nbsprotВестник войск РХБ защитыJournal of NBC Protection Corps2587-57283034-279127 Научный центр10.35825/2587-5728-2024-8-2-164-175nbsprot-358Research ArticleВооружение и средства войск РХБ защитыWeapons and Means of NBC Protection TroopsСовременные аппаратные и программные решения для полногеномного секвенирования, перспективы их внедрения в практику войск радиационной, химической и биологической защиты Вооруженных Сил Российской ФедерацииModern Hardware and Software Solutions for Whole-Genome Sequencing, Prospects of Their Implementation in the Practice of Nuclear, Chemical and Biological Protection Troops of the Armed Forces of the Russian Federationhttps://orcid.org/0000-0002-9714-2085ПетровА. А.PetrovA. A.

Петров Александр Анатольевич. Начальник научно-исследовательского управления, д-р мед. наук

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Aleksandr A. Petrov. Head of Research Department, Dr Sci. (Med.).

Oktiabrskaya St., 11, Sergiev Posad-6 141306

КазанцевА. В.KazantsevA. V.

Казанцев Алексей Васильевич. Старший научный сотрудник, канд. биол. наук.

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Aleksey V. Kazantsev. Leading researcher, Cand. Sci. (Biol.)

Oktiabrskaya St., 11, Sergiev Posad-6 141306

КовальчукЕ. А.KovalchukE. A.

Ковальчук Елена Анатольевна. Научный сотрудник, канд. мед. наук.

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Elena A. Kovalchuk. Researcher, Cand. Sci. (Biol.)

Oktiabrskaya St., 11, Sergiev Posad-6 141306

ПавлюковМ. Ю.PavlyukovM. Yu.

Павлюков Михаил Юрьевич. Научный сотрудник

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Mikhail Yu. Pavlyukov. Researcher

Oktiabrskaya St., 11, Sergiev Posad-6 141306

СапкуловА. В.SapkulovA. V.

Сапкулов Алексей Владимирович. Помощник начальника ФГБУ «48 ЦНИИ» Минобороны России

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Aleksey V. Sapkulov. Assistant of Head of Federal State Budgetary Establishment “48 Central Scientific Research Institute” of the Ministry of Defense of the Russian Federation

Oktiabrskaya St., 11, Sergiev Posad-6 141306

КутаевД. А.KutaevD. A.

Кутаев Дмитрий Анатольевич. Заместитель начальника ФГБУ «48 ЦНИИ» Минобороны России по научной работе, канд. биол. наук

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Dmitriy A. Kutaev. Deputy of Head of Federal State Budgetary Establishment “48 Central Scientific Research Institute” of the Ministry of Defense of the Russian Federation on scientific research, Cand. Sci. (Biol)

Oktiabrskaya St., 11, Sergiev Posad-6 141306

https://orcid.org/0000-0002-6742-3919БорисевичС. В.BorisevichS. V.

Борисевич Сергей Владимирович. Начальник ФГБУ «48 ЦНИИ» Минобороны России, д-р биол. наук, профессор, академик РАН

141306, г. Сергиев Посад-6, ул. Октябрьская, д. 11

Sergey V. Borisevich. Head of Federal State Budgetary Establishment “48 Central Scientific Research Institute” of the Ministry of Defense of the Russian Federation, Academician of Russian Academy of Sciences, Dr Sci. (Biol.), Professor

Oktiabrskaya St., 11, Sergiev Posad-6 141306

48cnii@mil.ruФедеральное государственное бюджетное учреждение «48 Центральный научно-исследовательский институт» Министерства обороны Российской ФедерацииРоссия48 Central Scientific Research Institute of the Ministry of Defense of the Russian FederationRussian Federation20240409202482164175Copyright © Петров А.А., Казанцев А.В., Ковальчук Е.А., Павлюков М.Ю., Сапкулов А.В., Кутаев Д.А., Борисевич С.В., 20242024Петров А.А., Казанцев А.В., Ковальчук Е.А., Павлюков М.Ю., Сапкулов А.В., Кутаев Д.А., Борисевич С.В.Petrov A.A., Kazantsev A.V., Kovalchuk E.A., Pavlyukov M.Y., Sapkulov A.V., Kutaev D.A., Borisevich S.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.nbsprot.ru/jour/article/view/358

Одним из современных направлений совершенствования лабораторной диагностики инфекционных болезней является внедрение методов секвенирования геномов опасных патогенов. Среди них наибольшее распространение в настоящее время приобрел метод секвенирования следующего поколения (Next Generation Sequencing, NGS).

