NEW SERIES. SERIES: PHYSICS

Izvestiya of Saratov University.

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)


Cite this article as:

Ulyanov S. S., Ulianova O. V., Zaitsev S. S., Khizhnyakova M. A., Saltykov I. V., Filonova N. N., Subbotina I. A., Lyapina A. M., Feodorova V. A. Study of Statistical Characteristics of GB-speckles, Forming at Scattering of Light on Virtual Structures of Nucleotide Gene Sequences of Enterobacteria. //Izvestiya of Saratov University. New series. Series: Physics. , 2018, vol. 18, iss. 2, pp. 123-137. DOI: https://doi.org/10.18500/1817-3020-2018-18-2-123-137

Language: 
Russian
UDC: 
535.41

Study of Statistical Characteristics of GB-speckles, Forming at Scattering of Light on Virtual Structures of Nucleotide Gene Sequences of Enterobacteria

Autors: 
Ulyanov Sergei Sergeevich, Saratov State University
Ulianova Onega Vladimirovna, Federal Research Center of Virology and Microbiology
Zaitsev Sergei Sergeevich, Federal Research Center of Virology and Microbiology
Khizhnyakova Mariia Aleksandrovna, Federal Research Center of Virology and Microbiology
Saltykov Iurii Vladimirovich, Saratov State Agrarian University named after N.I. Vavilov
Filonova Nadezhda Nikolaevna, Federal Research Center of Virology and Microbiology
Subbotina Irina Anatol'evna, Federal Research Center of Virology and Microbiology
Lyapina Anna Mikhailovna, Federal Research Center of Virology and Microbiology
Feodorova Valentina Anatol'evna, Federal Research Center of Virology and Microbiology
Abstract: 

Background and Objectives: A brief review of methods of modern bioinformatics, based on the usage of virtual optical GBspeckles (gene-based speckles), has been presented in this paper. An algorithm of transformation of a nucleotide sequence into a 2D GB-speckle-structure has been proposed and discussed. Materials and Methods: Computer simulation of the process of formation of GB-speckles at the scattering of coherent light on quasi-random virtual surfaces, corresponding to initial nuclear sequence of the genes, encoded by the Omptin family proteins, such as SopA, OmpP, OmpT, PgtE and Pla in Enterobacteriaceae spp. has been carried out. Results: Statistical properties of GB-speckles, coding of different sequences of the genes have been investigated. Conclusion: It has been shown that GB-speckles of this type obey Gaussian statistics. It has also been found that classical methods of statistical analysis of GB speckles are not informative and low-effective from a viewpoint of detection of common fragments in initial nucleotide sequences. However, a direct comparison of the probability density functions of spatial fluctuations of the speckle intensity allows to find common motifs of the comparing genes. A criterion for the reliable detection of the presence of common motifs in these genes, based on the methods of speckle-optics has been suggested. These motifs could be innovated promising molecular targets for the development of a new generation of effective synthetic Omptin-based peptide precise medical devices for smart laboratory diagnostics of a group of Gramnegative Enterobacterial pathogens.

