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What is Next Generation Sequencing?


Next generation sequencing (NGS), or massively parallel high-throughput sequencing, enables sequence profiling of everything from genomes and transcriptomes to DNA-protein interactions. As an integral part of genomic research NGS is now redefining several additional areas of scientific research—from biology to agriculture, to environmental research.

What is Next Generation Sequencing?

Next-generation sequencing, also known as massively parallel sequencing, is a high-throughput, rapid, and scalable sequencing alternative to first generation Sanger sequencing. With NGS, researchers can sequence millions of DNA fragments from hundreds of samples all on a single sequencing run, making NGS a fast, cost-effective method for genomic research.

A typical NGS workflow

NGS starts with genetic material—either DNA or RNA—extracted from cells or tissue. Then, a sequencing “library” is created by fragmenting the DNA (either sample DNA or cDNA created from RNA) into relatively short (100–300 bp) segments. Adapters are ligated to the DNA in a process called sample indexing that finishes the library and ensures it can be successfully sequenced by the chosen platform. The fragments contain sample barcodes (or indexes) to allow for multiple samples to be sequenced on the same run (sample multiplexing).

If there are specific regions of interest in the genome such as marker genes, or gene variants, these can be enriched by hybridization capture or amplicon sequencing. Target enrichment focuses sequencing to specific targets of interest.

The library molecules are then sequenced, and the sequencing data—called reads—are pieced back together like a jigsaw puzzle to elucidate the genomic sequence.


  • Beckman Coulter Life Sciences Genomics Reagents

  • Illumina
  • Pacific Biosciences
  • Oxford Nanopore Technologies
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NGS 101 application guide

This detailed overview walks you through major advances in sequencing technology, types of next generation sequencing, their applications and more.

Advantages of NGS

NGS has several advantages over other common technologies, including Sanger sequencing, qPCR, and microarrays. The speed and scalability of NGS generally make it an attractive and feasible alternative. Key benefits of NGS over other technologies are compared next.

NGS vs. Sanger sequencing

Sanger sequencing, also known as capillary electrophoresis sequencing, is a highly accurate (0.001% error rate), fast and cost-effective sequencing method [1]. However, Sanger sequencing does have the limitation of sequencing only one DNA fragment at a time whereas NGS can sequence millions of individual fragments in a single reaction. And, when used with nucleotide “barcodes,” multiple samples can be decoded in a single NGS sequencing run [1]. Combined with its ability to detect a greater variety of nucleotide changes, and a lower limit of detection, NGS has become more accessible and practical for research labs interested in these objectives [1]:

  • Sequencing hundreds to thousands of genes, or genomic regions, simultaneously
  • Analyzing different types of genomic features (such as SNVs, structural variants, gene fusions) in a single sequencing run
  • Detecting novel or rare variants, and resolving mutations

NGS vs. qPCR

qPCR is a commonly used technology for analyzing or detecting genomic variants, as well as for analyzing gene expression and RNA transcripts. Although qPCR is an accurate and reliable approach, there are two major advantages to using NGS instead to identify variants or study RNA transcripts. However, there are key differences that must be considered when deciding between NGS vs qPCR:

  • Since qPCR uses targeted primers and probes, this approach can only identify known sequences or detect expression levels of targeted genes. It cannot be used to identify novel variants or give a big-picture view of transcription in a sample [2]; because NGS requires no previous knowledge of a sequence, it is a more powerful tool for detecting novel or rare variants [1].
  • The NGS approach of RNA-sequencing (RNA-seq) can result in sequences from all the transcripts in a sample, without needing information about target-regions before, meaning this approach can be useful in discovery workflows such as identifying new transcripts, unannotated transcripts isoforms, and transcripts from unknown fusion genes [3].
  • When working with multiple or unknown targets, NGS is better at scaling to a large number of samples compared to qPCR. NGS relies on multiplexing and parallel sequencing, can identify variants across hundreds or thousands of regions, from hundreds of samples simultaneously [1]. More specifically, RNA-seq is capable of sequencing whole transcriptomes. By comparing the transcriptional profiles between healthy vs. disease, or between treatment vs. no treatment groups, RNA-seq can be used to gain insight into the underlying molecular mechanisms. Since only a few transcripts can be analyzed in a single reaction in qPCR, it is a more suitable method for focused studies where there are fewer targets for gene expression analysis (e.g., genes involved in a specific pathway) [3].

NGS vs. microarrays

Microarrays are a powerful tool for gene expression studies but consider NGS for sequencing the transcriptome (RNA-seq), due to these inherent advantages over microarrays:

  • Microarrays require species- or transcript-specific probes to identify gene expression variants; because NGS is not limited by this requirement it can identify novel transcripts, gene fusions, single nucleotide variants, indels (small insertions and deletions), alternatively spliced isoforms, splice sites, and even small and noncoding RNA [3].
  • RNA-seq is both more specific and more accurate than microarrays [4].
  • Since RNA-seq can be done on millions of sequence fragments at once, it can identify rare transcripts, single transcripts per cell, and weakly expressed genes [3].


  1. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol. 2008; 26(10):1135–1145.
  2. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR--a perspective. J Mol Endocrinol. 2005;34(3):597-601.
  3. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009; 10:57-63.
  4. Wang C, Gong B, Bushel PR, et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat Biotechnol. 2014; 32:926–932.

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