[Paper Review] Principle, analysis, application and challenges of next-generation sequencing: a review

Khalid Raza, Sabahuddin Ahmad|arXiv (Cornell University)|Jun 15, 2016

Genomics and Phylogenetic Studies47 references7 citations

TL;DR

This review synthesizes the principles, applications, and computational challenges of next-generation sequencing (NGS), highlighting its high-throughput, parallelized sequencing capabilities that enable rapid, cost-effective analysis of vast genomic datasets. It emphasizes NGS's transformative role in advancing medical research, crop improvement, and disease treatment through specialized bioinformatics tools and data analysis pipelines.

ABSTRACT

Next Generation Sequencing (NGS), a recently evolved technology, have served a lot in the research and development sector of our society. This novel approach is a newbie and has critical advantages over the traditional Capillary Electrophoresis (CE) based Sanger Sequencing. The advancement of NGS has led to numerous important discoveries, which could have been costlier and time taking in case of traditional CE based Sanger sequencing. NGS methods are highly parallelized enabling to sequence thousands to millions of molecules simultaneously. This technology results into huge amount of data, which need to be analysed to conclude valuable information. Specific data analysis algorithms are written for specific task to be performed. The algorithms in group, act as a tool in analysing the NGS data. Analysis of NGS data unravels important clues in quest for the treatment of various life-threatening diseases; improved crop varieties and other related scientific problems related to human welfare. In this review, an effort was made to address basic background of NGS technologies, possible applications, computational approaches and tools involved in NGS data analysis, future opportunities and challenges in the area.

Motivation & Objective

• To provide a comprehensive overview of the foundational principles and technological evolution of next-generation sequencing (NGS).
• To examine the diverse applications of NGS in biomedical research, agriculture, and human health.
• To analyze the computational tools and algorithms used in NGS data processing.
• To identify key challenges and future opportunities in NGS technology and data analysis.

Proposed method

• The paper employs a narrative review methodology to synthesize existing literature on NGS technologies and their applications.
• It outlines the technical architecture of NGS platforms, emphasizing their parallelization of sequencing reactions.
• It details the computational pipelines used for NGS data analysis, including alignment, variant calling, and quality control.
• It evaluates specialized bioinformatics tools and algorithms tailored for specific NGS tasks such as RNA-Seq, ChIP-Seq, and whole-genome sequencing.
• The review discusses data management and computational challenges arising from the high volume and complexity of NGS-generated data.
• It integrates insights from published studies to assess the impact and limitations of current NGS workflows.

Experimental results

Research questions

• RQ1 What are the core technological principles underlying next-generation sequencing and how do they differ from Sanger sequencing?
• RQ2 What are the major applications of NGS in life sciences and human welfare?
• RQ3 Which computational tools and algorithms are essential for effective NGS data analysis?
• RQ4 What are the primary challenges in handling and interpreting large-scale NGS datasets?
• RQ5 What future opportunities and developments are anticipated in the field of NGS?

Key findings

• NGS enables the simultaneous sequencing of thousands to millions of DNA fragments, drastically increasing throughput compared to Sanger sequencing.
• The technology has accelerated discoveries in genomics, leading to faster and more cost-effective identification of genetic factors in diseases.
• Specialized bioinformatics tools are essential for processing and interpreting the massive data volumes generated by NGS platforms.
• NGS data analysis relies on algorithmic pipelines tailored to specific applications such as gene expression, epigenetics, and structural variation detection.
• Despite its advantages, NGS presents significant computational and data management challenges due to data volume and complexity.
• The review identifies ongoing opportunities for innovation in data analysis, integration, and scalability to support future genomic research.