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Next-Generation Sequencing

Massively parallel sequencing

Next-generation sequencing (NGS) is a term that broadly captures several related technologies that enable massively parallel or deep sequencing coverage for a selected region or the entire genome of an organism. Essential within the discipline of genomics-based research, sequencing technologies have existed for decades. However, the continual advancement of NGS or massively parallel DNA and RNA sequencing technologies have provided researchers with increases in genome-wide sequencing coverage and data analysis tools while rapidly decreasing in cost. Applications for NGS extend beyond whole genome analysis as it has significant implications for recent advancements in fundamental genomics and disease research alike.


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Related Protocols


NGS Methods Overview

While the methodology and reagents for NGS are continuously evolving, there are now numerous NGS systems that are available to researchers. Commonly used platforms incorporate the use of several critical steps in the NGS workflow, including sample or library preparation, cluster generation, sequencing, and data analysis. Sample preparation typically involves either DNA amplification or the addition of sequence linkers or adaptors. Cluster generation of each DNA sequence is when DNA containing the covalently attached linker hybridizes to a solid surface for bridge PCR amplification, or by alternate methods such as emulsion PCR. Additionally, there are many DNA sequencing methods, including sequencing by ligation, sequencing by synthesis, pyrosequencing, and ion semiconductor sequencing. Each sequencing method involves varying reaction steps and chemistries that ultimately determine the length of each sequence (read length), error rate, and reagent cost.

Analytical Approaches for NGS Data Analyses

A final element for all NGS workflows is the critical data analysis step that occurs after sequencing. While each NGS platform and workflow produce an enormous amount of digital information captured on computers, the raw data set must be analyzed by bioinformaticians using a continuously increasing number of analytical tools for read alignment and mapping, such as Bowtie, Galaxy, and many others. Many of the developments in the field of NGS technologies has come from the merger of numerous scientific fields to develop and optimize the analysis and interpretation of such large data sets. Depending on the specific application needs, researchers are now able to use these powerful tools to sequence entire genomes, exomes, or transcriptomes for fundamental and disease research studies.