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International Conference on Computer Science, Engineering and Education Applications

ICCSEEA 2019: Advances in Computer Science for Engineering and Education II pp 567–575Cite as

Multidimensional Approach for Analysis of Chromosomes Nucleotide Composition

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 938)

Abstract

A chromosome includes a DNA molecule with a part or all of the genome of an organism. Statistically known that chromosomes nucleotide compositions are different for different biological species. Special comparative method of visualization the chromosomes nucleotide composition of various organisms is described. This analysis is conducted by means of a metric space of binary orthogonal functions taking into account physical-chemical parameters of nitrogenous bases of the genetic code. In consideration that genetic algebra and geometry are connected with a relations purposed in this article algorithms allows to display a statistical chromosome nucleotide composition data in a metric spaces using multidimensional analysis.

Keywords

  • Nucleotide composition
  • DNA symmetries
  • Multidimensional analysis

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References

  1. Petoukhov, S.V., He, M.: Symmetrical Analysis Techniques for Genetic Systems and Bioinformatics: Advanced Patterns and Applications, 271 p. IGI Global, Hershey (2010)

    Google Scholar 

  2. Chargaff, E., Lipshitz, R., Green, C.: Composition of the deoxypentose nucleic acids of four genera of sea-urchin. J. Biol. Chem. 195(1), 155–160 (1952). PMID 14938364

    Google Scholar 

  3. Scaria, T., Christopher, T.: Microarray gene retrieval system based on LFDA and SVM. Int. J. Intell. Syst. Appl. (IJISA) 10(1), 9–15 (2018). https://doi.org/10.5815/ijisa.2018.01.02

  4. Zhang, D., Wang, Y., Tao, W.: Epipolar geometry estimation for wide baseline stereo. Int. J. Eng. Manuf. (IJEM) 2(3), 38–45 (2012). https://doi.org/10.5815/ijem.2012.03.06

    CrossRef  Google Scholar 

  5. Jayapriya, J., Arock, M.: A novel distance metric for aligning multiple sequences using DNA hybridization process. Int. J. Intell. Syst. Appl. (IJISA) 8(6), 40–47 (2016). https://doi.org/10.5815/ijisa.2016.06.05

    CrossRef  Google Scholar 

  6. Hu, Z., Dychka, I., Sulema, Y., Valchuk, Y., Shkurat, O.: Method of medical images similarity estimation based on feature analysis. Int. J. Intell. Syst. Appl. (IJISA) 10(5), 14–22 (2018). https://doi.org/10.5815/ijisa.2018.05.02

    CrossRef  Google Scholar 

  7. Moallem, P., Karimizadeh, A., Yazdchi, M.: Using shape information and dark paths for automatic recognition of touching and overlapping chromosomes in G-band images. Int. J. Image Graphics Sign. Process. (IJIGSP) 5(5), 22–28 (2013). https://doi.org/10.5815/ijigsp.2013.05.03

    CrossRef  Google Scholar 

  8. Feldman, D.P.: “17.4 The chaos game”, Chaos and Fractals: An Elementary Introduction, pp. 178–180. Oxford University Press (2012). ISBN 9780199566440

    Google Scholar 

  9. Petoukhov, S.V.: Genetic coding and united-hypercomplex systems in the models of algebraic biology. Biosystems 158, 31–46 (2017)

    CrossRef  Google Scholar 

  10. Stepanian, I.V., Petoukhov, S.V.: The matrix method of representation, analysis and classification of long genetic sequences. http://arxiv.org/pdf/1310.8469.pdf

  11. Mousa, H.M.: DNA-genetic encryption technique. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 8(7), 1–9 (2016). https://doi.org/10.5815/ijcnis.2016.07.01

    CrossRef  Google Scholar 

  12. Chandel, A., Sood, M.: A genetic approach based solution for seat allocation during counseling for engineering courses. Int. J. Inf. Eng. Electron. Bus. (IJIEEB) 8(1), 29–36 (2016). https://doi.org/10.5815/ijieeb.2016.01.04

    CrossRef  Google Scholar 

  13. Wu, J., Zhang, W., Jiang, R.: Prioritization of candidate nonsynonymous single nucleotide polymorphisms via sequence conservation features. Int. J. Eng. Manuf. (IJEM) 1(5), 66–72 (2011). https://doi.org/10.5815/ijem.2011.05.09

    CrossRef  Google Scholar 

  14. Balonin, N.A., Balonin, Y.N., Djokovic, D.Z., Karbovskiy, D.A., Sergeev, M.B.: Construction of symmetric Hadamard matrices. https://arxiv.org/abs/1708.05098

  15. Georgiou, S., Koukouvinos, C., Seberry, J.: Hadamard matrices, orthogonal designs and construction algorithms. In: Designs 2002: Further Computational and Constructive Design Theory, pp. 133–205. Kluwer, Boston (2003). ISBN 1-4020-7599-5

    Google Scholar 

  16. Jeffrey, H.J.: Chaos game representation of gene structure. Nucleic Acids Res. 18(8), 2163–2170 (1990)

    CrossRef  Google Scholar 

  17. Petoukhov, S.V., Svirin, V.I.: Fractal genetic nets and symmetry principles in long nucleotide sequences. Symmetry Cult. Sci. 23(3–4), 303–322 (2012)

    Google Scholar 

  18. Rudner, R., Karkas, J.D., Chargaff, E.: Separation of B. SubtilisDNA into complementary strands. 3. Direct analysis. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 60, no. 3, pp. 921–922 (1968). https://doi.org/10.1073/pnas.60.3.921. PMC 225140

  19. Townsend, J.P., Su, Z., Tekle, Y.: Phylogenetic signal and noise: predicting the power of a data set to resolve phylogeny. Genetics 61(5), 835–849 (2012). https://doi.org/10.1093/sysbio/sys036. PMID 22389443

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Acknowledgments

Part of the calculations was performed on the supercomputer “MVS-10P” (JSCC RAS). Author thanks Sergey Petoukhov and Vitaly Svirin for scientific discussions.

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Authors and Affiliations

  1. Mechanical Engineering Research Institute, Russian Academy of Sciences, M. Kharitonievsky pereulok, 4, Moscow, Russia

    Ivan V. Stepanyan

Authors
  1. Ivan V. Stepanyan
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Correspondence to Ivan V. Stepanyan .

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Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Prof. Zhengbing Hu

  2. Mechanical Engineering Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Prof. Sergey Petoukhov

  3. “Igor Sikorsky Kiev Polytechnic Institute”, National Technical University of Ukraine, Kiev, Ukraine

    Prof. Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, FL, USA

    Prof. Matthew He

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Stepanyan, I.V. (2020). Multidimensional Approach for Analysis of Chromosomes Nucleotide Composition. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-16621-2_53

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