The 10th IASTED International Conference on
Signal and Image Processing
~SIP 2008~

August 18 – 20, 2008
Kailua-Kona, Hawaii, USA

TUTORIAL SESSION

Nucleotide Genomic Signal Representation and Processing

Paul Dan CRISTEA
Bio-Medical Engineering Center
"Politehnica" University of Bucharest
Bucharest, Romania
pcristea@dsp.pub.ro

Abstract

In previous papers3,4 we have developed a genomic signals methodology, based on the conversion of symbolic nucleotide sequences13 into genomic signals. There have been several other attempts to represent nucleotide sequences as digital signals10,11, most using some key properties attached to nitrogeneous bases. The main feature of the approach that will be presented and discussed in this tutorial is to be unbiased, adequate for different classes of problems related to DNA analysis. Specifically, the methodology can be used both for the analysis of large scale features of genomic sequences, maintained over distances of 106 - 108 base pairs7,9, revealing hidden features and ancestral structures of nucleotide sequences8, as well as for the study of the variability of pathogens5,6, particularly in the context of the development of the resistance to treatment12,15. This aspect is important for the fast diagnosis and early assessment of drug efficiency, allowing a simple and systematic use of the recent advances in molecular medicine to help clinical decisions1,14,16. The striking regularities of the digital genomic signals reveal surprising restrictions in the distribution of nucleotides and pairs of nucleotides along DNA sequences in both prokaryotes and eukaryotes2,3,4. Structurally, a chromosome appears to be more than a plain text, as it corresponds not only to a semantic and a grammar, but it also satisfies symmetry restrictions evoking the rhythm and rhyme in poems. These regularities allow to use prediction in nucleotide sequences using a methodology close to time series prediction and to easily identify exogenous inserts and horizontal transports in the genomes of prokaryotes4.

Based on the phase analysis of complex genomic signals, an interesting model of the "patchy" longitudinal structure of chromosomal DNA can be derived. The current structure derives from an putative ancestral highly ordered chromosome structure, as a result of processes linked to species separation and protection at molecular level. The complex genomic signal phase can be linked to molecular potentials corresponding to the unbalanced hydrogen bonds of nucleotides. Such potentials can describe the interaction of a DNA segment with proteins and with other DNA segments in processes like replication, transcription or crossover. In particular, this model can explain the functioning of DNA polymerase as a "brownian machine" during replication, by the conversion of random molecular movements into an ordered gradual advance of the enzyme along the DNA strand3.

Biography of the Presenter

Paul Cristea graduated the Faculty of Electronics of the "Politehnica" University of Bucharest in 1962, the Faculty of Physics of the University of Bucharest in 1969, and defended his Ph.D. in Physics at PUB in 1970. His research and teaching activities have addressed a wide range of fields related to Electrical and Electronics Engineering, including topics such as Genomic Signals, Signal and Image processing, Intelligent E-Learning Systems, Circuit Theory and Design, Computer Assisted Medical Equipment, Advanced Electrical Batteries and other. Prof. Cristea is author or co-author of more then 130 published papers, 11 patents, and contributed to more then 20 books. He gave keynote or plenary talks to many international conferences, and was an invited professor to several European and US universities. Prof. Cristea is a Corresponding Member of the Romanian Academy, the director of the Biomedical Engineering Center of PUB, and the director of the Romanian Bioinformatics Society.

Selected references

  1. J.C. Camus, Melinda J. Pryer, Claudine Medique, S.T. Cole, "Re-annotation of the genome sequence of Mycobacterium Tuberculosis H37Rv", Microbiology, vol. 148, 2967-2973 (2002).
  2. E. Chargaff, "Structure and function of nucleic acids as cell constituents," Fed. Proc., vol. 10, 1951, 654–659.
  3. P. D. Cristea, "Representation and Analysis of DNA sequences", chapter 1, pp.15-65 in Genomic Signal Processing and Statistics, edited by: E.G. Dougherty, I. Shmulevici, Jie Chen, Z. Jane Wang, Book Series on Signal Processing and Communications, Hindawi Publishing Corporation, 2005.
  4. P. D. Cristea, Rodica Tuduce, J. Cornelis, R. Deklerck, I. Nastac, M. Andrei, "Signal Representation and Processing of Nucleotide Sequences", IEEE 7th International Symposium on Bioinformatics & Bioengineering (IEEE BIBE 2007), Harvard Medical School, Boston, USA, October, 14-17, 2007.
  5. P. D. Cristea, "Genomic Signal Analysis of Mycobacterium tuberculosis," Progress in Biomedical Optics and Imaging, Proc. of SPIE, vol. 6447, ?C1 – C8 (2007).
  6. P. D. Cristea, D. Otelea, Rodica Tuduce, "Study of HIV Variability Based on Genomic Signal Analysis of Protease and Reverse Transcriptase Genes", EMBC'05 - 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, September 1-4, 2005, Shanghai, China, IEEE Catalog no: 05CH37611; ISBN: 0-7803-8740-6, ISSN: 1094-687X, paper 1845.
  7. P. D. Cristea, "Invariants of DNA Genomic Signals", in Biomedical Applications of Micro- and Nanoengineering II, edited by D.V. Nicolau, Proceedings of SPIE, vol. 5651, SPIE, Belingham, WA, 115-125 (2005).
  8. P. D. Cristea, "Genomic Signals of Re-Oriented ORFs", EURASIP – Journal on Applied Signal Processing, Special Issue on Genomic Signal Processing, vol. 2004, no.1, 132-137 (2004).
  9. P. D. Cristea, "Large Scale Features in DNA Genomic Signals", ELSEVIER, Signal Processing, Special Issue on Genomic Signal Processing, 83, 871-888 (2003).
  10. L. Frappat, P. Sorba, A. Sciarrino, "A crystal base for the genetic code", Physics Letters A, 1-3, (250), 214-221 (1998).
  11. M. X. He, S. V. Petoukhov, P. E. Ricci, "Genetic Code, Hamming Distance and Stochastic Matrices", Bulletin of Mathematical Biology 66, 1405–1421 (2004).
  12. J. M. Musser, "Antimicrobial Agent Resistance in Mycobacteria: Molecular Genetic Insights", Clinical Microbiology Reviews, 496–514, Oct., (1995).
  13. NIH - National Centre for Biotechnology Information, National Institutes of Health, National Library of Medicine, (NCBI/GenBank), 2007, http://www.ncbi.nlm.nih.gov/
  14. M. J. Torres et al, "Rapid Detection of Resistance Associated Mutations in Mycobacterium tuberculosis by LightCycler PCR", J. Clin. Microbiol., 38, 3194-3199 (2000).
  15. I.C. Shamputa, L. Rigouts, F. Portaels, "Molecular genetic methods for diagnosis and antibiotic resistance detection of mycobacteria from clinical specimens," APMIS, vol.112, 2004, . 728–752.
  16. WHO, Global tuberculosis control: surveillance, planning, financing. Geneva: WHO, Report WHO/HTM/TB/2005, 349.