Цель работы – определить перспективные направления и проблемы внедрения методов NGS в практику войск РХБ защиты Вооруженных Сил Российской Федерации Федерации (войск РХБ защиты ВС РФ).

Источниковая база исследования – данные, опубликованных в отечественных и англоязычных научных изданиях, доступных через сеть Интернет.

Метод исследования – аналитический.

Результаты

Результаты. Представлены материалы, обобщающие данные о трех поколениях технологий секвенирования (по Сэнгеру, NGS, секвенирования на основе нанопор), рассмотрены их преимущества и недостатки применительно к использованию в практике войск РХБ защиты ВС РФ. Показано, что последние тенденции в инструментах биоинформатики для анализа геномных данных сосредоточены на машинном обучении, облачных приложениях, удобных интерфейсах и программном обеспечении с открытым исходным кодом. Использование параллельных вычислений и оптимизация биоинформатических алгоритмов позволяют повысить эффективность обработки данных NGS секвенирования при неизменности технических характеристик используемого оборудования. Но поскольку большинство биоинформатических программ имеют открытый код, их сертификация органами военного управления представляется маловероятной.

Вывод

Вывод. Необходимым условием успешного внедрения NGS в практику подразделений войск РХБ защиты ВС РФ является решение двух основных проблем: 1) разработка отечественной аппаратной части (компьютеров достаточной мощности), сертифицированных для защиты информации в установленном порядке и при этом имеющих полный функционал; 2) разработка соответствующих биоинформатических инструментов и баз данных, сертифицированных для защиты информации в установленном порядке и функционирующих в автономном режиме без подключения к сети интернет.

One of the promising areas for improving laboratory diagnostics is the active introduction of next-generation sequencing (NGS), which retains the key advantages of molecular tests and allows the detection of a wide range of diverse microorganisms. Despite the significant advantages of NGS in the laboratory diagnosis of infectious diseases, there are also a number of objective obstacles to its widespread use.

The purpose of the work is to consider existing hardware and software solutions for whole-genome sequencing, as well as the prospects and problems of their implementation in the practice of the RCB protection troops of the Armed Forces of the Russian Federation for the identification of pathogens of various etiologies.

The source base of the research includes an analysis of data published in domestic and English-language scientific publications, as well as posted on the Internet.

The research method is analytical.

Results

Results. Materials are presented that summarize data on three generations of sequencing technologies, their advantages and disadvantages. It is shown that with the development of NGS technologies, many technical problems arise that need to be deeply analyzed and solved. Despite its great potential, NGS has not yet been widely adopted in clinical microbiology laboratories, partly due to the lack of standardized workflows.

Conclusions

Conclusions. For the successful use of NGS by units of the RCB protection troops of the Armed Forces of the Russian Federation, it is necessary to solve the main problem, which is the development of domestic hardware, appropriate bioinformatics tools and databases certified in accordance with the established procedure for functioning offiine without connecting to the Internet.

