Wingender, Edgar

[["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","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","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"]]

[["dc.bibliographiccitation.artnumber","e0160803"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Hartmann, Fabian"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T10:10:11Z"],["dc.date.available","2018-11-07T10:10:11Z"],["dc.date.issued","2016"],["dc.description.abstract","ChIP-seq experiments detect the chromatin occupancy of known transcription factors in a genome-wide fashion. The comparisons of several species-specific ChIP-seq libraries done for different transcription factors have revealed a complex combinatorial and contextspecific co-localization behavior for the identified binding regions. In this study we have investigated human derived ChIP-seq data to identify common cis-regulatory principles for the human transcription factor c-Fos. We found that in four different cell lines, c-Fos targeted proximal and distal genomic intervals show prevalences for either AP-1 motifs or CCAAT boxes as known binding motifs for the transcription factor NF-Y, and thereby act in a mutually exclusive manner. For proximal regions of co-localized c-Fos and NF-YB binding, we gathered evidence that a characteristic configuration of repeating CCAAT motifs may be responsible for attracting c-Fos, probably provided by a nearby AP-1 bound enhancer. Our results suggest a novel regulatory function of NF-Y in gene-proximal regions. Specific CCAAT dimer repeats bound by the transcription factor NF-Y define this novel cis-regulatory module. Based on this behavior we propose a new enhancer promoter interaction model based on AP-1 motif defined enhancers which interact with CCAATbox characterized promoter regions."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2016"],["dc.identifier.doi","10.1371/journal.pone.0160803"],["dc.identifier.isi","000381382100025"],["dc.identifier.pmid","27517874"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13702"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39809"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class"],["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","e0231326"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Daou, Rayan"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.editor","Mantovani, Roberto"],["dc.date.accessioned","2021-04-14T08:26:29Z"],["dc.date.available","2021-04-14T08:26:29Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1371/journal.pone.0231326"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17416"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/81960"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.relation.eissn","1932-6203"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Constructing temporal regulatory cascades in the context of development and cell differentiation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","UNSP 9"],["dc.bibliographiccitation.journal","Algorithms for Molecular Biology"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Bhar, Anirban"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Mukhopadhyay, Anirban"],["dc.contributor.author","Maulik, Ujjwal"],["dc.contributor.author","Bandyopadhyay, Sanghamitra"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T09:26:59Z"],["dc.date.available","2018-11-07T09:26:59Z"],["dc.date.issued","2013"],["dc.description.abstract","Background: Estrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm delta-TRIMAX that aims to find genes that are coexpressed over subset of samples across a subset of time points. Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold delta. Results: We have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor alpha. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1. Conclusions: Thus our integrative approach provides insights into breast cancer prognosis."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2013"],["dc.description.sponsorship","Erasmus Mundus Eurindia Project"],["dc.identifier.doi","10.1186/1748-7188-8-9"],["dc.identifier.isi","000319729700001"],["dc.identifier.pmid","23521829"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9067"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30428"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1748-7188"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell"],["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","D343"],["dc.bibliographiccitation.issue","D1"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","D347"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Schoeps, Torsten"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Krull, Mathias"],["dc.contributor.author","Dönitz, Jürgen"],["dc.date.accessioned","2019-07-09T11:45:00Z"],["dc.date.available","2019-07-09T11:45:00Z"],["dc.date.issued","2018"],["dc.description.abstract","TFClass is a resource that classifies eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs), available online at http://tfclass.bioinf.med.uni-goettingen.de. The classification scheme of TFClass was originally derived for human TFs and is expanded here to the whole taxonomic class of mammalia. Combining information from different resources, checking manually the retrieved mammalian TFs sequences and applying extensive phylogenetic analyses, >39 000 TFs from up to 41 mammalian species were assigned to the Superclasses, Classes, Families and Subfamilies of TFClass. As a result, TFClass now provides the corresponding sequence collection in FASTA format, sequence logos and phylogenetic trees at different classification levels, predicted TF binding sites for human, mouse, dog and cow genomes as well as links to several external databases. In particular, all those TFs that are also documented in the TRANSFAC® database (FACTOR table) have been linked and can be freely accessed. TRANSFAC® FACTOR can also be queried through an own search interface."],["dc.identifier.doi","10.1093/nar/gkx987"],["dc.identifier.pmid","29087517"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14998"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59137"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1362-4962"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","TFClass: