Izvestiya of Saratov University.


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Zimnyakov D. A., Alonova M. V., Skripal A. V., Dobdin S. Y., Feodorova V. A. Small-angle polarimetry as a technique for identification of nucleotide sequences in bioinformatics. Izvestiya of Saratov University. Physics , 2023, vol. 23, iss. 1, pp. 46-55. DOI: 10.18500/1817-3020-2023-23-1-46-55, EDN: IQKRQK

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Small-angle polarimetry as a technique for identification of nucleotide sequences in bioinformatics

Zimnyakov Dmitry Aleksandrovich, Yuri Gagarin State Technical University of Saratov
Alonova Marina Vasil'evna, Yuri Gagarin State Technical University of Saratov
Skripal Anatoly Vladimirovich, Saratov State University
Dobdin Sergey Yur'evich, Saratov State University
Feodorova Valentina Anatol'evna, Saratov State University

Background and Objectives: The method of identification of symbolic sequences associated with the genetic structure of biological objects using the principles of small-angle polarimetry is considered. This method of analyzing and visualizing symbolic sequences obtained by sequencing DNA fragments can be defined as small-angle polarimetry of phase-modulating structures associated with genetic information. Materials and Methods: The analyzed symbolic sequence is represented by a two-dimensional phase-modulating matrix, each element of which corresponds to one of the four basic nucleotides (adenine, cytosine, thymine, guanine), and the depth of modulation of the phase of the reading coherent linearly polarized beam is determined by the content of this nucleotide in the corresponding triplet in the nucleotide sequence. As a result of the diffraction of a reading coherent beam with a polarization plane oriented at an angle of 45° to the sides of the phase-modulating matrix, a spatial distribution of local polarization states of the reading field diffracted on the matrix is formed in the paraxial region of the far diffraction zone. Discrimination of local polarization states in accordance with the proposed algorithm makes it possible to synthesize a binary spatial distribution, which is a unique identifier of the analyzed symbol sequence. Results: Modeling of the processes of phase coding and subsequent analysis of local polarization states in the near-axial region using sequencing results for the strains “Wuhan”, “Delta” and “Omicron” of the SARS-CoV-2 virus has shown a high sensitivity of the method to local changes in the structure of nucleotide sequences. Conclusion: The results of the simulation allow us to conclude that binary distributions of local polarization states of light fields diffracted on DNA-associated phase-modulating structures recorded in the axial region are characterized by high sensitivity to local mutational changes in the structure of nucleotide sequences. The results obtained can be used as a basis for creating effective hybrid methods for analyzing genetic information using the principles of polarization coding and small-angle polarimetry.

