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Comment by Spacecosmonaut | Hacker News Reader

Current evolved Cas9 (CRISPR) variants are highly efficient and relatively unconstrained in terms of their human genome targeting coverage. Smaller nucleases and higher targeting specificity would be useful. But therapeutic use is mostly limited by delivery.

This seems revolve around a known retron-like reverse transcriptase. A sober framing would be something like: Claude identified a previously undescribed genomic arrangement around a known reverse transcriptase. Not all that sexy.

For now, this is mostly a story about how AI can be used to parse existing data to discover new biology (which is fantastic!).

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I've been using Claude Science a lot and it is VERY good at finding patterns in the DNA around my binding sites - quite often it went 'you could put your primer here but that looks like an Alu repeat, so better not, the primer won't be specific' - it seems like the press release is one step above that pattern recognition? I.e., 'there's a recurring motif here that hasn't been described before', which is probably straightforward to pick up when your context window is 1 million tokens, i.e. within the range of entire bacterial genomes...

Do you find it a big improvement over the tools you used previously?

It's honestly harder - you have to do a lot of extra work to ensure your work is traceable. It's super fast in doing things you have no overview of - it makes pretty figures, it runs command line jobs etc. and I'm sure there are mistakes in there. For now I'm pretending Claude Science is an IDE, like Positron/VSCode, and I have to keep enforcing proper git usage etc. so I can reproduce this work

Edit: compared to my tools before, it generally uses the same tools in the same way, just 20x faster than me and I mostly struggle to keep up and verify what it's doing

Sounds exactly like Claude Code (and ilk) tbh. Just a lot of the stakes are lower and a lot of people are more comfortable with it (e.g. there were always people blindly copying and lasting from StackOverflow) I suppose.

>copying and lasting

> have to keep enforcing proper git usage etc. so I can reproduce this work

Depending on what you mean here, it might be worth looking at jj, which works with git repositories. One of the features is that everything gets committed at change time (kind of) which may or may not be helpful to you here.

always add rules for it to never touch git besides git log. you do the commits. make reviews so much easier

Isn't finding this out from an LLM somewhat... complex and non reproducible.

Yep :) it's VERY non-reproducible - but I haven't asked it to look for Alu repeats in the first place, I can then go and reproduce the work it's doing

Everyone who survived 7 rounds of multi model reviews and they still keep finding mediums in their PRs is not in the least surprised. These things are not oracles - they miss stuff all the time even when told to look.

I'm convinced that the LLMs are capable of finding everything in one shot but that's not good for token usage so they only report a few at a time.

you're being downvoted for the paranoid tone i think. but that is correct.

well not token usage, but revenue. their costs for this work would have been astronomical in their own service tier because i bet the context was way larger than anything they even offer.

tweaking context size is the main, or only, "strategy" they have for cost/revenue. and is the reason new trained versions continue to generate hype: you need data in training because you cannot have it in context

I think I was downvoted for not using a /s tag. Im not sure why you're being downvoted.

Exactly like humans.

Curious about this: do you expect that Claude science will also scoop interesting directions looking at anonymous usage, to publish posts like this before the original author (similar to the Navier Stokes debacle)?

There's no evidence that's what happened with navier stokes. By all appearances some employees heard a (somewhat inaccurate) rumor which led them to believe it was solvable and they proceeded to throw utterly absurd amounts of compute at the problem. The ethics of that are still questionable but for an entirely different reason than they were accused of.

I think so, yes; anything we type here (or any other social media) will eventually turn up in the LLMs' memories and become avenues.

This is a good summary of what was going on. I kept reading the paper hoping for a cool wrinkle or function to be revealed, but it's just conserved, highly transcribed array sitting next to reverse transcriptases with a few possible partner genes.

A side note, Matt Durrant has hit on some pretty exciting recombinase activity previously (https://www.nature.com/articles/s41586-024-07552-4). If there's anyone who's well equipped to track down if ART is doing something cool, he's top of the list.

Sorry, but isn't a "conserved, highly transcribed array sitting next to reverse transcriptases" in itself the description of an unknown mechanism? If two parts are combined and conserved and we know what each means but not why they're combined and conserved then it's pretty intriguing, no?

But its just PR so far. They haven't published a refereed science paper, in say Nature or Science. At this stage, its of little value to others until verified.

I guess the Poincare conjecture or the theory of relativity will continue to have "little value" until they get published in a peer reviewed journal

Both of those were published in peer-reviewed journals.

I predict that the importance of refereed science paper, like say Nature or Science, will rapidly decline in many fields. They are a relatively recent phenomenon in the history of science and there's no particular reason for them to continue in their current form.

A better path forward is to shift from static journal articles to open, living Git (or similar revision management tool) repositories. That way everyone can file issues, add comments, submit PRs, etc. Obviously there will be some administrative challenges to block junk submitted by malicious or ignorant users but those problems are solvable.

Exactly the same mechanics was about astra decoding enigma encoded message: it's well-researched subject, with bunch of data and LLM created a breakthrough by identifying previously missed pattern/relation.

> For now, this is mostly a story about how AI can be used to parse existing data to discover new biology (which is fantastic!).

I'd like to expand that: in my view, this is also a story of how agentic AI systems can come up with bioinformatics strategies to discover novel features. One would think such a task would be the ideal domain of the genome language models, which have learned the structure and functional relationships of DNA/RNA sequences. The agents instead relied on classical bioinformatics methods such as HMMs to make their discovery.

Note: I could not find the Supplementary Note 1 that was supposed to describe how exactly agents came to their solution, but I assume it was autonomous.

It’s interesting that models seem, to a distant outsider of biology and drug discovery like me, to be good at coming to new conclusions from existing data. I feel like in coding, it’s the opposite - I have to drag the models kicking and screaming towards anything resembling a novel or nuanced approach to some problems. If anything, this behavior in coding is why we say senior+ engineers will continue to be high value employees, because we can steer the models away from boilerplate and overly generic solutions towards ones that fit aspects of the domain we understand more intrinsically.

This could easily just be how it looks from the outside of biology, but it does seem to produce more novel conclusions in biology than it does in coding and art. Curious if others have counter examples…

And no functional assay!

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