Institut für Medizinische Bioinformatik

[["dc.bibliographiccitation.artnumber","014026"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Physical Review. D"],["dc.bibliographiccitation.volume","89"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Geng, L.-S."],["dc.contributor.author","Weise, W."],["dc.date.accessioned","2021-09-17T09:47:43Z"],["dc.date.available","2021-09-17T09:47:43Z"],["dc.date.issued","2013-09-18"],["dc.description.abstract","Recent lattice QCD simulations of the scattering lengths of Nambu-Goldstone bosons off the $ mesons are studied using unitary chiral perturbation theory. We show that the Lattice QCD data are better described in the covariant formulation than in the heavy-meson formulation. The ^ _{s0}(2317)$ can be dynamically generated from the coupled-channels $ interaction without \\textit{a priori} assumption of its existence. A new renormalization scheme is proposed which manifestly satisfies chiral power counting rules and has well-defined behavior in the infinite heavy-quark mass limit. Using this scheme we predict the heavy-quark spin and flavor symmetry counterparts of the ^ _{s0}(2317)$."],["dc.identifier.arxiv","1309.4743v2"],["dc.identifier.doi","10.1103/PhysRevD.89.014026"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89646"],["dc.relation.issn","1550-7998"],["dc.relation.issn","1550-2368"],["dc.title","Scattering lengths of Nambu-Goldstone bosons off $ mesons and dynamically generated heavy-light mesons"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","390"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Physics Letters. B"],["dc.bibliographiccitation.lastpage","395"],["dc.bibliographiccitation.volume","696"],["dc.contributor.author","Geng, L. S."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Weise, W."],["dc.date.accessioned","2021-09-17T09:46:47Z"],["dc.date.available","2021-09-17T09:46:47Z"],["dc.date.issued","2010-12-03"],["dc.description.abstract","We study the light quark mass dependence of the $ and $ meson decay constants, $ and {D_s}$, using a covariant formulation of chiral perturbation theory ($\\chi) at next-to-next-to-leading order (NNLO). Using the HPQCD lattice results for the decay constants as a benchmark we show that covariant $\\chi can describe the HPQCD results better than heavy meson $\\chi (HM$\\chi) at both NLO and NNLO. Within the same framework, taking into account sub-leading (/m_Q$, with $ the heavy quark mass) corrections to the values of the low-energy constants and employing the lattice QCD results for {BB^ \\pi}$, we estimate the ratio of {B_s}/f_B$ to be .22^{+0.05}_{-0.04}$, which agrees well with the HPQCD result .226(26)$."],["dc.identifier.arxiv","1012.0666v1"],["dc.identifier.doi","10.1016/j.physletb.2010.12.060"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89635"],["dc.relation.issn","0370-2693"],["dc.title","Light quark mass dependence of the $ and $ decay constants"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["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","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.firstpage","219"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Bioinformatics"],["dc.bibliographiccitation.lastpage","226"],["dc.bibliographiccitation.volume","33"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Rehberg, T."],["dc.contributor.author","Zacharias, H. U."],["dc.contributor.author","Stämmler, F."],["dc.contributor.author","Dettmer, K."],["dc.contributor.author","Weber, D."],["dc.contributor.author","Hiergeist, A."],["dc.contributor.author","Gessner, A."],["dc.contributor.author","Holler, E."],["dc.contributor.author","Oefner, P. J."],["dc.contributor.author","Spang, R."],["dc.date.accessioned","2021-09-17T09:47:33Z"],["dc.date.available","2021-09-17T09:47:33Z"],["dc.date.issued","2017"],["dc.description.abstract","In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed."],["dc.identifier.doi","10.1093/bioinformatics/btw598"],["dc.identifier.pmid","27634945"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89644"],["dc.language.iso","en"],["dc.relation.eissn","1367-4811"],["dc.relation.issn","1367-4803"],["dc.relation.issn","1460-2059"],["dc.title","Reference point insensitive molecular data