Ramakrishnan Reshmi, Washington Ashitha, Suveena S, Rani J R, Oommen Oommen V
GENEFiTHUB, Ernakulam, Kochi, Kerala, India.
Computational Biology and Bioinformatics Lab, Department of Bioscience and Engineering, National Institute of Technology Calicut, Kozhikode, Kerala, India.
Methods Mol Biol. 2025;2952:459-482. doi: 10.1007/978-1-0716-4690-8_25.
The last decade has witnessed an explosion in NGS data, which was the gift of advances in NGS technology as well as computing power. Along with AI, NGS is revolutionizing healthcare research. In this chapter, we briefly discuss the contribution of NGS in dealing with the COVID-19 pandemic and mention its application across various fields like oncology, agriculture, archaeogenetics, and space biology, followed by a historical perspective on sequencing, the evolution of NGS technologies and those currently in use. The chapter further outlines various NGS methods and workflows, detailing the key stages and the tools commonly employed for efficient analysis. Additionally, we highlight the surge and complexity of NGS data generated by genomics, transcriptomics, and microbiome studies, challenges and discusses their clinical applications. Toward the end, we explore the future directions of NGS. Given the rapid increase in data volume and complexity, there is an urgent need for efficient big data technologies, state-of-the-art tools, and techniques to manage, analyze, and derive actionable insights from these vast datasets, addressing the demands of the present-day scientific landscape.
过去十年见证了二代测序（NGS）数据的爆炸式增长，这得益于NGS技术以及计算能力的进步。与人工智能一起，NGS正在彻底改变医疗保健研究。在本章中，我们简要讨论NGS在应对新冠疫情中的贡献，并提及它在肿瘤学、农业、古遗传学和空间生物学等各个领域的应用，随后是对测序的历史回顾、NGS技术的演变以及目前使用的技术。本章进一步概述了各种NGS方法和工作流程，详细介绍了关键阶段以及高效分析常用的工具。此外，我们强调了基因组学、转录组学和微生物组研究产生的NGS数据的激增和复杂性、挑战，并讨论了它们的临床应用。在结尾部分，我们探讨了NGS的未来发展方向。鉴于数据量和复杂性的快速增加，迫切需要高效的大数据技术、最先进的工具和技术来管理、分析这些海量数据集并从中获得可操作的见解，以满足当今科学领域的需求。
