اصول و کاربردهای فناوری توالی‌یابی نسل جدید (NGS) در علوم زیستی (با رویکرد به‌نژادی غلات)

نوع مقاله : مروری

نویسندگان

1 دانش آموخته کارشناسی ارشد، گروه زراعت و اصلاح نباتات، دانشکده علوم و مهندسی کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 نویسنده مسئول مکاتبات، استاد، گروه تولید و ژنتیک گیاهی، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران.

3 دانشیار، گروه زراعت و اصلاح نباتات، دانشکده علوم و مهندسی کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

4 دانش آموخته کارشناسی ارشد، انستیتو علوم اعصاب تولوز، فرانسه.

5 استاد، گروه بیوتکنولوژی کشاورزی، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران.

چکیده

مقدمه: از زمان معرفی توالی‌یابی نسل جدید (NGS)، در اوایل دهه ۲۰۰۰، این فناوری  به ‌عنوان یکی از تحولات بنیادین در علوم زیستی، باعث پیشرفت چشمگیر در تحقیقات ژنومیک، ترنسکریپتوم ، اپیژنوم و... شده است. اصول فناوری NGS شامل مباحث مربوط به تهیه کتابخانه‌، توالی‌یابی و تحلیل داده‌های حاصل از آن است. این فناوری با توالی‌یابی میلیون‌ها قطعه DNA به‌صورت موازی و با دقت بالا و هزینه پایین و  همچنین تولید حجم زیادی از داده‌های ژنومی در زمان کوتاه، توانسته است جایگزین روش‌های قدیمی‌تری مانند توالی‌یابی سنگر شود و با توالی‌یابی سریع، دقیق و کامل ژنوم‌ها و نواحی هدف انقلابی بزرگ در درک پیچیدگی‌های ژنتیکی، ساختار ژنوم و تعیین تنوع ژنتیکی ایجاد کند. از مهم‌ترین کاربردهای NGS در علوم زیستی می‌توان به شناسایی و مطالعه ژن‌های مرتبط با صفات کمی و کیفی، مطالعات تنوع ژنتیکی و ژنتیک جمعیت، تشخیص بیماری‌های ژنتیکی، اپیدمیولوژی، میکروبیوم‌شناسی، پزشکی قانونی، فیلوژنتیک، زیست‌شناسی سامانه‌ای، مهندسی ژنتیک و ویرایش ژنوم و  اصلاح نبات و دام اشاره کرد. بااین‌حال، استفاده مؤثر از داده‌های NGS مستلزم توسعه زیرساخت‌های محاسباتی قوی و الگوریتم‌های پیشرفته و همچنین گسترش اطلاعات محققان در رابطه با کاربردها و چالش‌های بیوانفورماتیکی مرتبط با داده‌های NGS، برای تحلیل و تفسیر این حجم عظیم از اطلاعات است.
مواد و روش‌ها: مقاله حاضر یک مقاله مروری می­باشد که به شیوه تحلیل محتوا (Content analysis) با جستجوی کلید واژه‌های توالی­یابی نسل جدید (NGS)، انواع توالی‌یابی NGS، تجزیه و تحلیل داده­های NGS، کاربردهای توالی‌یابیNGS ، در مقاله‌های مرتبط در پایگاه­های اینترنتی PubMed ،Web of science ،Google scholar و Scopus به دست آمده است.
یافته‌ها: این مطالعه قصد دارد با مرور تفصیلی توالی‌یابی‌های نسل اول، دوم و سوم ، بررسی مسیر تجزیه و تحلیل داده‌های NGS و کاربردهای گسترده NGS در زمینه‌های مختلف از جمله تحقیقات غلات، راهنمایی تقریباً کاملی برای تجزیه و تحلیل کارآمد و بهینه داده های حاصل از توالی‌یابی نسل جدید ارائه نماید. به این‌منظور در بخش اول به مرور توالی‌یابی‌های نسل اول (ماکسام-گیلبرت و سنگر)، نسل‌دوم (Illumina، ABI/SOLID، Roche/454 pyrosequencing، Ion Torrent) و نسل سوم (Heliscope، SMRT، Oxford Nanopore) پرداخته شد. سپس در بخش دوم انواع توالی‌یابی‌های NGS مانند: Whole genome sequencing; WGS، Whole exome sequencing; WES، Bulk RNA-seq و سایر روش ها معرفی و مسیر تجزیه و تحلیل آنها بررسی شده‌اند و در ادامه کاربرد توالی‌یابی نسل جدید در حوزه های مختف مانند شناسایی تنوعات ساختاری ژنومی (SVs)، مطالعه تغییرات اپی‌ژنتیکی، تجزیه و تحلیل جمعیت میکروبی، کشاورزی (با تأکید بر بهنژادی غلات) توضیح داده شد. در نهایت مزایا و چالش‌های پیشروی توالی­یابی نسل جدید بیان گردید.
نتیجه‌گیری: توالی‌یابی نسل جدید به عنوان یک فناوری انقلابی در ژنومیک، تأثیر بسزایی در تحقیقات علوم زیستی داشته است. کاهش هزینه‌ها و افزایش دقت توالی‌یابی به همراه توسعه روش‌های جدید، باعث شده است تا NGS به ابزاری کلیدی برای درک بهتر ژنتیک و توسعه راهبردهای درمانی شخصی‌سازی شده تبدیل شود. با پیشرفت‌های مداوم در این حوزه و ترکیب این فناوری با هوش مصنوعی، آینده‌ی NGS در تحلیل دقیق‌تر داده‌های ژنتیکی و بهبود فرایندهای درمانی بسیار روشن به نظر می‌رسد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Principles and Applications of Next-Generation Sequencing (NGS) Technology in Life Sciences (with a Focus on Cereal Breeding)

