of these interactions is an important challenge for understanding the molecular mechanisms of regulating biological processes. In this study, we present a new method based on pointwise mutual information, PC-TraFF, which considers the genome as a document, the sequences as sentences, and TF binding sites (TFBSs) as words to identify interacting TFs in a set of sequences. Results: To demonstrate the effectiveness of PC-TraFF, we performed a genome-wide analysis and a breast cancerassociated sequence set analysis for protein coding and miRNA genes. Our results show that in any of these sequence sets, PC-TraFF is able to identify important interacting TF pairs, for most of which we found support by previously published experimental results. Further, we made a pairwise comparison between PC-TraFF and three conventional methods. The outcome of this comparison study strongly suggests that all these methods focus on different important aspects of interaction between TFs and thus the pairwise overlap between any of them is only marginal. Conclusions: In this study, adopting the idea from the field of linguistics in the field of bioinformatics, we develop a new information theoretic method, PC-TraFF, for the identification of potentially collaborating transcription factors based on the idiosyncrasy of their binding site distributions on the genome. The results of our study show that PC-TraFF can succesfully identify known interacting TF pairs and thus its currently biologically uncorfirmed predictions could provide new hypotheses for further experimental validation. Additionally, the comparison of the results of PC-TraFF with the results of previous methods demonstrates that different methods with their specific scopes can perfectly supplement each other. Overall, our analyses indicate that PC-TraFF is a time-efficient method where its algorithm has a tractable computational time and memory consumption."],["dc.identifier.doi","10.1186/s12859-015-0827-2"],["dc.identifier.isi","000365858300001"],["dc.identifier.pmid","26627005"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12578"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35312"],["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.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information"],["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","W619"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","W624"],["dc.bibliographiccitation.volume","35"],["dc.contributor.author","Degenhardt, Jost"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Doenitz, Juergen"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Crass, Torsten"],["dc.date.accessioned","2018-11-07T11:01:14Z"],["dc.date.available","2018-11-07T11:01:14Z"],["dc.date.issued","2007"],["dc.description.abstract","High-throughput methods for measuring transcript abundance, like SAGE or microarrays, are widely used for determining differences in gene expression between different tissue types, dignities (normal/malignant) or time points. Further analysis of such data frequently aims at the identification of gene interaction networks that form the causal basis for the observed properties of the systems under examination. To this end, it is usually not sufficient to rely on the measured gene expression levels alone; rather, additional biological knowledge has to be taken into account in order to generate useful hypotheses about the molecular mechanism leading to the realization of a certain phenotype. We present a method that combines gene expression data with biological expert knowledge on molecular interaction networks, as described by the TRANSPATH(1) database on signal transduction, to predict additional - and not necessarily differentially expressed - genes or gene products which might participate in processes specific for either of the examined tissues or conditions. In a first step, significance values for over-expression in tissue/condition A or B are assigned to all genes in the expression data set. Genes with a significance value exceeding a certain threshold are used as starting points for the reconstruction of a graph with signaling components as nodes and signaling events as edges. In a subsequent graph traversal process, again starting from the previously identified differentially expressed genes, all encountered nodes 'inherit' all their starting nodes' significance values. In a final step, the graph is visualized, the nodes being colored according to a weighted average of their inherited significance values. Each node's, or sub-network's, predominant color, ranging from green (significant for tissue/condition A) over yellow (not significant for either tissue/condition) to red (significant for tissue/condition B), thus gives an immediate visual clue on which molecules - differentially expressed or not - may play pivotal roles in the tissues or conditions under examination. The described method has been implemented in Java as a client/server application and a web interface called DEEP (Differential Expression Effector Prediction). The client, which features an easy-to-use graphical interface, can freely be downloaded from the following URL: