While as part of my routine, I have deep understanding of technologies, emerging market landscape, and our application opportunities with help of DNA Sequencing. To start with, let us discuss briefly on all generation technology landscapes in DNA Sequencing evolved in past two decades –
- Sanger sequencing
- Reversible terminator sequencing
- Pyro Sequencing
- Proton detection sequencing
- Next-generation sequencing , or Second Generation Sequencing (NGS)
- Single-molecule real-time (SMRT) sequencing
- Nanopore sequencing
Sanger sequencing being first generation , its instruments operate using capillary electrophoresis, extremely accurate but expensive. NGS, most commonly used method today is highly parallel, shortening sequencing times at low cost but can only able to read short sequence . NGS techniques resemble running multiple tiny Sanger sequencing reactions in parallel, enabling large quantities of DNA. Whereas of the sequencing technologies discussed above, the third generation nanopore sequencing method is emerging and has capability of unprecedented read lengths ultra-long reads and real-time sequencing, and can remove the need for many of the reagents needed for sequencing today; which help significantly lower the cost of DNA sequencing. Unlike NGS, these third generation technologies don’t break down or amplify the DNA – they directly sequence a single DNA molecule but at very low accuracy.
While issue of lack of accuracy in nanopore sequencers can be mitigated by multiple strategies, like there are research efforts in plasmonic nanopores and hybrid nanopores . Contributing factors like Hybrid nanopores can make use of a wide array of proteins and DNA origami nanostructures, and proteins suitable . Research efforts in hybrid nanopores have also grown rapidly following product releases from Oxford Nanopore Technologies. Growing interest and falling costs of DNA sequencing have lead to a strong growth within market. Oxford Nanopore Technologies already gained some success in penetrating this market due to the ultra long-read capability their platforms, which has lead to use of their platforms in gaining additional insight into cancer – despite having a lower single-read accuracy than Illumina. NGS platforms have dominated the research industry due to two factors: high accuracy, which allows for use in many applications, and relatively low operational costs (as compared to 1st generation Sanger sequencing). 3rd generation platforms are gradually improving accuracy and reducing costs, and the growing interest in these platforms is reflected in the increasing number of related publications. However, this growth is slow, and it may be a few years before they can compete with current NGS platforms in this market.
CAPEX , Cost per base, Throughput , Speed , Portability , Maximum Read Length , Run Time and Accuracy is the key selection criteria for any given business scenario. We need to compare Throughput (Gb/hour) vs instrument cost (US$) among key players. Production-scale sequencing devices have a very high unit cost, but can provide large amounts of throughput and leverage economies of scale to lower the OPEX (i.e. the cost to sequence each bp). By comparing the unit costs of major instrument providers against the throughput of their devices, we can identify the strategies employed by these companies. As the dominant player in the market, Illumina provides a range of instruments encompassing various scales and throughputs, with the aim of providing a device for each use case. In contrast to this, Thermo Fisher and BGI both target non-overlapping segments of the market: BGI provides production-scale, high throughput machines through their subsidiary MGI, while Thermo Fisher caters towards organizations with more limited resources by providing only benchtop devices. Oxford Nanopore Technologies appears to be trying to disrupt the market by providing systems at lower costs than their competitors; notably, Oxford Nanopore is the sole provider of a portable sequencing device (the MinION). Oxford Nanopore Technologies is expected to employ this strategy prioritize expanding their customer base over the short term, until which time they can effectively employ their razor-blade business model to recoup costs.
