Picture a single strand of DNA curling up into a tight little ball, thousands of identical copies of itself packed into a space smaller than a red blood cell. Now imagine spreading billions of these balls across a chip, each one sitting in its own private parking spot, and reading every single one of them at once. That, in essence, is what MGI's DNBSEQ platform does every time it runs. No PCR thermal cycling. No bridge amplification. Just a ball of DNA and a very patient camera.
If you have been following this series, you already know the cast of characters. We started with Sanger sequencing, the original workhorse that gave us the first human genome, one careful read at a time. Then came the next-generation revolution: Illumina's bridge amplification and reversible terminator chemistry, Oxford Nanopore's electrical current reading of DNA threading through a protein pore, PacBio's real-time single-molecule imaging, and Ion Torrent's clever trick of measuring pH instead of light. Today we add a sixth platform to the lineup, one that has quietly become one of the most widely used sequencing technologies outside the United States: MGI's DNBSEQ.
Where did this technology come from?
DNBSEQ traces its roots back to Complete Genomics, a California company that published a landmark paper in Sciencein 2010 describing a method for sequencing a human genome using what they called self-assembling DNA nanoarrays (Drmanac et al., 2010). The idea was radical for its time: instead of amplifying DNA fragments with PCR, which introduces its own errors and biases, why not let the DNA circularize and replicate itself using a gentler, more faithful process? Complete Genomics was later acquired by the Chinese genomics company BGI, and its sequencing chemistry evolved into what we now call DNBSEQ, developed and commercialized by MGI Tech, BGI's technology arm.
Why does it matter that this technology skips PCR? Anyone who has tried to amplify an AT-rich genome like Plasmodium falciparum, sitting at roughly 80 percent AT content, knows the frustration of GC bias, dropout regions, and uneven coverage that standard PCR-based library prep can introduce. A method that avoids repeated PCR cycling has an obvious appeal for exactly this kind of genome.
So how does a DNA nanoball actually form?
The process starts in a familiar place. Double-stranded DNA fragments, each carrying adapter sequences at their ends, are heated until they denature into single strands. From here, DNBSEQ takes a different road than Illumina.
A splint oligonucleotide, a short synthetic sequence complementary to both ends of the same single strand, hybridizes to those ends simultaneously, pulling them together into a nicked circle. DNA ligase then seals the nick, producing a stable, closed single-stranded circle. Think of it like taking a piece of string and tying its two ends together to make a loop.
Once that circle exists, rolling circle amplification takes over. A single high-fidelity polymerase travels around and around the circular template, producing a long concatemer, essentially the same sequence repeated over and over, roughly 100 to 1000 times. This concatemer naturally coils up into a compact, tangled structure: the DNA nanoball.
Why is this gentler than PCR? In PCR, each new copy is made from the previous copy, so any error introduced early in the process gets carried forward and amplified exponentially with every cycle. Rolling circle amplification instead copies the same original circular template again and again. An error introduced during one pass around the circle does not get compounded, because the next pass starts fresh from the same original template. MGI's own technical materials describe this as producing far less accumulated amplification error than PCR-based approaches, and one convenient side effect is that DNB concentration can be measured with a simple Qubit fluorometer, no expensive quantification instrument required.
Getting the nanoballs onto the chip
Once formed, DNBs are loaded onto a patterned array flow cell. Rather than letting DNA molecules land wherever they please on a randomly coated surface, these chips are etched with an orderly grid of binding sites spaced at submicron distances, each designed to hold exactly one DNB. It is a bit like an egg carton for DNA: every nanoball gets its own compartment, evenly spaced from its neighbors. This deliberate spacing helps prevent signal crosstalk between neighboring clusters, which is one of the failure modes that can plague random-cluster sequencing arrays as they get more crowded.