DOI: 
10.18500/1817-3020-2018-18-2-123-137
References: 
  1. Sintchenko V., Roper M. P. Pathogen genome bioinformatics. Methodsin Molecular Biology, 2014, vol.1168, pр. 173–193. 
  2. Lesk A. M. Introduction to bioinformatics. Oxford, Oxford University Press, 2002. 314 p.
  3. Guo Q., Strauss K., Ceze L., Malvar H. High-density image storage using approximate memory cells. Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems. ASPLOS’16, Apr 2–6, 2016. Atlanta (US), IEEE, 2016, pp. 413–426. DOI: http://dl.acm.org/citation.cfm?doid=2872362.2872413
  4. Bornholt J., Lopez R., Carmean D. M., Ceze L., Seelig G., Strauss K. A DNA-based archival storage system. Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems. ASPLOS’16, Apr 2–6, 2016. Atlanta (US), IEEE, 2016, рр. 637–649. DOI: http://dx.doi.org/10.1145/2872362.2872397
  5. Rocky Pimentel. Why Data Storage Is Hot Again. Available at: https://www.recode.net/2014/1/10/11622168/stuffed-why-data-storage-is-ho... (accessed 10 January 2014). 
  6. Bornholt J., Lopez R., Carmean M. D. Toward a DNAbased archival storage system. IEEE MICRO, 2017, vol. 37, iss. 3, pp. 98–104. 
  7. Bornholt J., Lopez R., Ceze L. A DNA-Based Archival Storage System. Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems. ASPLOS’16, Apr 2–6, 2016. Atlanta (US), IEEE, 2016, рр. 637–649. DOI: http://dx.doi.org/10.1145/2872362.2872397 
  8. Guo Q., Strauss K., Ceze L. High-Density Image Storage Using Approximate Memory Cells. ASPLOS ‘16, 2016, pp. 1–14. DOI: http://dx.doi.org/10.1145/2872362.2872413
  9. Organick L., Dumas S., Ang S. D., Chen Y.-J., Lopez R. Scaling up DNA data storage and random access retrieval. BioRxiv., 2017, Posted March 7, pp. 1–14. DOI: http://dx.doi.org/10.1101/114553
  10. Rashtchian C., Makarychev K., Rácz M. Clustering Billions of Reads for DNA Data Storage. 31st Conference on Neural Information Processing Systems, Dec 4–9, 2017. Long Beach (US), NIPS. 2017, pp. 1–12. 
  11. Video This Too Shall Pass by group OK Go. Available at: https://www.youtube.com/watch?v=qybUFnY7Y8w (accessed 1 March 2018). 
  12. Rojahn S. Y. An Entire Book Written in DNA. Available at: https://www.technologyreview.com/s/428922/an-entirebook-written-in-dna (accessed 16 August 2012). 
  13. Church G. M., Gao Y., Kosuri S. Next-Generation Digital Information Storage in DNA. Science, 2012, vol. 337, pp. 1628. 
  14. Blawat M., Gaedke K., Hütter I. Forward Error Correction for DNA Data Storage. Procedia Computer Science, 2016, vol. 80, pp. 1011–1022. 
  15. Shipman S. L., Nivala J., Macklis J. D. CRISPR-Cas encoding of a digital movie into the genomes of a population of living bacteria. Nature, 2017, vol. 547, pp. 345–349. DOI: https://doi.org/10.1038/nature23017
  16. Goldman N., Bertone P., Chen S., Dessimoz Ch., LeProust E. M., Sipos B., Birney E. Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature, 2013, vol. 494, pp. 77–80. DOI: https://doi.org/10.1038/nature11875
  17. Gibson D. G., Glass J. I., Lartigue C., Noskov V. N., Chuang R. Y., Algire M. A., Benders G. A., Montague M. G., Ma L., Moodie M. M., Merryman C., Vashee S., Krishnakumar R., Assad-Garcia N., Andrews-Pfannkoch C., Denisova E. A., Young L., Qi Z. Q., SegallShapiro T. H., Calvey C. H., Parmar P. P., Hutchison C. A. 3rd., Smith H. O., Venter J. C. Creation of a bacterial cell controlled by a chemically synthesized genome. Science, 2010, vol. 329, pp. 52–56. 
  18. Clelland C. T., Risca V., Bancroft C. Hiding messages in DNA microdots. Nature, 1999, vol. 399, pp. 533–534. 
  19. Adleman L. M. Molecular computation of solutions to combinatorial problems. Science, 1994, vol. 266, pp. 1021–1024. 
