When Was Next Generation Sequencing Invented and How It Transformed Genomics

The Question that Sparked a Revolution: When Was Next Generation Sequencing Invented?

Every modern genomics lab relies on next-generation sequencing (NGS). But few stop to ask a simple question: when was NGS actually invented—and why did it change everything?

Before 2005, DNA sequencing was dominated by the Sanger method, developed in the 1970s. It was powerful yet slow—each reaction could read only a few hundred base pairs at a time. Even with automation, sequencing a whole genome demanded years of effort and millions of dollars. The Human Genome Project, completed in 2003, famously cost about $3 billion and took over a decade. These limitations set the stage for a breakthrough.

By the early 2000s, researchers were asking:

• Could sequencing be miniaturised and parallelised to handle thousands of fragments at once?
• Could chemistry and computing merge to read DNA faster and cheaper?

The answer came in 2005, when next-generation sequencing was born—ushering in an era of massively parallel, high-throughput sequencing that transformed biology forever.

NGS was not a single invention but a culmination of advances in microfluidics, imaging, and enzymology. It represented a paradigm shift from analysing one molecule at a time to billions simultaneously. Within just a few years, genome sequencing costs plummeted from millions of dollars to under $1,000 per genome, enabling everything from microbial ecology to population-scale genetics.

In this article, we trace how NGS emerged from decades of incremental progress and why understanding its history still matters for modern genomics research. For readers new to sequencing fundamentals, you may also wish to revisit our overview, DNA Sequencing: Definition, Methods, and Applications, which explains the basic principles behind these technologies.

From Sanger to Second Generation — The Road to High-Throughput DNA Sequencing

To understand when next-generation sequencing truly began, we must see how the field evolved from Sanger's foundational chemistry into scalable, high-throughput systems. This section traces that transition.

The Sanger Era: From Chain Termination to Automation

In 1977, Frederick Sanger and colleagues introduced the dideoxy (chain-termination) sequencing method, a landmark in molecular biology.

That method used modified nucleotides (ddNTPs) to terminate DNA synthesis at specific bases, creating fragments resolvable by electrophoresis.

Over the 1980s and 1990s, Sanger sequencing was refined via fluorescent labelling and capillary electrophoresis, enabling throughput improvements and widespread adoption.

Instruments from Applied Biosystems automated much of this workflow in the late 1980s, reducing manual gel loading and improving reproducibility.

Sanger sequencing became the "gold standard" for accuracy, though its throughput and cost per base remained limiting for large genomes.

The Limits of First Generation Sequencing

• Despite automation, Sanger methods still read one fragment per reaction, yielding read lengths of ~400–1000 base pairs.
• For large genomes, the cost, labor, and time remained prohibitive. For example, sequencing a human genome took years and massive funding.
• As projects expanded in scale (population genetics, microbiomes, cancer research), demand grew for more scalable technologies.

The Turning Point: 454 Pyrosequencing Launches NGS Era (2005)

In 2005, 454 Life Sciences released the first commercial next-generation sequencer based on pyrosequencing in microfabricated picoliter wells.

Its design used emulsion PCR to clonally amplify DNA fragments on beads, which were deposited into wells in a PicoTiterPlate. Each well received sequencing enzymes and reagents.

During each nucleotide flow, pyrophosphate (PPi) released by incorporation was converted to light via enzymatic reactions (ATP sulfurylase + luciferase), allowing real-time detection.

This massively parallel approach enabled millions of DNA fragments to be sequenced simultaneously—shifting from one-at-a-time to many-at-once.

While 454 was later phased out, its architecture laid the foundation for massively parallel sequencing and inspired successor platforms.

Fig 1. The 454 Life Sciences GS FLX System Sequencing Procedure.

The 2005–2010 Explosion

Between 2005 and 2010, NGS went from niche innovation to mainstream genomic tool. This era saw multiple platforms launch, throughput soar, and the cost per base drop precipitously. Below is a closer look at key breakthroughs that shaped the second generation of sequencing.

454 Pyrosequencing & the First Commercial NGS

As mentioned, 454 Life Sciences rolled out its Genome Sequencer FLX in 2005 based on pyrosequencing, enabling massively parallel sequencing using emulsion PCR and light detection.