биоинформационные инструментыгеномДНКпатогенСенгерсеквенирование следующего поколениясеквенаторыbioinformatics toolsnext-generation sequencingsequencersГосударственное бюджетное учреждение «48 Центральный научно-исследовательский институт» Министерства обороны Российской Федерации48 Central Scientific Research Institute of the Ministry of Defence of the Russian FederationReferencesSozhamannan S, Holland MY, Hall AT, Negrón DA, Ivancich M, Koehler JW, et al. Evaluation of Signature Erosion in Ebola Virus Due to Genomic Drift and Its Impact on the Performance of Diagnostic Assays. Viruses. 2015;7(6):3130–54. https://doi.org/10.3390/v7062763Sozhamannan S, Holland MY, Hall AT, Negrón DA, Ivancich M, Koehler JW, et al. Evaluation of Signature Erosion in Ebola Virus Due to Genomic Drift and Its Impact on the Performance of Diagnostic Assays. Viruses. 2015;7(6):3130–54. https://doi.org/10.3390/v7062763Sohn JI, Nam JW. The present and future of de novo whole-genome assembly. Briefings in Bioinformatics. 2018;19(1):23–40. https://doi.org/10.1093/bib/bbw096Sohn JI, Nam JW. The present and future of de novo whole-genome assembly. Briefings in Bioinformatics. 2018;19(1):23–40. https://doi.org/10.1093/bib/bbw096Бородинов АГ, Манойлов ВВ, Заруцкий ИВ, Петров АИ, Курочкин ВЕ. Поколения методов секвенирования ДНК (обзор). Научное приборостроение. 2020;30(4):3–20.Borodinov AG, Manoilov VV, Zarutsky IV, Petrov AI, Kurochkin VE. Generations of DNA sequencing methods (review). Nauchnoe priborostroenie. 2020;30(4):3–20 (in Russian).Алексеев ЯИ, Белов ЮВ, Малюченко ОП, Монахова ЮА, Натыров АН, Орехов ВА и др. Генетический анализатор для фрагментного анализа ДНК. Научное приборостроение. 2012;22(4):86–92.Alekseev YaI, Belov YuV, Malyuchenko OP, Monakhova YuA, Natyrov AN, Orekhov VA, et al. Genetic analyzer for DNA fragment analysis. Nauchnoe priborostroenie. 2012;22(4):86–92 (in Russian).Зубов ВВ. Cеквенирование по Ротбергу (потенциал полупроводникового секвенирования). Биомика. 2013;5(1–2):48–61.Zubov VV. Rotberg sequencing (potential of semiconductor sequencing). Biomica. 2013;5(1–2):48–61 (in Russian).Зубов ВВ, Чемерис ДА, Василов РГ, Курочкин ВЕ, Алексеев ЯИ. Краткая история методов высокопроизводительного секвенирования нуклеиновых кислот. Биомика. 2021;13(1):27–46. https://doi.org/10.31301/2221-6197.bmcs.2021-4Zubov VV, Chemeris DA, Vasilov RG, Kurochkin VE, Alekseev YaI. Brief history of high-throughput nucleic acid sequencing methods. Biomics. 2021;13(1):27–46 (in Russian). https://doi.org/10.31301/2221-6197.bmcs.2021-4MacKenzie M, Argyropoulos C. An introduction to nanopore sequencing: past, present, and future considerations. Micromachines. 2023;14(2):459. https://doi.org/10.3390/mi14020459MacKenzie M, Argyropoulos C. An introduction to nanopore sequencing: past, present, and future considerations. Micromachines. 2023;14(2):459. https://doi.org/10.3390/mi14020459Pervez M, Hasnain MJ, Abbas SH, Moustafa MF, Aslam N, Shah SSM. A Comprehensive Review of Performance of Next-Generation Sequencing Platforms. Biomed Res Int. 2022; 2022:3457806. https://doi.org/10.1155/2022/3457806Pervez M, Hasnain MJ, Abbas SH, Moustafa MF, Aslam N, Shah SSM. A Comprehensive Review of Performance of Next-Generation Sequencing Platforms. Biomed Res Int. 2022; 2022:3457806. https://doi.org/10.1155/2022/3457806Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, Ordoukhanian P. Library construction for next-generation sequencing: overviews and challenges. Biotechniques. 2014;56(2):61–77. https://doi.org/10.2144/000114133Head SR, Komori HK, LaMere SA, Whisenant T, Van Nieuwerburgh F, Salomon DR, Ordoukhanian P. Library construction for next-generation sequencing: overviews and challenges. Biotechniques. 2014;56(2):61–77. https://doi.org/10.2144/000114133Marine R, Polson SW, Ravel J, Hatfull G, Russell D, Sullivan M, et al. Evaluation of a transposase protocol for rapid generation of shotgun high-throughput sequencing libraries from nanogram quantities of DNA. Appl Environ Microbiol. 2011;77(22):8071–9. https://doi.org/10.1128/AEM.05610-11Marine R, Polson SW, Ravel J, Hatfull G, Russell D, Sullivan M, et al. Evaluation of a transposase protocol for rapid generation of shotgun high-throughput sequencing libraries from nanogram quantities of DNA. Appl Environ Microbiol. 