analysis"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["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","453"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","British Journal of Haematology"],["dc.bibliographiccitation.lastpage","456"],["dc.bibliographiccitation.volume","182"],["dc.contributor.author","Cascione, Luciano"],["dc.contributor.author","Rinaldi, Andrea"],["dc.contributor.author","Chiappella, Annalisa"],["dc.contributor.author","Kwee, Ivo"],["dc.contributor.author","Ciccone, Giovannino"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Kohler, Christian"],["dc.contributor.author","Vitolo, Umberto"],["dc.contributor.author","Inghirami, Giorgio"],["dc.contributor.author","Bertoni, Francesco"],["dc.date.accessioned","2021-09-17T09:48:01Z"],["dc.date.available","2021-09-17T09:48:01Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1111/bjh.14817"],["dc.identifier.pmid","28737236"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89650"],["dc.language.iso","en"],["dc.relation.eissn","1365-2141"],["dc.relation.issn","0007-1048"],["dc.title","Diffuse large B cell lymphoma cell of origin by digital expression profiling in the REAL07 Phase 1-2 study"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","116"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","British Journal of Haematology"],["dc.bibliographiccitation.lastpage","119"],["dc.bibliographiccitation.volume","179"],["dc.contributor.author","Szczepanowski, Monika"],["dc.contributor.author","Lange, Jonas"],["dc.contributor.author","Kohler, Christian W."],["dc.contributor.author","Masque-Soler, Neus"],["dc.contributor.author","Zimmermann, Martin"],["dc.contributor.author","Aukema, Sietse M."],["dc.contributor.author","Altenbuchinger, Michael"],["dc.contributor.author","Rehberg, Thorsten"],["dc.contributor.author","Mahn, Friederike"],["dc.contributor.author","Siebert, Reiner"],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Burkhardt, Birgit"],["dc.contributor.author","Klapper, Wolfram"],["dc.date.accessioned","2021-09-17T09:47:39Z"],["dc.date.available","2021-09-17T09:47:39Z"],["dc.date.issued","2017"],["dc.description.abstract","We present the largest series of diffuse large B-cell lymphoma (DLBCL) in patients younger than 18 years analysed to date by gene expression profiling using Nanostring technology to identify molecular subtypes and fluorescent in situ hybridization for translocations of MYC. We show that the activated B cell-like subtype of DLBCL is exceedingly rare in children and - in contrast to adults- not associated with outcome. Furthermore, we review the current literature and demonstrate that MYC translocations are not more frequent in paediatric compared to adult DLBCL. A prognostic role of MYC in the paediatric age groups seems unlikely."],["dc.identifier.doi","10.1111/bjh.14812"],["dc.identifier.pmid","28643426"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89645"],["dc.language.iso","en"],["dc.relation.eissn","1365-2141"],["dc.relation.issn","0007-1048"],["dc.title","Cell-of-origin classification by gene expression and MYC-rearrangements in diffuse large B-cell lymphoma of children and adolescents"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","3596"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Journal of Proteome Research"],["dc.bibliographiccitation.lastpage","3605"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Zacharias, Helena U."],["dc.contributor.author","Rehberg, Thorsten"],["dc.contributor.author","Mehrl, Sebastian"],["dc.contributor.author","Richtmann, Daniel"],["dc.contributor.author","Wettig, Tilo"],["dc.contributor.author","Oefner, Peter J."],["dc.contributor.author","Spang, Rainer"],["dc.contributor.author","Gronwald, Wolfram"],["dc.contributor.author","Altenbuchinger, Michael"],["dc.date.accessioned","2021-09-17T09:47:02Z"],["dc.date.available","2021-09-17T09:47:02Z"],["dc.date.issued","2017"],["dc.description.abstract","Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to 1H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum ."],["dc.identifier.arxiv","1703.07724v1"],["dc.identifier.doi","10.1021/acs.jproteome.7b00325"],["dc.identifier.pmid","28825821"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89638"],["dc.language.iso","en"],["dc.relation.eissn","1535-3907"],["dc.relation.issn","1535-3893"],["dc.title","Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]