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Yeast Naked DNA Spatial Organization Predisposes to Transcriptional Regulation

  • Oriane Matte-Tailliez
  • Joan Hérisson
  • Nicolas Ferey
  • Olivier Magneau
  • Pierre Emmanuel Gros
  • François Képès
  • Rachid Gherbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)

Abstract

This paper presents a new structural-based approach to explore spatial organization of naked DNA on a whole chromosome sequence and its biological features related to gene regulation. A 3D trajectory representation on full-length yeast chromosomes based on Bolshoy’s conformation model is discussed. These trajectories are predicted by our visualizing system ADN-Viewer. Observations show interesting geometric properties of chromosomes dealing with variability of 3D structures and the fact that regions linearly distant could be spatially close. These new observed phenomena are correlated then with biological considerations. In particular, transcriptional co-regulation of the data of Lee et al., 2002 are exploited. A characterization parameter (RLS), ratio of linear distance and 3D one, was computed for each couple of genes. The co-regulated genes are found to be either linearly distant and spatially close, or linearly close. The co-regulated genes arranged in 1D-clusters could be detected directly in raw data. But, our model offers new predictions of co-regulated genes thanks to 3D-clusters. Then, we concluded that yeast naked DNA spatial organization seems to predispose to transcriptional regulation.

Keywords

Linear Distance Transcriptional Regulatory Network Virtual Reality Platform Interesting Geometric Property Sphere Digit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oriane Matte-Tailliez
    • 1
    • 2
  • Joan Hérisson
    • 2
  • Nicolas Ferey
    • 2
  • Olivier Magneau
    • 3
  • Pierre Emmanuel Gros
    • 2
  • François Képès
    • 4
  • Rachid Gherbi
    • 2
  1. 1.Inference and Learning, LRIUMR CNRS 8326, Université Paris 11Orsay CedexFrance
  2. 2.Bioinformatics Team, LIMSI & IBISC CNRSGenopole Evry
  3. 3.VENISE transversal action on V&AR, LIMSI-CNRSOrsay CedexFrance
  4. 4.Epigenomics project & Atelier de Génomique Cognitive, CNRS UMR 8071EvryFrance

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