2011;77(22):8071–9. https://doi.org/10.1128/AEM.05610-11Knierim E, Lucke B, Schwarz JM. Systematic comparison of three methods for fragmentation of long-range PCR products for next generation sequencing. PLoS ONE. 2011;6(11):е28240. https://doi.org/10.1371/journal.pone.0028240Knierim E, Lucke B, Schwarz JM. Systematic comparison of three methods for fragmentation of long-range PCR products for next generation sequencing. PLoS ONE. 2011;6(11):е28240. https://doi.org/10.1371/journal.pone.0028240Podnar J, Deiderick H, Huerta G, Hunicke-Smith S. Next-Generation Sequencing RNA-Seq Library Construction. Curr Protoc Mol Biol. 2014;106(1):4.21.1–19. https://doi.org/10.1002/0471142727.mb0421s106Podnar J, Deiderick H, Huerta G, Hunicke-Smith S. Next-Generation Sequencing RNA-Seq Library Construction. Curr Protoc Mol Biol. 2014;106(1):4.21.1–19. https://doi.org/10.1002/0471142727.mb0421s106Barzon L, Lavezzo E, Militello V, Toppo S, Palù G. Applications of next-generation sequencing technologies to diagnostic virology. Int J Mol Sci. 2011;12(11):7861–84. https://doi.org/10.3390/ijms12117861Barzon L, Lavezzo E, Militello V, Toppo S, Palù G. Applications of next-generation sequencing technologies to diagnostic virology. Int J Mol Sci. 2011;12(11):7861–84. https://doi.org/10.3390/ijms12117861Wong K, Fong TT, Bibby K, Molina M. Application of enteric viruses for fecal pollution source tracking in environmental waters. Environ Int. 2012;45:151–64. https://doi.org/10.1016/j.envint.2012.02.009Wong K, Fong TT, Bibby K, Molina M. Application of enteric viruses for fecal pollution source tracking in environmental waters. Environ Int. 2012;45:151–64. https://doi.org/10.1016/j.envint.2012.02.009Zhang L, Chen F, Zeng Z, Xu M, Sun F, Yang L, et al. Advances in Metagenomics and Its Application in Environmental Microorganisms. Front Microbiol. 2021;12:е766364. https://doi.org/10.3389/fmicb.2021.766364Zhang L, Chen F, Zeng Z, Xu M, Sun F, Yang L, et al. Advances in Metagenomics and Its Application in Environmental Microorganisms. Front Microbiol. 2021;12:е766364. https://doi.org/10.3389/fmicb.2021.766364Sen P, Oresic M. Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview. Metabolites. 2019;9(2):22. https://doi.org/10.3390/metabo9020022Sen P, Oresic M. Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview. Metabolites. 2019;9(2):22. https://doi.org/10.3390/metabo9020022Schlaberg R, Chiu CY, Miller S, Procop GW, Weinstock G. Validation of Metagenomic Next-Generation Sequencing Tests for Universal Pathogen Detection. Arch Pathol Lab Med. 2017;141(6):776–86. https://doi.org/10.5858/arpa.2016-0539-RASchlaberg R, Chiu CY, Miller S, Procop GW, Weinstock G. Validation of Metagenomic Next-Generation Sequencing Tests for Universal Pathogen Detection. Arch Pathol Lab Med. 2017;141(6):776–86. https://doi.org/10.5858/arpa.2016-0539-RASeol D, Jhang SY, Kim H, Kim SY, Kwak HS, Kim SH, et al. Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data. Front Microbiol. 2019;10:1683. https://doi.org/10.3389/fmicb.2019.01683Seol D, Jhang SY, Kim H, Kim SY, Kwak HS, Kim SH, et al. Accurate and Strict Identification of Probiotic Species Based on Coverage of Whole-Metagenome Shotgun Sequencing Data. Front Microbiol. 2019;10:1683. https://doi.org/10.3389/fmicb.2019.01683Afridi OK, Ali J, Chang JH. Next-Generation Sequencing Based Gut Resistome Profiling of Broiler Chickens Infected with Multidrug-Resistant Escherichia coli. Animals (Basel). 2020;10(12):2350. https://doi.org/10.3390/ani10122350Afridi OK, Ali J, Chang JH. Next-Generation Sequencing Based Gut Resistome Profiling of Broiler Chickens Infected with Multidrug-Resistant Escherichia coli. Animals (Basel). 2020;10(12):2350. https://doi.org/10.3390/ani10122350Del Chierico F, Ancora M, Marcacci M, Cammà C, Putignani L, Conti S. Choice of next-generation sequencing pipelines. Methods Mol Biol. 2015;1231:31–47. https://doi.org/10.1007/978-1-4939-1720-4_3Del Chierico F, Ancora M, Marcacci M, Cammà C, Putignani L, Conti S. Choice of next-generation sequencing pipelines. Methods Mol Biol. 2015;1231:31–47. https://doi.org/10.1007/978-1-4939-1720-4_3McCorrison J, Venepally P, Sing I, Fouts DE, Lasken RS, Methé BA. NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly. BMC Bioinformatics. 2014;15(1):357. https://doi.org/10.1186/s12859-014-0357-3McCorrison J, Venepally P, Sing I, Fouts DE, Lasken RS, Methé BA. NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly. BMC Bioinformatics. 