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Leap in DNA synthesis slashes time to build new genetic sequences

spectrum.ieee.org

11–20 of 38 posts

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#11
post #3

Cool to see this from Brian Hie, who was doing interesting computational bio research at Meta's FAIR before they axed it. Interesting that this is work on the more physical/testing/manufacturing level than the computational, but it seems very useful. It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR…

> Eventually one of these things will surface that will be GPU/transistor type innovations

Why do you think that?

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#13

This is not a practical challenge - I order DNA from Twist at these ‘large’ scales trivially without needing to do oligo hybridization magic. The DNA arrives in a month - but considering how many oligos sidewinder calls for, not clear how they could be faster.

At a basic level, methods of combining oligos to produce long strands have been known for ages. The challenge is to be able to produce them with low enough error, high enough yield, and enough freedom on sequence. Low error improves your yield, reduces the amount of purification and amplification needed, and lets you make longer strands. Sequence constraints can be significant, too, especially around repeats.

If you're talking about Twist's gene fragment product, they advertise that as maxing out at 5 kb. Most, if not essentially all, of that month delivery time is likely the combination, not the oligo pool production. I think the Sidewinder people are actually using Twist pools; they're doing up to 12.5 kb.

By comparison, we recently needed something in the 20 kb range, with a not-so-great sequence, and it was a multi-month process to have a company produce it.

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#14
post #10

> 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. Never mind artificial genomes - let me have a snapshot of my DNA sequenced and re-created from scratch say 20 years later - telomeres and all.

What if the new one doesn't like you?

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#15
post #10

> 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. Never mind artificial genomes - let me have a snapshot of my DNA sequenced and re-created from scratch say 20 years later - telomeres and all.

What if the new one doesn't like you?

Exactly like the old one!

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#16
post #13

This is not a practical challenge - I order DNA from Twist at these ‘large’ scales trivially without needing to do oligo hybridization magic. The DNA arrives in a month - but considering how many oligos sidewinder calls for, not clear how they could be faster.

At a basic level, methods of combining oligos to produce long strands have been known for ages. The challenge is to be able to produce them with low enough error, high enough yield, and enough freedom on sequence. Low error improves your yield, reduces the amount of purification and amplification needed, and lets you make longer strands. Sequence constraints can be significant, too, especially around repeats. If you'…

Yes. Whole genome sequencing has... some limits. CYP2D6 for instance is an important gene address, yet is rather hard to sequence do to its many copies and minor mutations. If you don't use targeted copy callers, it can be hard to correctly sequence in WGS.

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#17
post #13

Earlier quoted context omitted.

At a basic level, methods of combining oligos to produce long strands have been known for ages. The challenge is to be able to produce them with low enough error, high enough yield, and enough freedom on sequence. Low error improves your yield, reduces the amount of purification and amplification needed, and lets you make longer strands. Sequence constraints can be significant, too, especially around repeats. If you'…

Yes. Whole genome sequencing has... some limits. CYP2D6 for instance is an important gene address, yet is rather hard to sequence do to its many copies and minor mutations. If you don't use targeted copy callers, it can be hard to correctly sequence in WGS.

> Yes. Whole genome sequencing has

We're speaking about gene synthesis, not about DNA sequencing

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#18

> that predictive models are now producing faster than anyone can construct them. Erm ... you have A T C G. You can have a gazillion of combinations there. Of course BY DEFAULT it will always be slower than ANY combination you would desire to have - and you most definitely do not need AI slop to have that either. Do we need AI slop for generating any permutation of those 4 letters now? So what is the point of stating…

> > that predictive models are now producing faster than anyone can construct them.

> Erm ... you have A T C G. You can have a gazillion of combinations there.

> Of course BY DEFAULT it will always be slower than ANY combination you would desire to have - and you most definitely do not need AI slop to have that either. Do we need AI slop for generating any permutation of those 4 letters now? So what is the point of stating "can construct".

The bit right before your quote says why:

  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.
Also, predictive models is broader than Transformers, but even then Transformers in the context of DNA is somewhat different from the context of natural (or even programming) languages; and even more than that, given how effective even mediocre early models were for code not useful to dismiss all of it even when it is definitely "slop" in other domains: https://www.nature.com/articles/s41592-024-02523-z

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#19
post #10

> 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. Never mind artificial genomes - let me have a snapshot of my DNA sequenced and re-created from scratch say 20 years later - telomeres and all.

What if the new one doesn't like you?

Then it's back to the drawing board of course.

Re: Leap in DNA synthesis slashes time to build new genetic sequences

#20
post #3

Cool to see this from Brian Hie, who was doing interesting computational bio research at Meta's FAIR before they axed it. Interesting that this is work on the more physical/testing/manufacturing level than the computational, but it seems very useful. It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR…

> Eventually one of these things will surface that will be GPU/transistor type innovations Why do you think that?

I just meant a big innovation that reshapes everything. I should have used 'level' instead of 'type' here.

But there are a lot of analogies to computation in bio as a physical, atomic forces-driven, massively parallel computer, so it's possible there will be something related to electronics and computers that falls out. For example, there's also applications directly related to other fields including DNA storage of data and neuron-based computation.

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