  20. Mandeles S. Nucleic acid sequence analysis. New York, London, Columbia University Press, 1972. 282 p. 
  21. Madi A., Friedman Y., Roth D., Regev T., BransburgZabary S., Jacob E. B. Genome holography: Deciphering function-form motifs from gene expression data. PLoS One, 2008, vol. 3, iss. 7, pp. 114. DOI: http://dx.doi.org/10.1371/journal.pone.0002708
  22. Ulyanov S. S., Zaytsev S. S., Ulianova O. V., Saltykov Y. V., Feodorova V. A. Using of methods of speckle optics for Chlamydia trachomatis typing. Proc. SPIE, 2017, vol. 10336, paper 03360D. DOI: https://doi.org/10.1117/12.2270760
  23. 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. Laser Physics Letters, 2018, vol. 15, no. 4, pp. 1–6. DOI: https://doi.org/10.1088/1612-202X/aaa11c
  24. Feodorova V. A., Ulyanov S. S., Zaytsev S. S., Saltykov Y. V., Ulianova O. V. Optimization of algorithm of coding of genetic information of Chlamydia. Proc. SPIE, 2018, vol. 10716, paper 107160Q. DOI: https://doi.org/10.1117/12.2314640
  25. Feodorova V. A., Saltykov Y. V., Zaytsev S. S., Ulyanov S. S., Ulianova O. V. Application of virtual phaseshifting speckle-interferometry for detection of polymorphism in the Chlamydia trachomatis omp1 gene. Proc. SPIE, 2018, vol. 10716, paper 107160M. DOI: https://doi.org/10.1117/12.2314700
  26. World Health Organization. Media centre. Diarrhoeal disease. Available at: http://www.who.int/mediacentre/factsheets/fs330/en/Updated (accessed 1 May 2018). 
  27. Zabokritskiy N. A. The infectious morbidity in the Russian Federation and tendencies of it is development in the next decade. Bulletin “Health & education millennium”’, 2015, vol. 17, no. 5, pp. 16–26 (in Russian). 
  28. Rospotrebnadzor obnarodoval statistiku infektsionnykh boleznei za pervoe polugodie (Rospotrebnadzor published statistical data regarding infl ection diseases for fi rst half of year). Available at: http://www.yaprivit.ru/news/2365/ (accessed 18 July 2016) (in Russian). 
  29. D’haeseleer P. What are DNA sequence motifs? Nature Biotechnology, 2006, vol. 24, iss. 4, pp. 423–425. DOI: https://doi.org/10.1038/nbt0406-423
  30. Kukkonen M., Korhonen K. The omptin family of enterobacterial surface proteases/adhesins: from housekeeping in Escherichia coli to systemic spread of Yersinia pestis. Intern. J. Med. Microbiol., 2004, July, vol. 294, no. 1, рр. 7–14. DOI: https://doi.org/10.1016/j.ijmm.2004.01.003
  31. Feodorova V. A., Khizhnyakova M. A., Zaitsev S. S., Lyapina A. M., Sayapina L.V., Lyapina E.P., Ulyanova O. V., Motin V. L. Evaluation of diagnostic potential of immunoreactive epitopes of the Omptin protease family by using a peptide library. Biopreparations, 2017, vol. 17, no. 3, pp. 180–186 (in Russian). 
  32. Kobzar’ A. I. Prikladnaia matematicheskaia statistika. Dlia inzhenerov i nauchnykh rabotnikov [Applied Statistics for Engineers and Scientists]. Moscow, FIZMATLIT Publ., 2006. 816 p. (in Russian). 
  33. Goodman J. Statisticheskaia optika [Statistical Optics]. Moscow, Mir Publ., 1988. 528 p. (in Russian). 
  34. Jakeman E. Speckle statistics with a small number of scatterers. Optical Engineering, 1984, vol. 23, no. 4, pp. 453–661. DOI: https://doi.org/10.1117/12.7973317
  35. Daugman J. How Iris Recognition Works. IEEE transactions on circuits and systems for video technology, 2004, vol. 14, no. 1, January, pp. 21–30. DOI: https://doi.org/10.1109/TCSVT.2003.818350
  36. Wendy L. Martinez., Angel R. Martinez. Computational statistics handbook with Matlab. Boca Raton, London, New York, Washington, D.C., Chapman & Hall / CRC, 2002. 585 p.
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