Though relatively short reads by today's standards (~200–300 bp initially), 454 demonstrated proof of concept: millions of templates could be sequenced in parallel.

Its success encouraged competition and innovation from other companies.

3.2 Rise of Illumina / Solexa: Sequencing by Synthesis

Solexa technology (founded from Cambridge University work by Balasubramanian & Klenerman) first matured in the mid-2000s. The company commercialized a sequencing-by-synthesis (SBS) method that used reversible terminators.

In 2007, Illumina acquired Solexa, folding in its SBS technology and launching the Genome Analyzer platform.

The 2008 Nature paper by Bentley et al. was pivotal — it demonstrated accurate whole-human genome resequencing using Illumina's reversible terminator chemistry.

That demonstration signaled that NGS could compete with Sanger for large genomes.

Other Key Contenders: SOLiD, ABI & Beyond

Around 2006, Applied Biosystems introduced SOLiD (Sequencing by Oligonucleotide Ligation and Detection). This platform employed a two-base encoding ligation chemistry.

SOLiD offered high accuracy but shorter reads, favoring applications like transcriptomics or resequencing rather than de novo assembly.

Meanwhile, various labs and startups experimented with alternate chemistries (ligation, hybridization, etc.), but few matched the throughput or economics of Illumina's growing dominance.

The Human Genome Resequencing Milestones

In November 2008, Illumina announced sequencing of a Yoruban human genome at >30× coverage using the Genome Analyzer. This marked one of the first times a human sample was resequenced using a second-generation platform.

Simultaneously, multiple groups published human genome resequencing using Illumina, validating its feasibility and accuracy.

This evidence helped shift community confidence from Sanger to NGS for large-scale genomic work.

Why This Period Was Transformative

Exponential throughput gains: Platforms moved from megabase/day to gigabase/day scales.

Cost collapse: The cost per base dropped by orders of magnitude, making population genomics and large cohort studies viable.

Standardization of workflows: Library prep, flow cell technology, and data pipelines matured rapidly.

Ecosystem growth: Toolkits for alignment, variant calling, and storage matured in parallel, enabling broader adoption.

For example, software like MAQ and ELAND handled early short-read alignment from Illumina GA data.

Over just 5 years, NGS technologies went from early proofs to the backbone of genomics research.

Entering the Third Generation – Real-Time Sequencing and Nanopore Innovation

The shift to third-generation sequencing marked a qualitative leap: moving from amplification-based, short-read systems to single-molecule, real-time and long-read technologies. This transition addressed key limitations of second-generation NGS—such as assembly gaps, structural variant detection, and phasing—while opening new possibilities in genomics. In this section, we explore how PacBio's SMRT and Oxford Nanopore's nanopore platforms advanced sequencing into a new era.

PacBio SMRT Sequencing: Watching DNA Synthesis in Real Time

Commercial launch: PacBio began shipping its RS instrument around 2011, after beta testing in 2010.

Zero-mode waveguides (ZMWs): Central to SMRT are ZMW nanowells, where a single DNA polymerase at the bottom incorporates fluorescent nucleotides. Signal is recorded in real time as the fluorophore emits light while held in the detection volume.

No amplification step: Because SMRT reads single molecules directly, it avoids biases introduced by PCR amplification (e.g., GC skew) and enables more uniform coverage.

Long reads & increased throughput over time:

• Early read lengths were modest (a few kilobases), but with evolving chemistries and polymerases, read lengths expanded to tens of kilobases.
• Throughput per SMRT Cell also increased (e.g., via more ZMWs per cell) and better basecall accuracy.

Epigenetic detection capability: SMRT sequencing can identify DNA modifications (e.g., methylation) by measuring interpulse durations and kinetics as polymerase incorporates bases.

Application advantage: Because long reads alleviate assembly gaps and structural variant miscalls, SMRT data often stands alone or complements short-read sequences. Koren et al. (2013) showed that single-molecule long reads can finish microbial genomes with greater accuracy than hybrid assemblies.

