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- Misulovin, Ziva, et al.
(författare)
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Association of cohesin and Nipped-B with transcriptionally active regions of the Drosophila melanogaster genome
- 2008
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Ingår i: Chromosoma. - : Springer Science and Business Media LLC. - 0009-5915 .- 1432-0886. ; 117:1, s. 89-102
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Tidskriftsartikel (refereegranskat)abstract
- The cohesin complex is a chromosomal component required for sister chromatid cohesion that is conserved from yeast to man. The similarly conserved Nipped-B protein is needed for cohesin to bind to chromosomes. In higher organisms, Nipped-B and cohesin regulate gene expression and development by unknown mechanisms. Using chromatin immunoprecipitation, we find that Nipped-B and cohesin bind to the same sites throughout the entire non-repetitive Drosophila genome. They preferentially bind transcribed regions and overlap with RNA polymerase II. This contrasts sharply with yeast, where cohesin binds almost exclusively between genes. Differences in cohesin and Nipped-B binding between Drosophila cell lines often correlate with differences in gene expression. For example, cohesin and Nipped-B bind the Abd-B homeobox gene in cells in which it is transcribed, but not in cells in which it is silenced. They bind to the Abd-B transcription unit and downstream regulatory region and thus could regulate both transcriptional elongation and activation. We posit that transcription facilitates cohesin binding, perhaps by unfolding chromatin, and that Nipped-B then regulates gene expression by controlling cohesin dynamics. These mechanisms are likely involved in the etiology of Cornelia de Lange syndrome, in which mutation of one copy of the NIPBL gene encoding the human Nipped-B ortholog causes diverse structural and mental birth defects.
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- Rübel, Oliver, et al.
(författare)
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Integrating data clustering and visualization for the analysis of 3D gene expression data
- 2010
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - 1545-5963 .- 1557-9964. ; 7:1, s. 64-79
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Tidskriftsartikel (refereegranskat)abstract
- The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex data sets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss 1) the integration of data clustering and visualization into one framework, 2) the application of data clustering to 3D gene expression data, 3) the evaluation of the number of clusters k in the context of 3D gene expression clustering, and 4) the improvement of overall analysis quality via dedicated postprocessing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.
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- Weber, Gunther H., et al.
(författare)
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Visual exploration of three-dimensional gene expression using physical views and linked abstract views
- 2009
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - 1545-5963 .- 1557-9964. ; 6:2, s. 296-309
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Tidskriftsartikel (refereegranskat)abstract
- During animal development, complex patterns of gene expression provide positional information within the embryo. To better understand the underlying gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed methods that support quantitative computational analysis of three-dimensional (3D) gene expression in early Drosophila embryos at cellular resolution. We introduce PointCloudXplore (PCX), an interactive visualization tool that supports visual exploration of relationships between different genes' expression using a combination of established visualization techniques. Two aspects of gene expression are of particular interest: 1) gene expression patterns defined by the spatial locations of cells expressing a gene and 2) relationships between the expression levels of multiple genes. PCX provides users with two corresponding classes of data views: 1) Physical Views based on the spatial relationships of cells in the embryo and 2) Abstract Views that discard spatial information and plot expression levels of multiple genes with respect to each other. Cell Selectors highlight data associated with subsets of embryo cells within a View. Using linking, these selected cells can be viewed in multiple representations. We describe PCX as a 3D gene expression visualization tool and provide examples of how it has been used by BDTNP biologists to generate new hypotheses.
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