AlphaGenome Atlas: a high-resolution map of human DNA

Videos. [2] is for the scientists to start using AlphaGenome Atlas from AntiGravity.

1. https://www.youtube.com/watch?v=U0aToL5C-bQ

2. https://www.youtube.com/watch?v=b2qw3rDNX0Q

In another HN thread about this AlphaGenome Atlas, someone has posted a link to:

https://www.science.org/content/blog-post/mutate-em-all-and-...

which comments the results of this study:

https://www.biorxiv.org/content/10.64898/2026.07.25.740675v1

That study has done in reality what the AlphaGenome Atlas does in fiction, but instead for a human they have done it for one of the simplest viruses.

So they have fuzzed the virus by mutating one by one each position of its DNA.

And various dedicated AI models all made poor predictions of the results of that experiment, which casts doubts about the value of the AlphaGenome predictive map.

A virus is much simpler than a human, but even for that simple virus the effects of most of the mutations could not be predicted. A half of the mutations had harmful effects, and for a half of those it is unknown for now why they were harmful.

For a human the uncertainty about the effects of a mutation will be far greater than for one of the simplest viruses.

Not a word about promoter sequences.

Imagine cellular activity as an industry zone, its not just what you can or can not make, its also 'for what concentrations of chemical species, what transcription rates should be used' so apart from the discrete Mendelian aspects (like what eye color or what have you) there is also a concensus sequence and deviations from consensus. They mention the dataset captures non-coding DNA, which should imply promoter sequences. Will it be possible to query the atlas for joint probabilities of promoter and putative target protein occurence in human genomes?

Personalized medicine could never credibly take off as long as promoter sequences were excised before sequencing!

[delayed]

Can this be used with a 23andMe genome to find pathogenic mutations?

Alpha Fold has continued to impact the field of protein networks, but I do hear that not every one of the deep learning biology models from Google/Deep Mind and others have made equivalent impact or had as lasting relevance in their respective domains..some have performed more poorly than other available models. I'd love to learn more about this, but this has mostly come from little snips of conversations here and there, in person and online, but I haven't seen anything comprehensive in terms of evaluating their impacts overall

This may not be anything new but it makes using several Google/DeepMind resources a lot less painful.

I'm comfortable programming but others who also do mol bio may be less so or may not recognize when Claude is going off the rails.

People are upvoting this because it has the “Alpha______” prefix. Meanwhile, everyone in the field of genomics knows that AlphaGenome provides essentially zero improvements over the previous SOTA, Borzoi…

Is this just Google precomputing Alpha genome values - which were already accessible via API and making them available as another API (presumably more broadly)? Or is there actually new information?

This has Demis written all over it. There is a great video of him with AlphaFold chatting with the team about releasing some results, and he asked something like “what if we just do them all?”

Very excited to see that happen here.

I saw a really interesting talk by Katie Pollard at ISMB this year about the limitations of variant prediction.

The gist was, can existing variation provide enough context to infer impact of variation? The answer seemed to be no.

Kind of like how frontier LLMs need to ingest larger and large amounts of text to advance. We are going to need to leverage comparative data from other species, and likely tremendous amounts of laboratory mutagenesis experiments to actually make headway on variant prediction. Nature, as it stands, just doesn't have enough human variation.

I really love Deep Mind, its genuinely focused on using AI to make the world a better place.

I am freaking out. This is a huge moment.

I don't want to drag the discourse away from this achievement, but I hate how this is announced with blatant corporate advertising (our internal model, here are the benchmarks, gpt astra TM yours now for the low low price of £200pcm). I just didn't think Navier-Stokes falling would be sponsored by McDonald's.

Still. I am crying right now. Navier-Stokes is solved.

So I will know which DNAs to change to become a wolverine! yay!

There are AI labs headed by marketing CEOs that fake metrics, have an utter disregard for humanity and make up "AGI is imminent" propaganda for their IPO, and then there are AI labs headed by actual scientists that do actual science for humanity without constantly trying to put themselves into the spotlight.

Anyone know if you can use an indel VCF file with this?

Cool. But is this actually advanced biology, or just making predictions about biology more accurate? Those aren't the same thing.

I'm convinced AI labs are absolutely hallucinating with their random names. It doesn't even mean anything anymore. Can we go back to numbers?

could this be used with a nebula genomic sequence to find pathogenic sequences?

This Google blog post is a distilled version of a Deep Mind blog post:

https://deepmind.google/blog/alphagenome-atlas-a-predictive-...

They are only announcing a cache. The origin for the cache is not discussed.

In particular, the question of whether to trust the predictions is not addressed. For that, I think the citation is from January:

https://www.nature.com/articles/s41586-025-10014-0

[dead]

hmm gate keeping the database to elitist institutions and private businesses, I'm excited for the future!

All controlled by an adCompany.

That is outright scary. Science is being slurped up here.

unleash gemini 4 stop saving dario

the Google DeepMind PR team can't catch a break, shame it makes no sense to me - if someone's in the field maybe they could explain to the rest of us if this is a big deal, just a PR move, or nah?

not my field so I can't judge how useful it is but assuming this data can be used for drug discovery and their ToS limiting to non-commercial use only. Does DeepMind plan on selling this data to pharmaceutical companies?

Google doing work to uncover the pandoras box of genetics? How long before they shove this under the rug...

An atlas of the human genome from the company whose other atlas still routes me into a lake.