Meckbach, Cornelia

[["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.artnumber","33"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Frontiers in Genetics"],["dc.bibliographiccitation.lastpage","17"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Zeidler, Sebastian"],["dc.contributor.author","Meckbach, Cornelia"],["dc.contributor.author","Tacke, Rebecca"],["dc.contributor.author","Raad, Farah S."],["dc.contributor.author","Roa, Angelica"],["dc.contributor.author","Uchida, Shizuka"],["dc.contributor.author","Zimmermann, Wolfram-Hubertus"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2017-09-07T11:54:27Z"],["dc.date.available","2017-09-07T11:54:27Z"],["dc.date.issued","2016"],["dc.description.abstract","Transcription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this study was to analyze co-occurring transcription factor binding sites (TFBSs) in a time series dataset from a new cell-culture model of human heart muscle development in order to identify common as well as specific co-occurring TFBS pairs in the promoter regions of regulated genes which can be essential to enhance cardiac tissue developmental processes. To this end, we separated available RNAseq dataset into five temporally defined groups: (i) mesoderm induction stage; (ii) early cardiac specification stage; (iii) late cardiac specification stage; (iv) early cardiac maturation stage; (v) late cardiac maturation stage, where each of these stages is characterized by unique differentially expressed genes (DEGs). To identify TFBS pairs for each stage, we applied the MatrixCatch algorithm, which is a successful method to deduce experimentally described TFBS pairs in the promoters of the DEGs. Although DEGs in each stage are distinct, our results show that the TFBS pair networks predicted by MatrixCatch for all stages are quite similar. Thus, we extend the results of MatrixCatch utilizing a Markov clustering algorithm (MCL) to perform network analysis. Using our extended approach, we are able to separate the TFBS pair networks in several clusters to highlight stage-specific co-occurences between TFBSs. Our approach has revealed clusters that are either common (NFAT or HMGIY clusters) or specific (SMAD or AP-1 clusters) for the individual stages. Several of these clusters are likely to play an important role during the cardiomyogenesis. Further, we have shown that the related TFs of TFBSs in the clusters indicate potential synergistic or antagonistic interactions to switch between different stages. Additionally, our results suggest that cardiomyogenesis follows the hourglass model which was already proven for Arabidopsis and some vertebrates. This investigation helps us to get a better understanding of how each stage of cardiomyogenesis is affected by different combination of TFs. Such knowledge may help to understand basic principles of stem cell differentiation into cardiomyocytes"],["dc.identifier.doi","10.3389/fgene.2016.00033"],["dc.identifier.gro","3145188"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13173"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/2896"],["dc.language.iso","en"],["dc.notes.intern","Crossref Import"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-8021"],["dc.relation.issn","1664-8021"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","96"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Gueltas, Mehmet"],["dc.contributor.author","Duezguen, Guencel"],["dc.contributor.author","Herzog, Sebastian K."],["dc.contributor.author","Jaeger, Sven Joachim"],["dc.contributor.author","Meckbach, Cornelia"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Waack, Stephan"],["dc.date.accessioned","2018-11-07T09:41:23Z"],["dc.date.available","2018-11-07T09:41:23Z"],["dc.date.issued","2014"],["dc.description.abstract","Background: The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. Results: The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs. Conclusions: QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF's algorithm, we leveraged Compute Unified Device Architecture (CUDA)."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft (DFG) [WA 766/7-1]"],["dc.identifier.doi","10.1186/1471-2105-15-96"],["dc.identifier.isi","000335348300001"],["dc.identifier.pmid","24694117"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10109"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33716"],["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 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Quantum coupled mutation finder: predicting functionally or structurally important sites in proteins using quantum Jensen-Shannon divergence and CUDA programming"],["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","189"],["dc.bibliographiccitation.journal","Frontiers in genetics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Meckbach, Cornelia"],["dc.contributor.author","Wingender, Edgar"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2019-07-09T11:45:38Z"],["dc.date.available","2019-07-09T11:45:38Z"],["dc.date.issued","2018"],["dc.description.abstract","Today, it is well-known that in eukaryotic cells the complex interplay of transcription factors (TFs) bound to the DNA of promoters and enhancers is the basis for precise and specific control of transcription. Computational methods have been developed for the identification of potentially cooperating TFs through the co-occurrence of their binding sites (TFBSs). One challenge of these methods is the differentiation of TFBS pairs that are specific for a given sequence set from those that are ubiquitously appearing, rendering the results highly dependent on the choice of a proper background set. Here, we present an extension of our previous PC-TraFF approach that estimates the background co-occurrence of any TF pair by preserving the (oligo-) nucleotide composition and, thus, the core of TFBSs in the sequences of interest. Applying our approach to a simulated data set with implanted TFBS pairs, we could successfully identify them as sequence-set specific under a variety of conditions. When we analyzed the gene expression data sets of five breast cancer associated subtypes, the number of overlapping pairs could be dramatically reduced in comparison to our previous approach. As a result, we could identify potentially cooperating transcriptional regulators that are characteristic for each of the five breast cancer subtypes. This indicates that our approach is able to discriminate specific potential TF cooperations against ubiquitously occurring combinations. The results obtained with our method may help to understand the genetic programs governing specific biological processes such as the development of different tumor types."],["dc.identifier.doi","10.3389/fgene.2018.00189"],["dc.identifier.pmid","29896218"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15267"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59271"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-8021"],["dc.relation.issn","1664-8021"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]