Ultima Genomics claims $100 full genome sequencing after stealth $600M raise – TechCrunch

The appetite for genomic data continues to grow in biotechnology and pharmaceutical research, but cost is still a factor, even sequencing a full genome now costs just $1,000. But with claims of bringing that cost down another order of magnitude to $100, Ultima Genomics can accelerate this economy even further.

Ultima says its sequencing engine and software platform, the UG 100, can perform a full sequencing of a human genome in about 20 hours with a precision comparable to existing options, but at a far lower cost per “gigabase,” meaning per million Base pairs of the analyzed DNA.

The technical advances may not be entirely understandable to people who are not already familiar with how DNA is sequenced, and as I am not an expert myself I will not attempt to provide a full explanation. But it helps to understand that essentially the DNA amplified in a reagent (so basically a lot of the same DNA in a solution) is passed through small channels where fragments bind to specific microscopic mechanisms that prime them, imaged by many to be replaced by basic detectors working in parallel. These sequences are then reassembled into the entire genome by joining their ends together.

Ultima’s claimed benefit is threefold. First, the micromachinery (“a dense array of electrostatic landing pads”) is etched onto a 200mm silicon wafer, rather than allowing the reagent to flow through fluidic channels that must then be flushed in preparation for the next step. This well-known method uses cheap, readily available material and can be mass-produced.

But more importantly, the reagent can be easily deposited in the center of the wafer, which spins to distribute it evenly over its entire surface using centrifugal force. This is efficient, mechanically simple and allows the resulting sequences to be read “while the wafer rotates in a continuous process, analogous to reading a CD”.

Diagram of the UG 100 open water process with an image of the microstructured surface of the wafer.

The second advance is a bit more mysterious and has to do with the process of preparing and reading DNA directly – instead of replacing the bases with more machine-readable ones or relying on tricky particle-level imaging, a clever combination of both is struck. It’s less destructive to the original strands, but also doesn’t require error-prone measurements like single photon counts.

The third advance involves machine learning to speed up the process of converting optical data (the CD-style scan signal) into usable data. A deep convolutional neural network trained on multiple genomes and fragments is tuned based on a sample from the genome to be sequenced, and then sent to work to verify every tiny piece of data and assemble it into the entire genome. This process speeds up work and eliminates errors.

There is significant room for improvement in the process, mainly in the size and density of the wafer and its surface, resulting in improved throughput. This could push the price down, but right now a 90% reduction is more than enough to enter the market with.

Founder and CEO Gilad Almogy (also the first author of many on the paper cited above), said the company is currently working with early access partners to release some early proof-of-concept studies that show the capabilities of the sequencing technique. The first of these, collaborations with the Broad Institute, Whitehead Institute, Baylor College of Medicine, and others, are forthcoming or present available as preprints.

Wider commercial deployment is expected in 2023 (final price is yet to be determined but will likely reflect the advantage this method offers over others). I asked Almogy what he thinks are the areas of the biotechnology and medical industries that will benefit the most from this new capability.

“We believe that genomics will be the first-line diagnostic for all diseases,” he said, noting that it complements many existing techniques and only improves understanding of them.

But the far lower cost could lead to population genomic studies and improve our general understanding of systematic variance in the genome across different groups and over time. “We are already talking to partners who are interested in developing more genomes, but also RNA expression and proteomics at the population scale,” Almogy said. This is also key to epigenetic studies, which look at methylation and other ways our DNA changes as we age.

“Deep oncology,” or the use of genetic profiling to characterize and target cancer, could be one of the earliest clinical applications—and it is Isabel is way ahead of him. The company’s rapid turnaround whole-genome tumor sequencing could be even faster.

Similarly, single-cell sequencing (e.g., of a blood cell or a neuron) could be useful in both clinical and research settings, but “the cost of sequencing also prevents us from routinely using single-cell sequencing for applications such as immune profiling.” said Almogy. Such a significant reduction in cost could change this equation.

With sequencing reduced from a billion-dollar process to one you could do monthly if you wanted to, and with insurance, the biotech industry seems poised on the precipice of another data explosion beyond the scale of our unprecedented are already in the middle. As companies like Ultima multiply data, the next opportunity is likely not in production but in managing and utilizing this newly deepened sea of ​​information.

Ultima Genomics claims $100 full genome sequencing after stealth $600M raise – TechCrunch Source link Ultima Genomics claims $100 full genome sequencing after stealth $600M raise – TechCrunch

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