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.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","225"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Gueltas, Mehmet"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Tuysuz, Nesrin"],["dc.contributor.author","Waack, Stephan"],["dc.date.accessioned","2018-11-07T09:05:54Z"],["dc.date.available","2018-11-07T09:05:54Z"],["dc.date.issued","2012"],["dc.description.abstract","Background: The detection of significant compensatory mutation signals in multiple sequence alignments (MSAs) is often complicated by noise. A challenging problem in bioinformatics is remains the separation of significant signals between two or more non-conserved residue sites from the phylogenetic noise and unrelated pair signals. Determination of these non-conserved residue sites is as important as the recognition of strictly conserved positions for understanding of the structural basis of protein functions and identification of functionally important residue regions. In this study, we developed a new method, the Coupled Mutation Finder (CMF) quantifying the phylogenetic noise for the detection of compensatory mutations. Results: To demonstrate the effectiveness of this method, we analyzed essential sites of two human proteins: epidermal growth factor receptor (EGFR) and glucokinase (GCK). Our results suggest that the CMF is able to separate significant compensatory mutation signals from the phylogenetic noise and unrelated pair signals. The vast majority of compensatory mutation sites found by the CMF are related to essential sites of both proteins and they are likely to affect protein stability or functionality. Conclusions: The CMF is a new method, which includes an MSA-specific statistical model based on multiple testing procedures that quantify the error made in terms of the false discovery rate and a novel entropy-based metric to upscale BLOSUM62 dissimilar compensatory mutations. Therefore, it is a helpful tool to predict and investigate compensatory mutation sites of structural or functional importance in proteins. We suggest that the CMF could be used as a novel automated function prediction tool that is required for a better understanding of the structural basis of proteins. The CMF server is freely accessible at http://cmf.bioinf.med.uni-goettingen.de."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2012"],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [DFG:WA 766/7-1]"],["dc.identifier.doi","10.1186/1471-2105-13-225"],["dc.identifier.fs","591912"],["dc.identifier.isi","000315639200001"],["dc.identifier.pmid","22963049"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8541"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25431"],["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","CC BY 2.0"],["dc.rights.access","openAccess"],["dc.rights.holder","Mehmet Gültas et al.; licensee BioMed Central Ltd."],["dc.rights.uri","http://creativecommons.org/licenses/by/2.0"],["dc.title","Coupled mutation finder: A new entropy-based method quantifying phylogenetic noise for the detection of compensatory mutations"],["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","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","115"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Pharmacogenomics"],["dc.bibliographiccitation.lastpage","127"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Dalila, Nawar"],["dc.contributor.author","Brockmoeller, Juergen"],["dc.contributor.author","Tzvetkov, Mladen Vassilev"],["dc.contributor.author","Schirmer, Markus"],["dc.contributor.author","Haubrock, Martin"],["dc.contributor.author","Vormfelde, Stefan Viktor"],["dc.date.accessioned","2018-11-07T10:03:50Z"],["dc.date.available","2018-11-07T10:03:50Z"],["dc.date.issued","2015"],["dc.description.abstract","Aim: Polymorphisms in the mineralocorticoid receptor may affect urinary sodium and potassium excretion. We investigated polymorphisms in the MR gene in relation to urinary electrolyte excretion in two separate studies. Patients & methods: The genotype-phenotype association was studied in healthy volunteers after single doses of bumetanide, furosemide, torsemide, hydrochlorothiazide, triamterene and after NaCl restriction. Results: High potassium excretion under all conditions except torsemide, and high NaCl excretion after bumetanide and furosemide were associated with the A allele of the intron-3 polymorphism (rs3857080). This polymorphism explained 5-10% of the functional variation and in vitro, rs3857080 affected DNA binding of the transcription factor LHX4. Conclusion: rs3857080 may be a promising new candidate for research in cardiac and renal disorders and on antialdosteronergic drugs like spironolactone. Original submitted 23 June 2014; Revision submitted 5 November 2014"],["dc.identifier.doi","10.2217/pgs.14.163"],["dc.identifier.isi","000348455400003"],["dc.identifier.pmid","25616098"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38561"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Future Medicine Ltd"],["dc.relation.issn","1744-8042"],["dc.relation.issn","1462-2416"],["dc.title","Impact of mineralocorticoid receptor polymorphisms on urinary electrolyte excretion with and without diuretic drugs"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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"]]