2014;15(1):357. https://doi.org/10.1186/s12859-014-0357-3Jia X, Hu L, Wu M, Ling Y, Wang W, Lu H, et al. A streamlined clinical metagenomic sequencing protocol for rapid pathogen identification. Sci Rep. 2021;11(1):4405. https://doi.org/10.1038/s41598-021-83812-xJia X, Hu L, Wu M, Ling Y, Wang W, Lu H, et al. A streamlined clinical metagenomic sequencing protocol for rapid pathogen identification. Sci Rep. 2021;11(1):4405. https://doi.org/10.1038/s41598-021-83812-xParker K, Wood H, Russell JA, Yarmosh D, Shteyman A, Bagnoli J, et al. Development and optimization of an unbiased, metagenomics-based pathogen detection workflow for infectious disease and biosurveillance applications. Trop Med Infect Dis. 2023;8(2):121. https://doi.org/10.3390/tropicalmed8020121Parker K, Wood H, Russell JA, Yarmosh D, Shteyman A, Bagnoli J, et al. Development and optimization of an unbiased, metagenomics-based pathogen detection workflow for infectious disease and biosurveillance applications. Trop Med Infect Dis. 2023;8(2):121. https://doi.org/10.3390/tropicalmed8020121Ibañez-Lligoña M, Colomer-Castell S, González-Sánchez A, Gregori J, Campos C, Garcia-Cehic D, et al. Bioinformatic tools for NGS-based metagenomics to improve the clinical diagnosis of emerging, re-emerging and new viruses. Viruses. 2023;15(2):587. https://doi.org/10.3390/v15020587Ibañez-Lligoña M, Colomer-Castell S, González-Sánchez A, Gregori J, Campos C, Garcia-Cehic D, et al. Bioinformatic tools for NGS-based metagenomics to improve the clinical diagnosis of emerging, re-emerging and new viruses. Viruses. 2023;15(2):587. https://doi.org/10.3390/v15020587Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1). https://doi.org/10.1186/s13059-019-1891-0Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1). https://doi.org/10.1186/s13059-019-1891-0Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104. https://doi.org/10.7717/peerj-cs.104Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104. https://doi.org/10.7717/peerj-cs.104Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics. 2015;16: 236. https://doi.org/10.1186/s12864-015-1419-2Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics. 2015;16: 236. https://doi.org/10.1186/s12864-015-1419-2Blanco-Míguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, et al. Extending and Improving Metagenomic Taxonomic Profiling with Uncharacterized Species with using MetaPhlAn 4. Nat Biotechnol. 2023;41(11):1633–4. https://doi.org/10.1038/s41587-023-01688-wBlanco-Míguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, et al. Extending and Improving Metagenomic Taxonomic Profiling with Uncharacterized Species with using MetaPhlAn 4. Nat Biotechnol. 2023;41(11):1633–4. https://doi.org/10.1038/s41587-023-01688-wNayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. New insights from uncultivated genomes of the global human gut microbiome. Nature. 2019;568(7753):505–10. https://doi.org/10.1038/s41586-019-1058-xNayfach S, Shi ZJ, Seshadri R, Pollard KS, Kyrpides NC. New insights from uncultivated genomes of the global human gut microbiome. Nature. 2019;568(7753):505–10. https://doi.org/10.1038/s41586-019-1058-xRaju RS, Al Nahid A, Chondrow Dev P, Islam R. VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment. Genomics. 2022;114:10414. https://doi.org/10.1016/j.ygeno.2022.110414Raju RS, Al Nahid A, Chondrow Dev P, Islam R. VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment. Genomics. 2022;114:10414. https://doi.org/10.1016/j.ygeno.2022.110414Roux S, Tournayre J, Mahul A, Debroas D, Enault F. Metavir 2: New tools for viral metagenome comparison and assembled virome analysis. BMC Bioinformatics. 2014;15:76. https://doi.org/10.1186/1471-2105-15-76Roux S, Tournayre J, Mahul A, Debroas D, Enault F. Metavir 2: New tools for viral metagenome comparison and assembled virome analysis. BMC Bioinformatics. 2014;15:76. https://doi.org/10.1186/1471-2105-15-76Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985. https://doi.org/10.7717/peerj.985Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985. https://doi.org/10.7717/peerj.985Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: A novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5(1):69. https://doi.org/10.1186/s40168-017-0283-5Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: A novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5(1):69. https://doi.org/10.1186/s40168-017-0283-5

The authors declare that there are no conflicts of interest present.