Selecting the Right NGS Method

Over a decade of work went into drafting the human genome: ~92% coverage by 2001 (IHGSC, 2001; Venter et al., 2001) and complete coverage by 2022 (Nurk et al., 2022). Genomic sequencing is much faster now than two decades ago. The current record for obtaining one person’s sequence is 5 hours, 2 minutes (Doxzen, 2022)—which enabled initial diagnosis of a rare genetic disorder 7 hours, 18 minutes after obtaining a blood sample (Gorzynski et al., 2022). The cost is also much less: ca. US$3 billion for the first genome in 2001 (NHGRI, 2022), to possibly US$100 with upcoming technologies (Pennisi, 2022).

Next-generation sequencing (NGS)—also termed massively parallel or high-throughput sequencing—is critical to modern genomic sequencing and analysis. For example, NGS has provided insights into African demographics dating back >200k years (Fan et al., 2023), has helped manage viral outbreaks (Quer et al., 2022), and will be central to treating rare genetic disorders (Vockley, 2023). In fact, the global NGS market was estimated to be US$13 billion in 2022, and is predicted to reach US$27 billion by 2027 (MarketsandMarkets, 2022).

An appropriate analogy for most NGS technologies is copying a book many times, shredding the copies, and then reconstructing the original book by identifying overlapping words (Marx, 2013). When conducting such complex work, it’s critical to choose appropriate tools for the job at hand. Here, we explain factors that you should consider when selecting an NGS technology, and we highlight capabilities of representative vendors.

NGS lingo

A read is the nucleotide base sequence of a copied DNA fragment. Read length is the number of bases in a fragment. Sequence coverage (depth) is the number of unique reads that includes a specific base; essentially, the overlap between different reads. Ultrahigh accuracy (the correctness of a measurement) is essential for reliable sequencing.

NGS typically generates reads of various lengths by amplification, which refers to generating many copies of each fragment for instrumental detection. Coverage corresponds to how the reads fit together into a continuous DNA sequence, enabled by bioinformatics software. A high coverage minimizes errors in genome reconstruction and facilitates identification of genetic variants such as single-nucleotide polymorphisms (SNPs, a predictor of disease susceptibility). Regarding permissible error, the Bermuda Standards for genomic sequencing requires 99.99% accuracy: 1 error per 10,000 bases (HGPIA, 2019). A fundamental distinction between various NGS technologies is whether they’re based on short-read sequencing (SRS) or long-read sequencing (LRS).

SRS entails a maximum read length of a few hundred bases. Historically, SRS has maximized the coverage, maximized the accuracy, and minimized the cost of NGS. A limitation of SRS is that the DNA fragments are typically amplified prior to sequencing. The fact that efficiency or copying errors are exponentially magnified can lead to difficulties in sequencing. Furthermore, it is difficult to use SRS to resolve long repetitive genomic regions. Returning to the book analogy, imagine the difficulty of accurate reassembly if many passages were simply the same word or sentence written over and over (Marx, 2013). SRS is especially useful for applications such as identifying SNPs within otherwise well-conserved sequences.

LRS entails a read length of a few thousand to over a million bases. Complex structural variations such as translocations are easier to identify by LRS compared with SRS. Thus, one might think that LRS would be inherently far superior to SRS. However, compared with SRS, LRS has historically exhibited reduced accuracy per read (due to systematic instrumental errors) and higher-cost instrumentation. However, certain modern platforms report high accuracy of LRS and sufficient portability for use in fieldwork. LRS is especially useful for applications such as haplotype phasing: estimating groups of inherited clusters of SNPs.

Representative tools

Many vendors have NGS expertise and have applied it to versatile instrumentation, software, and assays. For example, Agilent’s SureSelect Cancer Comprehensive Genomic Profiling (CPG) assay focuses on genetic mutations that are common across various solid tumors. Many classes of mutations can be detected—such as SNPs, translocations, and microsatellite instability. Mark Garner, Global Cancer Segment Market Manager at Agilent, says: “The unique molecular identifier barcodes in the Agilent SureSelect Cancer CGP Assay enable maximal recovery of unique reads, increasing the confidence of calling low-frequency variant alleles with reasonable sequencing amounts.”