Reading the sequence: cPAS
The actual base calling happens through what MGI calls combinatorial probe-anchor synthesis, or cPAS. Four fluorescently labeled probes, one for each base, bind to the growing strand in a cycle. Lasers excite the fluorophores, a camera captures the resulting flash of color at every single DNB position on the chip, and proprietary software translates millions of these tiny light signals into a base call. The cycle repeats, one base at a time, across the whole flow cell simultaneously.
Where DNBSEQ sits in the platform landscape
MGI now offers a range of instruments spanning very different throughput needs, from compact benchtop systems like the E25 and G99 up through high-throughput workhorses like the G400, T7, and T20×2, the last of which is designed for genome centers running dozens of samples a day.
How does it actually stack up against Illumina, the platform most labs already know? A benchmarking study using the Korean Reference Genome compared DNBSEQ-T7 against six Illumina platforms, including the NovaSeq 6000, and found the two technologies to be broadly comparable for whole-genome sequencing accuracy (Kim et al., 2021). A separate study looking at DNA metabarcoding, sequencing COI gene amplicons from soil samples, found highly correlated results between MGI's DNBSEQ-G400RS and Illumina's NovaSeq 6000, though the MGI platform recovered a somewhat higher number of distinct taxonomic units in that particular comparison (Anslan et al., 2021).
Why this matters for malaria genomics
For a lab working on P. falciparum genomics, sequencing choices are never purely academic. Library prep cost, turnaround time, and how a platform handles an extremely AT-rich genome all factor into which sequencer ends up processing your field isolates. DNBSEQ's PCR-free amplification step is worth understanding even if your lab primarily runs Illumina, because it represents a genuinely different philosophy for how to get from a DNA sample to a finished sequence, and it is increasingly likely to show up in a collaborator's data or a published dataset you are trying to compare against your own.
Related Content
Sanger Sequencing: The Original Read of Life: https://adwoabiotech.blogspot.com/2026/06/sanger-sequencing-explained-how-one.html
Illumina Sequencing: Bridges, Clusters, and Light: https://adwoabiotech.blogspot.com/2026/06/what-is-next-generation-sequencing-ngs.html
Oxford Nanopore: Reading DNA Through a Pore: https://adwoabiotech.blogspot.com/2026/06/what-is-oxford-nanopore-sequencing-how.html
PacBio vs. Nanopore: Two Roads to Long Reads: https://adwoabiotech.blogspot.com/2026/06/pacbio-smrt-sequencing-vs-oxford.html
Ion Torrent: When DNA Sequencing Meets pH: https://adwoabiotech.blogspot.com/2026/07/ion-torrent-sequencing-dna-reader-that.html
References
Anslan, S., Mikryukov, V., Armolaitis, K., Ankuda, J., Lazdina, D., Makovskis, K., Vesterdal, L., Schmidt, I. K., & Tedersoo, L. (2021). Highly comparable metabarcoding results from MGI-Tech and Illumina sequencing platforms. PeerJ, 9, e12254. https://doi.org/10.7717/peerj.12254
Drmanac, R., Sparks, A. B., Callow, M. J., Halpern, A. L., Burns, N. L., Kermani, B. G., Carnevali, P., Nazarenko, I., Nilsen, G. B., Yeung, G., Dahl, F., Fernandez, A., Staker, B., Pant, K. P., Baccash, J., Borcherding, A. P., Brownley, A., Cedeno, R., Chen, L., ... Reid, C. A. (2010). Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science, 327(5961), 78–81. https://doi.org/10.1126/science.1181498
Kim, H.-M., Jeon, S., Chung, O., Jun, J. H., Kim, H.-S., Blazyte, A., Lee, H.-Y., Yu, Y., Cho, Y. S., Bolser, D. M., & Bhak, J. (2021). Comparative analysis of 7 short-read sequencing platforms using the Korean Reference Genome: MGI and Illumina sequencing benchmark for whole-genome sequencing. GigaScience, 10(3), giab014. https://doi.org/10.1093/gigascience/giab014