نویسندگان [English]

  • Omid Mohammadalizadeh 1
  • Reza Darvishzadeh 2
  • Valiollah Mohammadi 3
  • Somaieh Soufimaleky 4
  • Danial Kahrizi 5
1 MSc Graduate of Agricultural Biotechnology, Department of Agronomy & Plant Breeding, Faculty of Sciences and Agricultural Engineering, Tehran University, Karaj, Iran.
2 Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia. Iran.
3 Associate Professor, Department of Agronomy & Plant Breeding, Faculty of Sciences and Agricultural Engineering, Tehran University, Karaj, Iran.
4 MSc Graduate, Institut des Sciences du Cerveau de Toulouse, France.
5 Professor, Department of Agricultural Biotechnology, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
چکیده [English]

Introduction: Since the introduction of Next-Generation Sequencing (NGS) in the early 2000s, this technology has emerged as a transformative advancement in the life sciences, significantly propelling genomic, transcriptomic, epigenomic, and other related research fields. The core principles of NGS technology encompass library preparation, sequencing, and the analysis of the resulting data. By enabling the parallel sequencing of millions of DNA fragments with high accuracy, low cost, and rapid turnaround, NGS has effectively replaced older methods like Sanger sequencing. It has revolutionized our understanding of genetic complexities, genome structures, and genetic diversity through the swift and precise sequencing of entire genomes and target regions. Key applications of NGS in the life sciences include the identification and study of genes related to quantitative and qualitative traits, genetic diversity studies, population genetics, the diagnosis of genetic diseases, epidemiology, microbiome analysis, forensic science, phylogenetics, systems biology, genetic engineering, genome editing, and plant and animal breeding. However, the effective use of NGS data necessitates the development of robust computational infrastructure and advanced algorithms, as well as the expansion of researchers' knowledge regarding the bioinformatic applications and challenges associated with NGS data analysis and interpretation.
Materials and methods: The present article is a review paper, conducted through content analysis by searching for keywords related to Next-Generation Sequencing (NGS), types of NGS sequencing, NGS data analysis, and the applications of NGS in relevant articles found in online databases such as PubMed, Web of Science, Google Scholar, and Scopus.
Results: This study aims to provide a comprehensive guide for the efficient and optimal analysis of NGS data by thoroughly reviewing first-, second-, and third-generation sequencing methods, examining NGS data analysis pipelines, and exploring the broad applications of NGS in various fields, including cereal research. The first section reviews first-generation sequencing (Maxam-Gilbert and Sanger), second-generation sequencing (Illumina, ABI/SOLID, Roche/454 pyrosequencing, Ion Torrent), and third-generation sequencing (Heliscope, SMRT, and Oxford Nanopore). The second section introduces various NGS sequencing methods, such as Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), Bulk RNA-Seq, and others, and examines their analysis pathways. The subsequent discussion elaborates on the application of NGS in diverse areas, including the identification of structural genomic variations (SVs), the study of epigenetic changes, microbial population analysis, and agriculture (with an emphasis on cereal breeding). Finally, the advantages and challenges of NGS are discussed.
Conclusion: As a revolutionary technology in genomics, Next-Generation Sequencing has profoundly impacted life sciences research. The reduction in sequencing costs, coupled with increased accuracy and the development of new methods, has positioned NGS as a critical tool for a deeper understanding of genetics and the development of personalized therapeutic strategies. With ongoing advancements in this field and the integration of NGS with artificial intelligence, the future of NGS in enhancing the precision of genetic data analysis and improving therapeutic processes appears promising.

کلیدواژه‌ها [English]

  • Big Data Analysis
  • Bioinformatics
  • Sequencing Platforms
  • Next-Generation Sequencing (NGS)

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بیوتکنولوژی و بیوشیمی غلات
دوره 3، شماره 1 - شماره پیاپی 1
فروردین 1403
صفحه 110-231

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