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The Genetic Code Revisited: Inner-to-Outer Map, 2D-Gray Map, and World-Map Genetic Representations

  • H. M. de Oliveira
  • N. S. Santos-Magalhães
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3124)

Abstract

How to represent the genetic code? Despite the fact that it is extensively known, the DNA mapping into proteins remains as one of the relevant discoveries of genetics. However, modern genomic signal processing usually requires converting symbolic-DNA strings into complex-valued signals in order to take full advantage of a broad variety of digital processing techniques. The genetic code is revisited in this paper, addressing alternative representations for it, which can be worthy for genomic signal processing. Three original representations are discussed. The inner-to-outer map builds on the unbalanced role of nucleotides of a ’codon’ and it seems to be suitable for handling information-theory-based matter. The two-dimensional-Gray map representation is offered as a mathematically structured map that can help interpreting spectrograms or scalograms. Finally, the world-map representation for the genetic code is investigated, which can particularly be valuable for educational purposes – besides furnishing plenty of room for application of distance-based algorithms.

Keywords

Genetic Code Signal Processing Technique Genomic Signal Voronoi Region Standard Genetic Code 
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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References

  1. 1.
    Int. Human Genome Sequencing Consortium: Initial Sequencing and Analysis of the Human Genome. Nature 409, 860–921 (2001)Google Scholar
  2. 2.
    NIH (2004) National Center for Biotechnology Information, GenBank [on line], Available 05/05/04: http://www.ncbi.nlm.nih.gov/Genomes/index.html
  3. 3.
    Zhang, X.-Y., Chen, F., Zhank, Y.-T., Agner, S.C., Akay, M., Lu, Z.-H., Waye, M.M.Y., Tsui, S.K.-W.: Signal Processing Techniques in Genomic Engineering. Proc. of the IEEE 90, 1822–1833 (2002)CrossRefGoogle Scholar
  4. 4.
    Nelson, D.L., Cox, M.M.: Lehninger Principles of Biochemistry, 3rd edn. Worth Publishers, New York (2000)Google Scholar
  5. 5.
    Alberts, B., Bray, D., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: Essential Cell Biology. Garland Pub., New York (1998)Google Scholar
  6. 6.
    Battail, G.: Is Biological Evolution Relevant to Information Theory and Coding? In: Proc. Int. Symp. on Coding Theory and Applications, ISCTA 2001, Ambleside UK, pp. 343–351 (2001)Google Scholar
  7. 7.
    Anastassiou, D.: Genomic Signal Processing. IEEE Signal Processing Mag., 8–20 (2001)Google Scholar
  8. 8.
    Cristea, P.: Real and Complex Genomic Signals. In: Int. Conf. on DSP, vol. 2, pp. 543–546 (2002)Google Scholar
  9. 9.
    Battail, G.: Does Information Theory Explain Biological Evolution? Europhysics Letters 40, 343–348 (1997)CrossRefGoogle Scholar
  10. 10.
    de Oliveira, H.M., Battail, G.: Generalized 2-dimensional Cross Constellations and the Opportunistic Secondary Channel. Annales des Télécommunications 47, 202–213 (1992)zbMATHGoogle Scholar
  11. 11.
    Tsonis, A.A., Kumar, P., Elsner, J.B., Tsonis, P.A.: Wavelet Analysis of DNA Sequences. Physical Review E 53, 1828–1834 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • H. M. de Oliveira
    • 1
  • N. S. Santos-Magalhães
    • 2
  1. 1.Departamento de Eletrônica e SistemasGrupo de Processamento de Sinais CaixaRecifeBrazil
  2. 2.Departamento de Bioquímica–Laboratório de Imunologia Keizo-Asami–LIKAUniversidade Federal de PernambucoRecifeBrazil

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