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Программное обеспечение и возможности современных языков программирования для изучения биоинформатики и вычислительной вакцинологии новой коронавирусной инфекции

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Аннотация

Обзорная статья сосредоточена на вопросах применения программного обеспечения для целей геномики, иммуноинформатики, вычислительной вакцинологии, математической эпидемиологии и филогенеза новой коронавирусной инфекции. Приводится разработанная авторами классификация программного обеспечения для изучения COVID-19.

Об авторах

М. В. Спринджук
Объединенный институт проблем информатики НАН Беларуси
Беларусь

к. т. н., старший научный сотрудник лаборатории математической кибернетики

ул. Сурганова, д. 6, 220012, г. Минск



А. С. Владыко
РНПЦ эпидемиологии и микробиологии
Беларусь

д. м. н., профессор, главный научный сотрудник лаборатории биотехнологии и иммунодиагностики

ул. Филимонова, д. 23, 220114, г. Минск



Л. П. Титов
РНПЦ эпидемиологии и микробиологии
Беларусь

д. м. н., профессор, член – корреспондент НАН Беларуси, заведующий лабораторией клинической и экспериментальной микробиологии

ул. Филимонова, д. 23, 220114, г. Минск



А. П. Кончиц
Институт леса НАН Беларуси
Беларусь

к. б. н., ведущий научный сотрудник лаборатории лесной селекции и семеноводства

ул. Пролетарская, д. 71, 246001, г. Гомель



Список литературы

1. Tomar, Marton. Immunoinformatics. – P [S.l.]: Springer US, 2020. – 409 p.

2. De, R.K., Tomar, N. Immunoinformatics. – New York: Humana Press, 2014. – P xix, 586 pages.

3. Schönbach, C., Ranganathan, S., Brusic, V. Immunoinformatics. – New York: Springer, 2008. – xix, 200 p.

4. Flower, D.R. Bioinformatics for vaccinology. – Chichester, West Sussex, England; Hoboken, NJ: John Wiley & Sons, 2008. – 314 p.

5. Prabhakar, H., Kapoor, I., Mahajan, C. Clinical synopsis of COVID-19: evolving and challenging. – 1 online resource (XIV, 260 pages).

6. Chandra, P., Roy, S. Diagnostic strategies for COVID-19 and other coronaviruses. – Singapore: Springer, 2020. – 1 online resource (viii, 199 pages).

7. Saxena, S.K. Coronavirus disease 2019 (COVID-19): epidemiology, pathogenesis, diagnosis, and therapeutics. – 1 online resource (224 pages).

8. Plotkin, S.A., Orenstein, W.A., Offit, P.A. Plotkin's vaccines. – Philadelphia, PA: Elsevier, 2018. – xxi, 1691 pages.

9. Enjuanes, L. Coronavirus replication and reverse genetics. – Berlin; New York: Springer, 2005. – vii, 257 p.

10. Varshney, D., Singh, M., SpringerLink (Online service). Lyophilized Biologics and Vaccines Modality – Based Approaches. – New York, NY: Springer New York: Imprint: Springer, 2015. – XI, 401 p. 99 illus., 68 illus. in color.

11. Nunnally, B.K., Turula, V.E., Sitrin, R.D., SpringerLink (Online service). Vaccine Analysis: Strategies, Principles, and Control. – Berlin, Heidelberg: Springer Berlin Heidelberg: Imprint: Springer, 2015. – XII, 665 p. 124 illus., 76 illus. in color.

12. Maier, H.J., Bickerton, E., Britton, P. Coronaviruses methods and protocols. – New York: Humana Press; Springer, 2015. – xi, 285 pages.

13. Kiyono, H., Ogra, P.L., McGhee, J.R. Mucosal vaccines. – San Diego: Academic Press, 1996. – xix, 479 p.

14. Vanniasinkam, T., Tikoo, S.K., Samal, S.K. Viral vectors in veterinary vaccine development: a textbook. – Cham, Switzerland: Springer, 2021. – 1 online resource (xi, 230 pages).

15. Hwang, W., Lei, W., Katritsis, N.M. et al. Current and prospective computational approaches and challenges for developing COVID-19 vaccines // Advanced Drug Delivery Reviews. – 2021. epub.

16. Sohail, M.S., Ahmed, S.F., Quadeer, A.A., McKay, M.R. In silico T cell epitope identification for SARS– CoV– 2: Progress and perspectives // Adv Drug Deliv Rev. – 2021. – Vol. 171, – P. 29-47.

17. Noorimotlagh, Z., Karami, C., Mirzaee, S.A. et al. Immune and bioinformatics identification of T cell and B cell epitopes in the protein structure of SARS–CoV–2: A systematic review // Int Immunopharmacol. – 2020. – Vol. 86. – P. 106738.

