Haubrock, Martin

[["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.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","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"]]