This work was supported by the Russian Science Foundation (project no. 22-21-00194).
  1. Andelfinger G., Hitte C., Etter L., Guyon R., Bourque G., Tesler G., Pevzner P., Kirkness E., Galibert F., Benson D. W. Detailed four-way comparative mapping and gene order analysis of the canine ctvm locus reveals evolutionary chromosome rearrangements. Genomics, 2004, vol. 83, pp. 1053–1062. https://doi.org/10.1016/j.ygeno.2003.12.009
  2. Anisimova M., Bielawski J. P., Yang Z. Accuracy and power of Bayes prediction of amino acid sites under positive selection. Mol. Biol. Evol., 2002, vol. 19, pp. 950–958. https://doi.org/10.1093/oxfordjournals.molbev.a004152
  3. Rivas E., Eddy S. R. Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinform, 2001, vol. 2, pp. 1–19. https://doi.org/10.1186/1471-2105-2-8
  4. Hwang D. G., Green P. Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Proc. Natl. Acad. Sci. U.S.A., 2004, vol. 101, pp. 13994–14001. https://doi.org/10.1073/pnas.0404142101
  5. Eddy S. R. A model of the statistical power of comparative genome sequence analysis. PLoS Biol., 2005, vol. 3, pp. e10. https://doi.org/10.1371/journal.pbio.0030010
  6. Gitter A., Siegfried Z., Klutstein M., Fornés O., Oliva B., Simon I., Bar-Joseph Z. Backup in gene regulatory networks explains differences between binding and knockout results. Mol. Syst. Biol., 2009, vol. 5, pp. 276. https://doi.org/10.1038/msb.2009.33
  7. Cooper G. M., Brudno M., Green E. D., Batzoglou S., Sidow A. Quantitative estimates of sequence divergence for comparative analyses of mammalian genomes. Genome Res., 2003, vol. 13, pp. 813–820. https://doi.org/10.1101/gr.1064503
  8. Abnizova I., Walter K., Te Boekhorst R., Elgar G., Gilks W. R. Statistical information characterization of conserved non-coding elements in vertebrates. J. Bioinform. Comput. Biol., 2007, vol. 5, pp. 533–547. https://doi.org/10.1142/S0219720007002898
  9. Orlov Y. L., Te Boekhorst R., Abnizova I. I. Statistical measures of the structure of genomic sequences: Entropy, complexity, and position information. J. Bioinform. Comput. Biol., 2006, vol. 4, pp. 523–536. https://doi.org/10.1142/S0219720006001801
  10. Sorek R., Safer H. M. A novel algorithm for computational identification of contaminated EST libraries. Nucleic Acids Res., 2003, vol. 31, iss. 3, pp. 1067–1074. https://doi.org/10.1093/nar/gkg170
  11. Altschul S. F., Gish W., Miller W., Myers E. W., Lipman D. J. Basic local alignment search tool. J. Mol. Biol., 1990, vol. 215, pp. 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2
  12. Bishop M. J., ed. Guide to Human Genome Computing. 2nd ed. Academic Press, San Diego, CA, USA, 1998. 306 p.
  13. Adams M. D., Fields C., Venter J. C., eds. Automated DNA Sequencing and Analysis. 1st ed. Academic Press, San Diego, CA, USA, 1994. 368 p.
  14. Posada D., ed. Bioinformatics for DNA Sequence Analysis. 1st ed. Humana Press Inc., Totova, NJ, USA, 2009. 368 p. https://doi.org/10.1007/978-1-59745-251-9
  15. Opticheskaya golografiya. Pod red. G. Colfilda [Colfild G., ed. Optical holography: in 2 vols.]. Moscow, Mir Publ., 1982. Vol. 2. 186 p. (in Russian).
  16. Ulianova O. V., Zaytsev S. S., Saltykov Y. V., Lyapina A., Subbotina I., Filonova N., Ulyanov S. S., Feodorova V. A. Speckle-interferometry and speckle-correlometry of GB-speckles. Front. Biosci. (Landmark Ed), 2019, vol. 24, pp. 700–711. https://doi.org/10.2741/4744
  17. Ulyanov S. S., Ulianova O. V., Zaytsev S. S., Saltykov Y. V., Feodorova V. A. Statistics on gene-based laser speckles with a small number of scatterers: Implications for the detection of polymorphism in the Chlamydia trachomatis omp1 gene. Las. Phys. Lett., 2018, vol. 15, no. 4, article no. 045601. https://doi.org/10.1088/1612-202X/aaa11c
  18. Goodman J. W. Introduction to Fourier Optics. 4th ed. Macmillan Learning, New York, USA, 2017. 564 p.
  19. Goodman J. W. Statistical Optics. 2nd ed. J. Wiley and Sons, Inc., Hoboken, NJ, USA, 2015. 544 p.
  20. Chipman R., Lam W.-S. T., Young G. Polarized Light and Optical Systems. 1st ed. Optical Sciences and Applications of Light. CRC Press, Boca-Raton, FL, USA, 2018, 1036 p.
  21. GISAID: Official hCoV-19 Reference Sequence. Available at: https://gisaid.org/wiv04/.Acc.ID:EPI_ISL_402124 (accessed 15 Augist 2021).
  22. GISAID: Official hCoV-19 Reference Sequence. Available at: https://gisaid.org/wiv04/.Acc.ID:EPI_ISL_2552101 (accessed 15 Augist 2021).
  23. GISAID: Official hCoV-19 Reference Sequence. Available at: https://gisaid.org/wiv04/.Acc.ID:EPI_ISL_9991311 (accessed 15 Augist 2021).
  24. Bennett C. H., Brassard G. Quantum cryptography: Public key distribution and coin tossing. Proceedings of International Conference on Computers, Systems & Signal Processing, Dec. 9–12, 1984, Bangalore, India. IEEE, 1984, pp. 175–179.
  25. Bennett C. H., Brassard G. Quantum cryptography: Public key distribution and coin tossing. Theoretical Computer Science, 2014, vol. 560 (part 1), pp. 7–11. https://doi.org/10.1016/j.tcs.2014.05.025
  26. Bennett C. H. Quantum cryptography using any two nonorthogonal states. Phys. Rev. Lett., 1992, vol. 68, pp. 3121-3124. https://doi.org/10.1103/PhysRevLett.68.3121