expanding the classification of human transcription factors to their mammalian orthologs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","374"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nucleic Acids Research"],["dc.bibliographiccitation.lastpage","378"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Matys, V."],["dc.contributor.author","Haubrock, M."],["dc.contributor.author","Wingender, E."],["dc.date.accessioned","2022-06-08T07:59:10Z"],["dc.date.available","2022-06-08T07:59:10Z"],["dc.date.issued","2003"],["dc.description.abstract","The TRANSFAC® database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER® database on anatomical structures and developmental stages. The functionality of MatchTM, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with MatchTM Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC® public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC® Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER® free download versions are available at http://www.biobase.de:8080/index.html."],["dc.identifier.doi","10.1093/nar/gkg108"],["dc.identifier.fs","12171"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?goescholar/4111"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110656"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1362-4962"],["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","TRANSFAC(R): transcriptional regulation, from patterns to profiles"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","200"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Bhar, Anirban"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Mukhopadhyay, Anirban"],["dc.contributor.author","Wingender, Edgar"],["dc.date.accessioned","2018-11-07T09:55:40Z"],["dc.date.available","2018-11-07T09:55:40Z"],["dc.date.issued","2015"],["dc.description.abstract","Background: Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits. Results: In this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm delta-TRIMAX. Its aim is to make optimal use of delta-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA-delta-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation. Conclusions: EMOA-delta-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz."],["dc.description.sponsorship","Open-Access Publikationsfunds 2015"],["dc.identifier.doi","10.1186/s12859-015-0635-8"],["dc.identifier.isi","000357248500001"],["dc.identifier.pmid","26108437"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12283"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36805"],["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","Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes"],["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","199"],["dc.bibliographiccitation.journal","BMC Systems Biology"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Wang, J."],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Cao, Kun-Ming"],["dc.contributor.author","Hua, X. U."],["dc.contributor.author","Zhang, C."],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Li, Jie"],["dc.date.accessioned","2018-11-07T08:48:50Z"],["dc.date.available","2018-11-07T08:48:50Z"],["dc.date.issued","2011"],["dc.description.abstract","Background: MicroRNA (miRNA) is a class of small RNAs of similar to 22nt which play essential roles in many crucial biological processes and numerous human diseases at post-transcriptional level of gene expression. It has been revealed that miRNA genes tend to be clustered, and the miRNAs organized into one cluster are usually transcribed coordinately. This implies a coordinated regulation mode exerted by clustered miRNAs. However, how the clustered miRNAs coordinate their regulations on large scale gene expression is still unclear. Results: We constructed the miRNA-transcription factor regulatory network that contains the interactions between transcription factors (TFs), miRNAs and non-TF protein-coding genes, and made a genome-wide study on the regulatory coordination of clustered miRNAs. We found that there are two types of miRNA clusters, i.e. homo-clusters that contain miRNAs of the same family and hetero-clusters that contain miRNAs of various families. In general, the homo-clustered as well as the hetero-clustered miRNAs both exhibit coordinated regulation since the miRNAs belonging to one cluster tend to be involved in the same network module, which performs a relatively isolated biological function. However, the homo-clustered miRNAs show a direct regulatory coordination that is realized by one-step regulation (i.e. the direct regulation of the coordinated targets), whereas the hetero-clustered miRNAs show an indirect regulatory coordination that is realized by a regulation comprising at least three steps (e.g. the regulation on the coordinated targets by a miRNA through a sequential action of two TFs). The direct and indirect regulation target different categories of genes, the former predominantly regulating genes involved in emergent responses, the latter targeting genes that imply long-term effects. Conclusion: The genomic clustering of miRNAs is closely related to the coordinated regulation in the gene regulatory network. The pattern of regulatory coordination is dependent on the composition of the miRNA cluster. The homo-clustered miRNAs mainly coordinate their regulation rapidly, while the hetero-clustered miRNAs exert control with a delay. The diverse pattern of regulatory coordination suggests distinct roles of the homo-clustered and the hetero-clustered miRNAs in biological processes."],["dc.identifier.doi","10.1186/1752-0509-5-199"],["dc.identifier.isi","000300295900001"],["dc.identifier.pmid","22176772"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7063"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/21316"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1752-0509"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Regulatory coordination of clustered microRNAs based on microRNA-transcription factor regulatory network"],["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"]]