18. Baruah, V., Bose, S. Immunoinformatics aided identification of T cell and B cell epitopes in the surface glycoprotein of 2019 nCoV // Journal of medical virology. – 2020. – Vol. 92, No. 5. – P. 495-500.

19. Oliveira, S.C., de Magalhães, M.T., Homan, E.J. Immunoinformatic Analysis of SARS–CoV–2 Nucleocapsid Protein and Identification of COVID–19 Vaccine Targets // Frontiers in immunology. – 2020. – Vol. 11. – P. 2758.

20. Feng, Y., Qiu, M., Zou, S. et al. Multi–epitope vaccine design using an immunoinformatics approach for 2019 novel coronavirus in China (SARS–CoV–2) // BioRxiv. – 2020. – Vol. 1, No. 1. – P. 1-12.

21. Wang, X., Xu, W., Tong, D. et al. A chimeric multi– epitope DNA vaccine elicited specific antibody response against severe acute respiratory syndrome – associated coronavirus which attenuated the virulence of SARS–CoV in vitro // Immunology letters. – 2008. – Vol. 119, No. 1– 2. – P. 71-77.

22. Belikova Y., Samsonov Y., Abakushina E. Modern vaccines and coronavirus infections. Research and Practical Medicine Journal, 2020, Vol 7, No. 4. pp. 135-154. (In Russian).

23. Kononov A., Mishchenko V., Dumova B. et al. Antigenic properties of bovine coronavirus vaccine with various adjuvants. Proceedings of the Federal Center for Animal Health, 2009, Vol 7, pp. 50-55. (In Russian).

24. Kharchenko E. P. Coronavirus SARS-Cov-2: the complexity of pathogenesis, the search for vaccines and future pandemics. Epidemiology and Vaccine Prevention, 2020, Vol 19, No. 3. pp. 4-20. (In Russian).

25. Ozharovskaya T., Zubkova O., Dolzhikova I. et al. Immunogenicity of various forms of glycoprotein S of the Middle East respiratory syndrome coronavirus. Acta Naturae (Russian version), 2019, Vol 11, No. 1 (40). pp. 38-47. (In Russian).

26. Chepurnov A. A., Sharshov K. A., Kazachinskaya E. I. et al. Antigenic properties of SARS-CoV-2 / human / RUS / Nsk-FRCFTM-1/202 coronavirus isolate isolated from a patient in Novosibirsk. Journal of Infectology, 2020, Vol 12, No. 3. pp. 42-50. (In Russian).

27. Kharchenko E.P. SARS-CoV-2 coronavirus: features of structural proteins, contagiousness and possible immune collisions. Epidemiology and Vaccine Prevention, 2020, Vol 19, No. 2. pp. 13-30. (In Russian).

28. Titova M.A. Approaches to modeling immunogenic peptides: author. dis. for a job. learned. step. Cand. chem. Sciences: 02.00.10 / Titova Maya Adolfovna. - M., 2003, 23 p. M., 2003..(In Russian).

29. Hu, T., Li, J., Zhou, H. et al. Bioinformatics resources for SARS–CoV–2 discovery and surveillance // Briefings in Bioinformatics. – 2021. – Vol. 22, No. 2. – P. 631-641.

30. Hufsky, F., Lamkiewicz, K., Almeida, A. et al. Computational strategies to combat COVID–19: useful tools to accelerate SARS–CoV–2 and coronavirus research // Briefings in Bioinformatics. – 2020. – Vol. 22, No. 2. – P. 642-663.

31. Kangabam, R., Sahoo, S., Ghosh, A. et al. Next– generation computational tools and resources for coronavirus research: From detection to vaccine discovery // Computers in biology and medicine. – 2020. – Vol. 1, No. 1. – P. 104-115.

32. Kiyotani, K., Toyoshima, Y., Nemoto, K., Nakamura, Y. Bioinformatic prediction of potential T cell epitopes for SARS-Cov–2 // Journal of human genetics. – 2020. – Vol. 65, No. 7. – P. 569-575.

33. Ogishi, M., Yotsuyanagi, H. Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space // Frontiers in Immunology. – 2019. – Vol. 10, No. 827. – P. 1-20.

34. Ong, E., Wang, H., Wong, M.U. et al. Vaxign– ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens // Bioinformatics. – 2020. – Vol. 36, No. 10. – P. 3185-3191.

35. Xiang, Z., He, Y. Genome– wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology // BMC Bioinformatics. – 2013. – Vol. 14, No.1. – P. S2.

36. He, Y., Xiang, Z., Mobley, H.L. Vaxign: the first web– based vaccine design program for reverse vaccinology and applications for vaccine development // J Biomed Biotechnol. – 2010. – Vol. 10, No. Epub 2010 Jul 4. – P. 297505.