Fig 2. Improving PacBio RS sequence lengths.

Oxford Nanopore: Pushing Boundaries with Electrical Sensing

Founding & early development: Oxford Nanopore was founded in 2005 to commercialize nanopore-based molecular sensors.

First sequencing demonstration: Around 2011, the first nanopore sequencing experiment combining a mutated pore and enzyme motor was reported.

MinION device launch: In 2014, Oxford Nanopore introduced the MinION— a portable, USB-sized sequencer that streams data in real time.

Core principle: DNA or RNA molecules pass through a nanoscale pore; as each nucleotide traverses, it disrupts the ionic current. These current changes are measured and base-called in real time.

Advantages:

• Real-time sequencing: data output starts as the molecule is read—not after run completion.
• Ultra-long read potential: enables reads exceeding hundreds of kilobases.
• Direct detection of base modifications: methylation and other epigenetic marks can be inferred without separate protocols.

Challenges and noise correction: Nanopore data is inherently noisier, so advanced signal processing, base calling, and error correction (e.g., RawHash algorithm for real-time mapping of raw signals) are critical.

Comparing the Two Paradigms: Strengths & Trade-Offs

Why This Generation Matters for Modern Genomics

• Closing the gap in genome completeness: Third-generation sequencing often resolves previously intractable repetitive regions and structural variants.
• Single-molecule insights: No amplification means less bias; direct readout of native DNA or RNA molecules becomes possible.
• Speed + flexibility: Real-time output supports adaptive sampling (e.g., selective sequencing of regions mid-run) and near-immediate decision making.
• Integration with short-read data: Many modern pipelines combine short-read and long-read data ("hybrid assemblies") for optimal accuracy and coverage.

Why the Invention of NGS Still Matters Today

Although NGS emerged over a decade ago, its impact continues to ripple across modern research. The "invention" of NGS fundamentally shifted what's possible in genomics—and understanding that legacy clarifies how we use sequencing today.

Dramatic Drop in Sequencing Costs

From the early 2000s to 2022, the cost of sequencing a human genome fell by orders of magnitude—a drop so steep it outpaced Moore's Law.

In NHGRI's dataset, per-genome costs dropped from millions of dollars in the Sanger era to a few thousand dollars in the 2010s.

The cost per megabase also declined steeply, enabling projects that were once unaffordable.

This cost collapse transformed sequencing from a niche capability into a routine tool usable in many research settings.

Enabling Large-Scale & High-Resolution Studies

Because NGS enables parallel sequencing of millions to billions of fragments, studies can scale to hundreds or thousands of samples, a requirement for population genetics, epidemiology, and comparative genomics.

Deep sequencing unlocks rare variant detection, allele frequencies, and minor subpopulations—features impossible with low-throughput methods. For example, Morelli et al. used Illumina NGS to detect minor viral variants present at <1% frequency in foot-and-mouth disease virus within host samples.

NGS enables resolution at single-base level (SNPs, indels), structural variation, copy number variation, and even epigenetic modifications in many contexts.

Broad Application Across Biological Domains

NGS is now foundational in transcriptomics (RNA-seq), epigenomics (ChIP-seq, ATAC-seq), metagenomics, and single-cell omics.

It has expanded into non-clinical research fields like microbiome profiling, environmental genomics, agricultural breeding, and evolutionary biology.

By powering multi-omics integration, NGS bridges DNA, RNA, and epigenetic layers, offering richer insight than any single layer alone.

Driving the Development of New Technologies & Methods

The demands of NGS spurred immense advances in bioinformatics, storage, alignment, and data analysis pipelines. Tools like BWA, Bowtie, GATK, etc., were developed to handle high-throughput data. (While not a single citation here, this is well documented in genomics literature.)

The "big data" demands of NGS catalysed cloud and HPC solutions, data compression formats, and compressed reference indexing strategies.

Errors, biases, and limitations revealed by NGS (e.g.,, PCR bias, GC bias, mapping ambiguity) also motivated new innovations such as duplex sequencing, and long-read technologies.

Connecting the Past to Your Projects Today

When you design experiments now—whether whole-genome, exome, RNA-seq, or more—you're working within ecosystems built on NGS foundations.

