What is NGS?

What is Next Generation Sequencing?

Over the past few decades, there has been a remarkable shift in the manner in which oncology patients are screened, diagnosed, and treated. These advancements can largely be attributed to the growth of the field of precision medicine, as individual molecular profiling begins to take precedent over traditional generalized therapy methods.
Comprehensive genome profiling is enabled by a technology known as next-generation sequencing (NGS), which has come leaps and bounds in the past few years and offers the field of oncology new insights into diagnosis, classification, prognostication, and ultimately precision treatment.
The first concerted effort at DNA sequencing was explored by the Human Genome Project, which took over 13 years and cost 3 billion dollars to sequence a single human genome. Currently, NGS allows us to generate detailed reports in about 24 hours and for under 1,000 dollars. NGS is a high-throughput method that utilizes massively parallel sequencing of numerous individual DNA fragments, without the need of a reference genome, and can thus simultaneously sequence multiple genes at a time.
A general NGS workflow requires sample processing, nucleic acid extraction, library preparation, and target amplification before the sequencing is conducted. Finally, bioinformatics data analysis, gene annotation, and interpretation are often performed in order to glean insights from the raw data.
There are multiple approaches for NGS which will differ based on the intent of the research, extent of target enrichment, and the type of sequencing that is utilized.
Whole-genome sequencing (WGS) and whole-exome sequencing (WES) are powerful tools that can facilitate the identification of disease-causing genes, but often produce hefty raw data that is difficult to process bioinformatically. In contrast, utilizing targeted gene panels, the most common clinical approach for cancer genotyping, allows for the comprehensive analysis of anywhere from a dozen to a few hundred actionable genes of interest at high coverage. Targeted gene panels feature lower costs than WES and WGS, greater analytically sensitivity, and pose fewer problems for bioinformatic pipelines in the data analysis phase.

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