37. Vita, R., Overton, J.A., Greenbaum, J.A. et al. The immune epitope database (IEDB) 3.0 // Nucleic Acids Res. –Vol. 43, No. 1. – P. D405-12.

38. El– Manzalawy, Y., Dobbs, D., Honavar, V. Predicting linear B–cell epitopes using string kernels // J Mol Recognit. – 2008. – Vol. 21, No. 4. – P. 243-55.

39. Seemann, T. Prokka: rapid prokaryotic genome annotation // Bioinformatics. – 2014. – Vol. 30, No. 14. – P. 2068-9.

40. Huerta-Cepas, J., Szklarczyk, D., Heller, D. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses // Nucleic Acids Res. – 2018. – Vol. 47, No. D1. – P. D309-D314.

41. Jones, P., Binns, D., Chang, H.Y. et al. InterProScan 5: genome–scale protein function classification // Bioinformatics. 2014. – Vol. 30, No. 9. – P. 1236-40.

42. Mulder, N., Apweiler, R. InterPro and InterProScan: tools for protein sequence classification and comparison // Methods Mol Biol. – 2007. – Vol.396. – P. 59-70.

43. Syed, A., Upton, C. Java GUI for InterProScan (JIPS): a tool to help process multiple InterProScans and perform ortholog analysis // BMC Bioinformatics. – 2006. – Vol. 7. – P. 462.

44. Quevillon, E., Silventoinen, V., Pillai, S. et al. InterProScan: protein domains identifier // Nucleic Acids Res. – 2005. – Vol. 33, No. 1. – P. W116- 20.

45. Katoh, K., Standley, D.M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability // Mol Biol Evol. – 2013. – Vol. 30, No. 4. – P. 772-80.

46. Katoh, K., Frith, M.C. Adding unaligned sequences into an existing alignment using MAFFT and LAST // Bioinformatics. – 2012. – Vol. 28, No. 23. – P. 3144-6.

47. Katoh, K., Toh, H. Parallelization of the MAFFT multiple sequence alignment program // Bioinformatics. – 2010. – Vol. 26, No. 15. – P. 1899-900.

48. Katoh, K., Asimenos, G., Toh, H. Multiple alignment of DNA sequences with MAFFT // Methods Mol Biol. – 2009. – Vol. 537. – P. 39-64.

49. Hung, C.L., Lin, Y.S., Lin, C.Y. et al. CUDA ClustalW: An efficient parallel algorithm for progressive multiple sequence alignment on Multi– GPUs // Comput Biol Chem. – Vol. 58. – P. 62-8.

50. Hung, J.H., Weng, Z. Sequence Alignment and Homology Search with BLAST and ClustalW // Cold Spring Harb Protoc. 2016. – Vol. 16, No. 11. – P. 1-10.

51. Vangala, R.K., Singh, L., Gupta, R.P. BioParishodhana: A novel graphical interface integrating BLAST, ClustalW, primer3 and restriction digestion tools // Bioinformation. – 2012. – Vol. 8, No. 13. – P. 639-43.

52. Zaal, D., Nota, B. ADOMA: A Command Line Tool to Modify ClustalW Multiple Alignment Output // Mol Inform. – 2016. – Vol. 35, No. 1. – P. 42-4.

53. Darling, A.E., Treangen, T.J., Messeguer, X., Perna, N.T. Analyzing patterns of microbial evolution using the mauve genome alignment system // Methods Mol Biol. – 2007. – Vol. 396. – P. 135-52.

54. Darling, A.C., Mau, B., Blattner, F.R., Perna, N.T. Mauve: multiple alignment of conserved genomic sequence with rearrangements // Genome Res. – 2004. – Vol. 14, No. 7. – P. 1394-403.

55. Angiuoli, S.V., Salzberg, S.L. Mugsy: fast multiple alignment of closely related whole genomes // Bioinformatics. – 2010. – Vol. 27, No. 3. – P. 33-42.

56. Cleemput, S., Dumon, W., Fonseca, V. et al. Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes // Bioinformatics. – Vol. 36, No. 11. – P. 3552-3555.

57. Vilsker, M., Moosa, Y., Nooij, S. et al. Genome Detective: an automated system for virus identification from high-throughput sequencing data // Bioinformatics. – 2018. – Vol. 35, No. 5. – P. 871-873.

58. Gupta, S., Kapoor, P., Chaudhary, K. et al. Peptide toxicity prediction // Methods Mol Biol. – Vol. 1268. – P. 143-57.

59. Gupta, S., Kapoor, P., Chaudhary, K. et al. In silico approach for predicting toxicity of peptides and proteins // PLoS One. – 2013. – Vol. 8, No. 9. – P. e. 73957.