Awareness of NGS history helps you interpret technology trade-offs: read length vs accuracy, depth vs cost, variant calling limitations vs bias sources.

It also guides future decisions: when to adopt long reads, hybrid methods, or newer platforms.

Timeline Snapshot: Key Milestones in NGS Development

Below is a concise timeline highlighting landmark events, innovations, and paradigm shifts in sequencing history. It complements narrative sections while giving readers a clear reference.

Looking Ahead — The Future Built on NGS

As sequencing continues evolving, its foundations rest firmly on NGS innovations. Emerging technologies, trends, and applications are shaping where genomics goes next. This section highlights key frontiers and what they mean for your sequencing strategy.

Higher Accuracy & Lower Error Rates (Q30 → Q40 and Beyond)

Long-read platforms (PacBio, Oxford Nanopore) are pushing error rates closer to short-read standards. Recent reports note Q30 (1 error per 1,000 bases) achievable in certain long-read modes. (Frontline Genomics, "The Latest Developments in Sequencing Technologies")

Some short-read instruments are exploring even higher fidelity (Q40 or more), representing one error per 10,000 bases.

As accuracy improves, applications like rare variant detection, single-cell sequencing, and structural variant calling will benefit directly.

Automation, Miniaturization & Sample Prep Innovation

The next leap involves reducing human labor and error through automation in library prep, microfluidics, and robotic systems. (Roots Analysis, "Future Trends in NGS Technologies")

Miniaturization lowers reagent use and cost per sample, making sequencing accessible to smaller labs and field settings. (Roots Analysis)

Integrated "sample-to-sequence" devices (with library prep onboard) are likely to emerge, reducing hands-on complexity.

Hybrid & Multi-Omics Sequencing

Sequencing used to be DNA or RNA only. Now, integrated multi-omics (epigenomics, transcriptome, chromatin accessibility) are converging. (Roots Analysis)

Spatial sequencing is rising — sequencing in situ within tissues gives both spatial context and molecular detail.

Multiomic experiments (e.g.,, combining transcriptome + methylome + chromatin with sequencing) will be more routine.

Novel Sequencing Modalities & Emerging Paradigms

Solid-state nanopores (non-biological pores, e.g., graphene) are under development, promising durability and speed.

Linked-read technologies (barcoding long molecules but sequencing via short reads) remain relevant for structural insight without full long-read cost.

3D genome sequencing methods (Hi-C, spatial chromosome conformation capture) are evolving to finer resolution.

Also, algorithm-architecture co-design is being explored to accelerate genomic pipelines, optimize memory usage, and reduce data movement (Mutlu & Firtina, 2023)

Applications That Will Drive Future Demand

Population Pangenomes & Reference Graphs

Projects like the Human Pangenome Reference revise the paradigm from one linear reference genome to graph-based references. (Wikipedia)

Environmental / Metagenomics in the Field

Portable sequencers combined with automation and cheaper reagents will enable large-scale ecological, soil, and water sequencing in remote settings.

Precision in Single Cells, Spatial Sequencing & Epigenomics

Single-cell multiomic sequencing in situ will bridge molecular profiling with spatial biology, enabling mapping of cell states in tissue.

AI & Machine Learning–Driven Sequencing Interpretation

As datasets expand, AI / deep learning tools will be central in basecalling, variant calling, signal deconvolution, epigenetic inference, and aligning multiomic data. (Tarozzi et al., 2024)

What This Means for Your Sequencing Strategy Today

• Choose platforms with upgrade paths: consider accuracy, read length, and throughput tradeoffs.
• Stay alert to sample prep automation — it will reduce cost and hands-on labor.
• Explore hybrid or multiomic designs now; build infrastructure for integrative datasets.
• Develop or partner for advanced bioinformatics capacity: improved algorithms, AI models, cloud/storage solutions.
• Plan for evolving reference frameworks (e.g., pangenomes) rather than relying solely on old linear references.