Complete Genomics has developed DNA nanoball-based sequencing technology (DNBSEQ). This patterned nanoarray features high detection accuracy and efficiency, for whole-genome and whole-exome sequencing. Rade Drmanac, CSO of Complete Genomics, says: “This innovative technology eliminates index hopping and clonal errors during the sequencing step of the workflow, facilitating greater accuracy and flexibility. DNBSEQ sequencing technology has two to three times less indel errors than PCR clusters.”

Illumina’s NextSeq 1000 and 2000 sequencing systems are sufficiently scalable and flexible to support a wide range of applications. Brooke Murphy, Senior Director of Product Marketing at Illumina, says: “The NextSeq 1000/2000 Systems simplify sequencing with integrated, onboard bioinformatics, best-in-class usability with no maintenance wash needed between runs, and broad compatibility with the larger genomics ecosystem.” Murphy also reveals that upcoming “XLEAP–SBS chemistry will unlock the next level of scale, cost efficiencies, and capabilities on NextSeq 1000/2000.”

Arima Genomics’ Hi-C technology gives understanding of the three-dimensional genome, in the context of immunology and other complex biology. Anthony Schmitt, Senior Vice President for Science, says that with knowledge of the DNA sequence and the proximity of DNA segments to one another, Hi-C technology “can identify promoter-enhancer interactions for gene regulation studies, detect structural rearrangements, and scaffold contigs for genome assemblies to define chromosomes de novo.”

NGS is a valuable tool, yet its cost-effectiveness for disease diagnosis can be contentious, at least in certain contexts (Rezapour et al., 2023). Pervez et al. (2022) provides a recent critical evaluation of various vendors’ platforms, but keep in mind that the NGS field is advancing quickly.

References

Doxzen K (2022). Record-breaking rapid DNA sequencing. ASBMB Today, Feb. 27. (last accessed May 4, 2023)

Fan S, et al. (2023). Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation. Cell 186(5):923–939.

Gorzynski JE, et al. (2022). Ultrapure nanopore genome sequencing in a critical care setting. N. Engl. J. Med. 386(7):700–702.

Human Genome Project Information Archive (HGPIA) (2019). Bermuda sequence policies archive. Oak Ridge National Laboratory, May 2, 2019. (last accessed May 11, 2023)

International Human Genome Sequencing Consortium (IHGSC) (2001). Initial sequencing and analysis of the human genome. Nature 409(6822):860–921.

MarketsandMarkets (2023). Next-generation sequencing (NGS) market by product & service (consumables, platforms, services), technology (SBS, Nanopore), application (diagnostic, drug delivery, agriculture), end user (pharma, biotech, academic) - Global forecast to 2027. Report BT 2697, Jan. (last accessed May 4, 2023)

Marx V (2013). The genome jigsaw. Nature Sep. 11. (last accessed Jun. 2, 2023)

National Human Genome Research Institute (NHGRI) (2022). Human Genome Project. Aug. 24. (last accessed May 4, 2023)

Nurk S, et al. (2022). The complete sequence of a human genome. Science 376(6588):44–53.

Pennisi E (2022). A $100 genome? New DNA sequencers could be a ‘game changer’ for biology, medicine. Science Jun. 15. (last accessed May 4, 2023)

Pervez MT, et al. (2022). A comprehensive review of performance of next-generation sequencing platforms. Biomed. Res. Int. 2022: 3457806.

Quer J, et al. (2022). Next-generation sequencing for confronting virus pandemics. Viruses 14(3):600.

Rezapour A, et al. (2023). Economic evaluation of next-generation sequencing techniques in diagnosis of genetic disorders: A systematic review. Clin. Genet. 103(5):513–528.

Venter JC, et al. (2001). The sequence of the human genome. Science 291(5507):1304–1351.

Vockley J, et al. (2023). Whole-genome sequencing holds the key to the success of gene-targeted therapies. Am. J. Med. Genet. 193(1):19–29.