http://deep.bioinf.med.uni-goettingen.de."],["dc.identifier.doi","10.1093/nar/gkm469"],["dc.identifier.isi","000255311500115"],["dc.identifier.pmid","17584786"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4016"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/51101"],["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","DEEP - A tool for differential expression effector prediction"],["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","D689"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","D694"],["dc.bibliographiccitation.volume","36"],["dc.contributor.author","Doenitz, Juergen"],["dc.contributor.author","Goemann, Bjoern"],["dc.contributor.author","Lize, Muriel"],["dc.contributor.author","Michael, Holger"],["dc.contributor.author","Sasse, Nicole"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Potapov, Anatolij P."],["dc.date.accessioned","2018-11-07T11:20:30Z"],["dc.date.available","2018-11-07T11:20:30Z"],["dc.date.issued","2008"],["dc.description.abstract","EndoNet is an information resource about intercellular regulatory communication. It provides information about hormones, hormone receptors, the sources (i.e. cells, tissues and organs) where the hormones are synthesized and secreted, and where the respective receptors are expressed. The database focuses on the regulatory relations between them. An elementary communication is displayed as a causal link from a cell that secretes a particular hormone to those cells which express the corresponding hormone receptor and respond to the hormone. Whenever expression, synthesis and/or secretion of another hormone are part of this response, it renders the corresponding cell an internal node of the resulting network. This intercellular communication network coordinates the function of different organs. Therefore, the database covers the hierarchy of cellular organization of tissues and organs as it has been modeled in the Cytomer ontology, which has now been directly embedded into EndoNet. The user can query the database; the results can be used to visualize the intercellular information flow. A newly implemented hormone classification enables to browse the database and may be used as alternative entry point. EndoNet is accessible at: http://endonet.bioinf.med.uni-goettingen.de/."],["dc.identifier.doi","10.1093/nar/gkm940"],["dc.identifier.isi","000252545400124"],["dc.identifier.pmid","18045786"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4133"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55550"],["dc.notes.intern","Merged from goescholar"],["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.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","EndoNet: an information resource about regulatory networks of cell-to-cell communication"],["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","S15"],["dc.bibliographiccitation.journal","BMC Systems Biology"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Li, Jie"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T09:02:19Z"],["dc.date.available","2018-11-07T09:02:19Z"],["dc.date.issued","2012"],["dc.description.abstract","Background: Transcriptional networks of higher eukaryotes are difficult to obtain. Available experimental data from conventional approaches are sporadic, while those generated with modern high-throughput technologies are biased. Computational predictions are generally perceived as being flooded with high rates of false positives. New concepts about the structure of regulatory regions and the function of master regulator sites may provide a way out of this dilemma. Methods: We combined promoter scanning with positional weight matrices with a 4-genome conservativity analysis to predict high-affinity, highly conserved transcription factor (TF) binding sites and to infer TF-target gene relations. They were expanded to paralogous TFs and filtered for tissue-specific expression patterns to obtain a reference transcriptional network (RTN) as well as tissue-specific transcriptional networks (TTNs). Results: When validated with experimental data sets, the predictions done showed the expected trends of true positive and true negative predictions, resulting in satisfying sensitivity and specificity characteristics. This also proved that confining the network reconstruction to the 1% top-ranking TF-target predictions gives rise to networks with expected degree distributions. Their expansion to paralogous TFs enriches them by tissue-specific regulators, providing a reasonable basis to reconstruct tissue-specific transcriptional networks. Conclusions: The concept of master regulator or seed sites provides a reasonable starting point to select predicted TF-target relations, which, together with a paralogous expansion, allow for reconstruction of tissue-specific transcriptional networks."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2012"],["dc.identifier.doi","10.1186/1752-0509-6-S2-S15"],["dc.identifier.isi","000312991400015"],["dc.identifier.pmid","23282021"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8461"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24656"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.publisher.place","London"],["dc.relation.conference","23rd International Conference on Genome Informatics (GIW)"],["dc.relation.eventlocation","Tainan, TAIWAN"],["dc.relation.issn","1752-0509"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks"],["dc.type","conference_paper"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]