medsovetМедицинский СоветMeditsinskiy sovet = Medical Council2079-701X2658-5790REMEDIUM GROUP Ltd.10.21518/ms2025-351medsovet-9544Research ArticleПРАКТИКАPRACTICEОт данных секвенирования к пониманию болезни: как врачу обработать NGS-данные пациента на своем компьютереFrom sequencing data to disease understanding: How can a doctor process patient’s NGS data on their own computerhttps://orcid.org/0000-0001-5870-8042КорнеенковА. А.KorneenkovA. A.

Корнеенков Алексей Александрович, д.м.н., профессор, заведующий научно-исследовательской лабораторией клинической информати­ки и биостатистики

190013, Санкт-Петербург, ул. Бронницкая, д. 9

Aleksei A. Korneenkov, Dr. Sci. (Med.), Professor, Head of the Research Laboratory of Clinical Informatics and Biostatistics

9, Bronnitskaya St., St Petersburg, 190013

alkorneenkov@yandex.ruhttps://orcid.org/0000-0001-9195-128XЯновЮ. К.YanovYu. K.

Янов Юрий Константинович, д.м.н., профессор, академик РАН, профессор кафедры оториноларингологии, Военно-медицинская ака­демия имени С.М. Кирова; профессор, кафедра оториноларингологии, Северо-Западный государственный медицинский университет имени И.И. Мечникова

194044, Санкт-Петербург, ул. Академика Лебедева, д. 6,

191015, Санкт-Петербург, ул. Кирочная, д. 41

Yuri K. Yanov, Dr. Sci. (Med.), Professor, Member of the Russian Academy of Sciences, Military Medical Academy named after S.M. Kirov; Professor, Department of Otolaryngology, North-Western State Medical University named after I.I. Mechnikov

6, Akademik Lebedev St., St Petersburg, 194044,

41, Kirochnaya St., St Petersburg, 191015

9153764@mail.ruhttps://orcid.org/0000-0002-4141-2226ВяземскаяЕ. Э.VyazemskayaE. E.

Вяземская Елена Эмильевна, инженер научно-исследовательской лаборатории клинической информатики и биостатистики

190013, Санкт-Петербург, ул. Бронницкая, д. 9

Elena E. Vyazemskaya, Engineer of the Research Laboratory of Clinical Informatics and Biostatistics

9, Bronnitskaya St., St Petersburg, 190013

vyazemskaya.elena@gmail.comhttps://orcid.org/0009-0002-6921-5299МедведеваА. Ю.MedvedevaA. Yu.

Медведева Анна Юрьевна, инженер научно-исследовательской лаборатории клинической информатики и биостатистики

190013, Санкт-Петербург, ул. Бронницкая, д. 9

Anna Y. Medvedeva, Engineer of the Research Laboratory of Clinical Informatics and Biostatistics

9, Bronnitskaya St., St Petersburg, 190013

a.medvedeva@niilor.ruСанкт-Петербургский научно-исследовательский институт уха, горла, носа и речиРоссияSaint Petersburg Research Institute of Ear, Throat, Nose and SpeechRussian FederationВоенно-медицинская академия имени С.М. Кирова; Северо-Западный государственный медицинский университет имени И.И. МечниковаРоссияMilitary Medical Academy named after S.M. Kirov; North-Western State Medical University named after I.I. MechnikovRussian Federation202520112025018108121Copyright © Корнеенков А.А., Янов Ю.К., Вяземская Е.Э., Медведева А.Ю., 20252025Корнеенков А.А., Янов Ю.К., Вяземская Е.Э., Медведева А.Ю.Korneenkov A.A., Yanov Y.K., Vyazemskaya E.E., Medvedeva A.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.med-sovet.pro/jour/article/view/9544Введение

Введение. Современный врач вынужден становиться специалистом широкого профиля, сочетая глубокие медицинские знания с техническими компетенциями. Доступность геномных исследований за последние десятилетия резко возросла, однако, для полной их интеграции в медицинскую практику еще существует много препятствий. Учитывая лавинообразный рост новых знаний об ассоциациях геномных данных с болезнями человека, может возникнуть медицинская потребность их самостоятельного анализа для назначения дополнительных медико-генетических исследований, особенно, когда у пациента уже есть полученные ранее NGS-данные (например, экзома).

Цель

Цель. Разработать и предоставить детализированное руководство по проведению самостоятельного биоинформатического анализа NGS-данных пациента.

Материалы и методы

Материалы и методы. Исходными данными являются примеры файлов NGS-данных, предоставляемые пациенту после проведения медико-генетического исследования. Используются реализации как известных, так и самостоятельно разра­ботанных программных алгоритмов выравнивания по референсному геному, обнаружения вариантов, их фильтрации по заданным критериям качества, генам (и их транскриптам) и оценки влияния на здоровье.

Результаты

Результаты. Разработан общий алгоритм и программный биоинформационный конвейер обработки и анализа данных секвенирования с использованием команд интерфейса Linux, docker-контейнеров известных биоинформатических инстру­ментов bwa, gatk, samtools, bcftools, программ R на основе пакетов проекта Bioconductor и собственных разработок. Этот алгоритм позволяет медицинскому специалисту самостоятельно получать и интерпретировать варианты генетических последовательностей из NGS-данных пациентов.

Выводы

Выводы. Полученная с помощью этого конвейера информация может служить основой для дальнейших работ по диагно­стике наследственных заболеваний, персонализированной медицине и фармакогенетике. Использование предложенного алгоритма позволяет достичь поставленных целей и получить на персональных компьютерах варианты геномной последова­тельности (экзома) пациента, пригодные для последующего анализа и интерпретации. Предполагается, что компьютер врача сможет справиться с подобной задачей за разумное время, обеспечивая надежную и воспроизводимую обработку данных.

Introduction

Introduction. In modern medicine, physicians are increasingly required to be versatile specialists, combining in-depth medical knowledge with technical expertise. While the accessibility of genomic research has dramatically increased over the past few decades, its full integration into medical practice still faces significant challenges. Given the rapid proliferation of new knowl­edge regarding the associations between genomic data and human diseases, there is a growing clinical need for physicians to be able to analyze this data themselves. This is especially true for subsequent medico-genetic studies, particularly when patients already have existing Next-Generation Sequencing (NGS) data (e.g., from exome sequencing).

Aim

Aim. The objective of this study is to develop and provide a detailed guide for medical specialists to independently perform bioinformatics analysis of a patient’s NGS data.

Materials and methods

Materials and methods. The source data for this study are examples of NGS data files provided to patients following a medicogenetic examination. We used both established and custom-developed software algorithms for read alignment against a refer­ence genome, variant discovery, variant filtering based on quality criteria and specific genes (and their transcripts), and assess­ing their potential health impact.

Results

Results. We developed a comprehensive algorithm and a bioinformatics processing pipeline for sequencing data analysis. This pipeline utilizes a Linux command-line interface, along with Docker containers for established bioinformatics tools such as bwa, gatk, samtools, and bcftools, as well as R scripts based on the Bioconductor project and our own proprietary developments. This algorithm allows medical professionals to independently obtain and interpret genetic variants from a patient’s NGS data.

Conclusion

Conclusion. The information obtained through this pipeline can serve as a foundation for further work in diagnosing hereditary diseases, personalized medicine, and pharmacogenetics. The proposed algorithm effectively achieves the study’s objective, enabling the retrieval of patient genomic sequence variants (exomes) suitable for subsequent analysis and interpretation on a personal computer. We anticipate that a physician’s computer can handle this task in a reasonable amount of time, ensuring reliable and reproducible data processing.

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