Lets have a brief look on accuracy and processing speed though it can vary significantly based on the experiment, algorithm settings, training data, and several other factors. Here is a benchmarked programs according to the highest values obtained in an experimental settings. –
|S.No||Year||DNA Sequencing Technology||Speed||Accuracy|
|1||(2020)||Oxford Nanopore Technologies |
Guppy v3.4.4 (2020)
|~1 500 000 bps||90%|
|2||(2018)||Chiron||~2 500 bps||88.8%|
|4||(2017)||Oxford Nanopore Technologies:|
|~120 000 bps||88.2%|
|5||(2020)||Casualcall||~7 000 bps||87.4%|
|6||(2017)||DeepNano||~3 500 bps||78%|
|7||(2018)||WaveNano||~144 230 bps||63.2%|
|8||(2019)||Oxford Nanopore Technologies |
|~1 400 000 bps||98.3%|
|9||(2020)||DeepNano-blitz||~19 500 000 bps||88%|
|10||(2020)||CATCaller||~6 792 678 bps||91.52%|
|11||(2021)||Fast-Bonito (2021)||~7 910 000 bps||98.5%|
All said and done, I can acknowledge that NGS actually lead to a rapid decrease in the costs of sequencing, with this reduction of cost far outpacing Moore’s law. Today, market leader Illumina managed to lower the cost of a human whole genome sequence from an estimated US$1M to US$1k in past 15 years. The throughput and unit costs of an sequencing instrument are important factors in defining its use case: for example, benchtop devices are typically cheap devices that provide a limited throughput, and are suitable for small scale sequencing applications. Below are the key industries driving Sequencing –
A. Drug discovery: The process of drug discovery is time consuming and expensive, with around 90% of therapies failing in development makes drug discovery time consuming and expensive. DNA sequencing can identify specific genes to be targeted, potentially lowering the development times and costs of drug development. Sequencing is also a core part of developing mRNA vaccines.
B. Genetic engineering of crops : Climate change and a growing population is leading to increasing strain on conventional agriculture, leading to greater research efforts into genetic modification of crops to increase yields and tolerance to environmental conditions. These efforts are heavily reliant on sequencing to understand the genetic traits of crops leading to these favorable properties.
C. Disease Outbreak Surveillance : There is a need for a surveillance network infrastructure to track disease outbreaks like in COVID 19 before they become a major problem because Sequencing avoids weaknesses of alternative techniques such as dependence on known primers. Additionally, sequencing can identify variants of a particular pathogen; this would be extremely difficult to do with other methods.
D. Population Genomics : Aim of advancing genomics understanding and evaluating members of a population at higher risk of developing genetic diseases develops further, greater preventive medicine approaches can be adopted to reduce healthcare spending on managing genetic disease.
Here are the key players with their business model –
|DNA Sequencing ( Components)||DNA Sequencing ( Services )|
|Proprietary sequencing platforms|
Illumina, Promega, Singular Genomics, ThermoFisher, BGI, Agilent, Oxford Nanopore, Danaher, QIAGEN, UG, etc
Children Hospital Los Angeles, Cedars Sinai etc
|Consumables and additional Hardware|
Merck, Twist Bioscience, Roche, Takara, GenScript, Novogene, Trilink Biotechnologies, EpigenTek,
|Direct to Customer|
Ancestry.com , GENE GENE, 23 and Me, 23M Fang, MapMyGenome, Kailos, SEQUENCING
|Sequencing as a Service SaaS|
EuroFins, BerryGenomics, Procomcure, MRC PPU, ONE CODEX, CeGaT Genomics, PerkinElmer, e-Zyvec, CGS, Babraham Institute
Today many universities and institutions provide SaaS services . SaaS providers are expected to prioritize throughput, accuracy, and sequencing costs in choosing sequencing platforms. SaaS allows organizations with limited access to sequencing platforms or trained personnel to make use of sequencing information. Human Genome is 3.1 Billion Base Pairs = File size of 3.1 GB , A Bacteria Gnome is 2 Million Base Pairs = File size of 2 MB. In Gnome sequencing we are understanding the order of DNA Genetic Material [ A,G,C,T] in all Human Gnome Base Pairs.
The industry still lacks real competitive players though we see meteoric expansion of the sequencing market, rise in academic publications rising by more than 10-15 times. With several startups and imminent expiry of several key patents of a major player will also allow China-based genomics giant BGI to enter the US sequencing market, further escalating competition. Anyway, lowering costs and improving accuracy of whole-genome sequencing, is leading to a new age of personalized medicine and greatly expand the sequencing market, bringing in the next revolution of DNA sequencing.