Conclusion

From the moment Sanger's chain-termination chemistry first decoded DNA, the journey to next-generation sequencing (NGS) has redefined what's possible in genomics. By roughly 2005, innovations in microfluidics, parallelisation, and enzymatic chemistry converged to launch the NGS era. Since then, sequencing costs have plummeted, throughput has soared, and biology has transformed with data. Today's long-read and real-time platforms build directly on that legacy.

For your laboratory or research team, here are ways to put that history into practice:

• Choose sequencing platforms that align with your experimental goals—whether short reads for depth or long reads for structure.
• Look for systems with upgrade paths, especially as accuracy improves in third- and fourth-generation technologies.
• Invest in sample prep automation and data infrastructure now—it's no longer just nice to have; it's essential.
• Bring in computational support early. As sequencing scales, your insights will depend more on analysis than raw output.
• Consider hybrid sequencing strategies that combine short- and long-read data for maximum coverage and accuracy.

If you're planning a sequencing project or considering upgrading your platform, we can help. We offer:

• Consultation on platform selection and experimental design
• Turnkey library prep, sequencing, and data analysis pipelines
• Custom bioinformatics tools tuned to your research goals
• Visualization and reporting support for publication or internal review

Let's turn sequencing history into your next discovery. Contact us today for a consultation or project quote, so we can move from "when was NGS invented" to "when will your research unlock new insights."

FAQs

When was next generation sequencing invented?

Next generation sequencing (NGS) was first commercialized in 2005, when 454 Life Sciences launched a massively parallel pyrosequencing platform, shifting DNA sequencing from one molecule at a time to millions in parallel (a hallmark termed "massive parallel sequencing")

What technology came before NGS?

Before NGS, sequencing was dominated by Sanger (chain-termination) methods, refined into automated capillary sequencing in the 1980s and 1990s. These "first-generation" systems delivered high accuracy but very limited throughput and high cost per base.

Why is the invention of NGS important?

The invention of NGS enabled dramatic reductions in sequencing cost, massive throughput expansion, and opened doors for large-scale genomics (population studies, metagenomics, transcriptomics). It transformed sequencing from a niche capability to a ubiquitous research tool

When is next generation sequencing used?

NGS is now used in nearly every non-clinical research application: whole genome sequencing, exome sequencing, RNA-seq, epigenomics (ChIP-seq, ATAC-seq), microbiome/metagenomic profiling, single-cell sequencing, and multiomic integration (combining DNA, RNA, etc.)

How has NGS evolved after 2010?

Since 2010, sequencing has moved into third-generation technologies like PacBio SMRT and Oxford Nanopore, enabling long reads, real-time detection, base modification detection, and hybrid assemblies that overcome short-read limitations

Is NGS the same as massively parallel sequencing?

Yes, "massively parallel sequencing" is often used synonymously with NGS or second-generation methods. It refers to sequencing many DNA fragments simultaneously, enabling high throughput per run

References:

1. Valencia, C.A., Pervaiz, M.A., Husami, A., Qian, Y., Zhang, K. (2013). Sanger Sequencing Principles, History, and Landmarks. In: Next Generation Sequencing Technologies in Medical Genetics. Springer Briefs in Genetics. Springer, New York, NY.
2. Trachtenberg EA, Holcomb CL. Next-generation HLA sequencing using the 454 GS FLX system. Methods Mol Biol. 2013;1034:197-219. History of sequencing by synthesis
3. Bentley, D., Balasubramanian, S., Swerdlow, H. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).
4. Firtina C, Mansouri Ghiasi N, Lindegger J, Singh G, Cavlak MB, Mao H, Mutlu O. RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes. Bioinformatics. 2023 Jun 30;39(39 Suppl 1):i297-i307.
5. Koren S, Harhay GP, Smith TP, Bono JL, Harhay DM, Mcvey SD, Radune D, Bergman NH, Phillippy AM. Reducing assembly complexity of microbial genomes with single-molecule sequencing. Genome Biol. 2013;14(9):R101. doi: 10.1186/gb-2013-14-9-r101. PMID: 24034426; PMCID: PMC4053942.
6. Shendure, J., Balasubramanian, S., Church, G. et al. DNA sequencing at 40: past, present and future. Nature 550, 345–353 (2017).