60. Krutz, N.L., Winget, J., Ryan, C.A. et al. Proteomic and Bioinformatic Analyses for the Identification of Proteins With Low Allergenic Potential for Hazard Assessment // Toxicol Sci. – – Vol. 170, No. 1. – P. 210-222.

61. Maurer– Stroh, S., Krutz, N.L., Kern, P.S. et al. AllerCatPro– prediction of protein allergenicity potential from the protein sequence // Bioinformatics. – 2019. – Vol. 35, No. 17. – P. 3020-3027.

62. Sharma, N., Patiyal, S., Dhall, A. et al. AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes // Brief Bioinform. – 2020. – Vol. 1, № 1. – P. 1-7.

63. Neeharika, D., Sunkar, S. Computational approach for the identification of putative allergens from Cucurbitaceae family members // J Food Sci Technol. – 2021. – Vol 58, No. 1. – P. 267-280.

64. Sircar, G., Saha, B., Bhattacharya, S.G., Saha, S. In silico prediction of allergenic proteins // Methods Mol Biol. – 2014. Vol. 1184. – P. 375-88.

65. Saha, S., Raghava, G.P. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes // Nucleic Acids Res. 2006. – Vol. 34, No. Web Server issue. – P. W. 202-9.

66. Pagadala, N.S., Syed, K., Tuszynski, J. Software for molecular docking: a review // Biophys Rev. – 2017. – Vol. 9, No. 2. – P. 91-102.

67. Grote, A., Hiller, K., Scheer, M. et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host // Nucleic Acids Res. – 2005. – Vol. 33, No. Web Server issue. – P. W526-31.

68. Zheng, W., Zhang, C., Bell, E.W., Zhang, Y. I – TASSER gateway: A protein structure and function prediction server powered by XSEDE // Future Gener Comput Syst. – 2019. – Vol. 99, – P. 73-85.

69. Roy, A., Kucukural, A., Zhang, Y. I – TASSER: a unified platform for automated protein structure and function prediction // Nat Protoc. – 2010. – Vol. 5, No. 4. – P. 725-38.

70. Zhang, Y. I– TASSER server for protein 3D structure prediction // BMC Bioinformatics. – 2008. – Vol. 9. – P. 40.

71. Raborn, R.T., Brendel, V.P. Using RAMPAGE to Identify and Annotate Promoters in Insect Genomes // Methods Mol Biol. – 2019. – Vol. 1858, No. 1. – P. 99-116.

72. Collatz, M., Mock, F., Hölzer, M. et al. EpiDope: A Deep neural network for linear B–cell epitope prediction // bioRxiv. 2020. – No. 1. – P. 1-8.

73. Suprun, M., Ellis, R.J., Sampson, H.A., Suárez– Fariñas, M. bbeaR: an R package and framework for epitope – specific antibody profiling // Bioinformatics. – 2021. – Vol. 37, No. 1. – P. 131-133.

74. Ogishi, M., Yotsuyanagi, H. Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space // Frontiers in Immunology. – 2019. – Vol .10. – P. 827.

75. Pittard, W.S., Li, S. The Essential Toolbox of Data Science: Python, R, Git, and Docker // Methods Mol Biol. – 2020. – Vol. 2104, No. 1. – P. 265-311.

76. Kwon, C., Kim, J., Ahn, J. DockerBIO: web application for efficient use of bioinformatics Docker images // PeerJ. – 2018. – Vol. 6, No. 1. – P. e. 5954.

77. Garofoli, A., Paradiso, V., Montazeri, H. et al. PipeIT: A Singularity Container for Molecular Diagnostic Somatic Variant Calling on the Ion Torrent Next– Generation Sequencing Platform // J. Mol Diagn. – 2019. – Vol. 21, No. 5. – P. 884-894.

78. Samdani, A., Vetrivel, U. POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening // Comput Biol Chem. – 2018. – Vol. 74, No. 1. – P. 39-48.

79. Красильников, А.П. Микробиологический словарь-справочник. – 2– е изд., доп. и перераб. – Мн.: ООО "Асар", 1999. – 397 c.


Для цитирования:


Спринджук М.В., Владыко А.С., Титов Л.П., Кончиц А.П. Программное обеспечение и возможности современных языков программирования для изучения биоинформатики и вычислительной вакцинологии новой коронавирусной инфекции. Цифровая трансформация. 2021;(3):47-57.

For citation:


Sprindzuk M.V., Vladyko A.S., Titov L.P., Konchits A.P. Software and Resources of Modern Programming Languages for Bioinformatics and Computational Vaccinology Research of the New Coronavirus Infection. Digital Transformation. 2021;(3):47-57. (In Russ.)

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ISSN 2522-9613 (Print)
ISSN 2524-2822 (Online)