A new method for writing DNA promises to unlock the potential of generative AI in biology, giving scientists a fast, affordable, and accurate way to physically build the novel genetic sequences that predictive models are now producing faster than anyone can construct them.
The technique, called Sidewinder, can assemble dozens of genetic sequences simultaneously in a single test tube, producing just one incorrect junction for every 10 million assembly events—a level of precision that far surpasses conventional methods, which misfire roughly once every 10 to 30 joins. Sidewinder also draws on cheap raw materials that have until now been too difficult to use reliably.
“It’s a step change,” says Thomas Gorochowski, a bioengineer at the University of Bristol, in England, who was not involved in the research. “It really opens up the feasibility of synthesizing large genetic systems, maybe even small genomes.” And that, he adds, “is uber-important for all of the AI stuff that’s coming out at the moment around generative genome sequences.”
The advance, presented earlier this month at SynBioBeta 2026 in San Jose, Calif., and detailed in a preprint posted to bioRxiv, addresses one of the more vexing mismatches in modern genomics research. Generative AI tools like Evo 2, trained on the genetic code of millions of organisms, can design new DNA sequences on demand at extraordinary speed. But physically constructing long DNA sequences in a laboratory has remained slow and expensive, especially when building not just one sequence at a time but dozens of different designs simultaneously, as testing AI predictions at scale demands.
In a demonstration of how squarely Sidewinder targets this bottleneck, the team behind the technique, led by Caltech synthetic biologist Kaihang Wang, harnessed the power of Evo 2 to redesign a 12,500-letter DNA sequence of the E. coli genome in silico and then used Sidewinder to build it from scratch—with no errors. Sequences of that length can encode entire biochemical pathways, laying the groundwork for engineered microbes that manufacture drugs, biofuels, or specialty chemicals, and eventually to the assembly of vast DNA constructs approaching complete artificial genomes.
In the past, says Brian Hie, the Stanford computational biologist whose lab developed Evo 2, a project like this would likely take more than a month, based on his team’s experience with conventional commercial methods. “With a technology like this,” he says, “you could probably achieve the same thing in a few days.”
A New Assembly Logic
The new method builds on a DNA synthesis strategy that Wang and his colleagues first outlined at the beginning of the year in Nature, but with substantially greater capacity.
Thanks to a new algorithm that automates the most computationally demanding part of the process and laboratory innovations in how raw ingredients are managed, it is now feasible to synthesize ever larger and more numerous DNA constructs simultaneously. This opens up applications including drug discovery, data storage, and the design of synthetic organisms.
“The pace at which you can start to explore these things just opened up massively,” Gorochowski says.
To understand how Sidewinder works, it helps to understand how DNA is typically made in a laboratory. The process begins with short, chemically manufactured strands called oligonucleotides, or oligos, the molecular alphabet blocks from which longer sequences are assembled.
Ordering oligos individually is reliable but expensive. Scientists discovered years ago that they could slash costs by synthesizing thousands of different oligos together in a single pool. But doing so creates a chaotic soup in which fragments tangle with unintended partners, leading to errors.
Sorting out specific sequences from such a pool has traditionally required elaborate separation steps: physically dividing up the fragments, isolating them in tiny droplets, or fishing them out one by one with laser light. Each approach added cost, time, and specialized equipment.
The Caltech team sidestepped the problem entirely.
Page Numbers for DNA
Sidewinder also starts with oligos, the kind anyone can buy from DNA synthesis vendors such as GenScript or Twist Bioscience, but tags each fragment with a unique molecular barcode. This short identifying sequence ensures that each piece links up only with its intended neighbor in the order that will yield the desired genetic sequence. When two bar-coded fragments meet, they form what chemists call a three-way junction: a fleeting molecular knot that locks the pieces in alignment before being cleanly removed, leaving a seamless strand.
Wang likens these barcodes to page numbers. Whereas conventional assembly is like collating an unnumbered manuscript by matching the last line of one page to the first line of the next—workable for a short document, a recipe for chaos when sequences repeat—Sidewinder’s barcodes guide each fragment to its correct partner regardless of what sequence it carries.
The original Sidewinder protocol required a computationally intensive calculation to design those barcodes, however, and this became impractically slow as the number of fragments grew.
A former Caltech undergraduate student named Jean-Sebastien Paul developed a workaround. While working in Wang’s lab one summer, Paul, who is now pursuing a Ph.D. at Stanford, built a software tool called PyWinder that churns out the barcodes in minutes on a standard laptop, replacing a calculation that had previously been too slow to scale.
Bioengineer Noah Robinson, a postdoc in Wang’s lab who codeveloped the original Sidewinder method, also adapted the approach to work from cheap, mass-produced DNA ingredients, further cutting time and cost.
Wang and Robinson, together with Hie and entrepreneur Adrian Woolfson, cofounded a company called Genyro—to commercialize the technology, hoping to turn a profit through paying pharmaceutical and biotech clients. According to Robinson, however, they intend to make the Sidewinder platform broadly accessible to the academic research community.
“We really want this to be an enabling platform,” says Robinson. “We want people to do cool things with the technology.”
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