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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.

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From DNA to Big Data: NGS Technologies and Their Applications

  • 实验方案

  • 发表日期：

    2025年

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  • Reshmi Ramakrishnan,
  • Ashitha Washington,
  • S. Suveena,
  • J. R. Rani,
  • Oommen V. Oommen

摘要

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.