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!
Don't be put off by the box asking for your "affiliation". I wrote "None", clicked submit and it took me straight to the Atlas.
Videos. [2] is for the scientists to start using AlphaGenome Atlas from AntiGravity.
Can this be used with a 23andMe genome to find pathogenic mutations?
23andMe and similar companies don't transcribe your entire genome because that would cost way more than they charge you. They just sample a few tiny sections of it.
fwiw sequencing your entire genome only costs $400 or so from providers like sequencing.com
A few as in tens of thousands.
23andMe used a custom Illumina Infinium microarray designed around segments of particular interest.
Probably not any 23andMe haven't already told you about. They test a limited set of SNPs, balancing between ones thought useful for genealogy, ones useful for ethnicity estimates and ones thought useful for health-related things (the latter they would like to make their main selling point, the two former are really all commercial DNA services' bread and butter).
It's unlikely that they would luck into testing some unknown SNP which turned out to be relevant for disease.
23andMe tests SNP's (single nucleotides) that are inferred to be significant in protein function/epigenitics.
Those SNP's i believe are testd from primers
so what 23andMe does is specifically on the back of previous research and afaik their data isnt technically clinically significant as most findings need confirmation or more tests.
Not really, no.
Why not? There is no logical reason as to why this would not work, IF it works in the first place, which I don't know. In theory the problem space here is finite, so there is of course a way to predict everything. Whether this is the case right now - who knows; I probably don't think it is currently ready. But eventually it will be. And it should not be in the hands of private companies.
So it sounds like you're coming to this with very little knowledge about biology. I encourage reading up on modern challenges in pathogenic prediction, especially with regards to SNPs: on their own, with the exception of a few diseases, individual SNP predictions are meaningless in terms of actual pathogenicity.
Because 23andme does not sequence your genome, only substring matches linked to specific gene variants.
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
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?
That’s my reading. (That this is a cached database of Alpha values)
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.
Yep. Sequence-to-function models are still very limited. AlphaGenome Atlas, despite the flashy branding, is unlikely to provide significant benefit to researchers.
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…
> a database that predicts the effects of every possible single nucleotide variant in the human genome. We used the AlphaGenome AI model to pre-calculate the regulatory impact of all 9 billion single-letter genetic changes, resulting in a massive, 1-petabyte dataset.
This is for a database, no? While Borzoi is a model?
> Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence.
Any model can be expressed as a database.
Kolmogorov looks at this with a ‘duh’ face
> Meanwhile, everyone in the field of genomics knows that AlphaGenome provides essentially zero improvements over the previous SOTA, Borzoi…
Can you elaborate on this? I'm confused why Google would build something that provides zero improvements over SOTA, Borzoi... as you mention. I'm not familiar with this field, just curious.
Speaking as somebody who has worked within Google Research before: the researchers are under tremendous pressure to publish SOTA and sometimes they juice their results a bit to look competitive when they can't match. This is not uncommon in the field- it's remarkably easy to edit a paper to make yourself look good by omitting information.
One of the most egregious cases of this, in my opinion, is only publishing metrics that cover part of the confusion matrix. “The false-negative rate? That could not possibly matter for a variant effect prediction model; why would we include that in the paper?” Example: AlphaMissense.
The use of an exact quote in an ungrammatical fashion is a bit of a language model smell. I can’t help but be reminded of the purely nonsensical AI interview answers. “It’s a pleasure to meet you, Chick Bongo”
Creating an account 12 minutes ago (from the time of this posting) to comment on how another comment seems to you like a "language model smell" is itself, a language model smell, or a scammer.
Please stop accusing or hinting at others being a language model or bot. Not only is it a dumb waste of time, it's wrong in this instance and you are not only going to continue to be wrong but you have no way to prove or demonstrate that any single post comes from a bot nor the ability to do anything about it if you did in fact believe some comment to be attributed to a bot.
They did benchmark the model and beat the SOTA on every metric though
Are you really taking a holier than thou approach on a google article?
Is it so bad to have another entrant, especially with the resources Google could bring to bear?
Imagine if the Apple EV had actually happened, you think the EV enthusiasts would roll their eyes like you are?
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.
Interestingly the labs led by marketing CEOs have much better models (astra is A LOT better than gemini).
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 don’t think Demis played a big role in this. It was mainly Ziga Avsec who developed Enformer (the first actually decent sequence-to-function model), and then AlphaGenome.
This has nothing to do with AlphaFold at all- not sure if you were implying that. (the scientific contriution is welcome, but it's not particularly significant)
Why are you excited about this?
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.
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.
This post is not about Navier-Stokes. That is in a different thread. Glad you're cry-happy though.
Wait, is NS solved? I thought I was just a singularity thing?
There is an announcement by OpenAI https://news.ycombinator.com/item?id=49613262
I really love Deep Mind, its genuinely focused on using AI to make the world a better place.
Be careful when loving shareholder-driven endeavors; they're a single executive decision away from breaking your heart.
It's "shareholder-linked" (probably not "driven"), but yes, upvoted. Nonetheless, even when with grave faults in the path, Google has managed to give us marvellous, paramount free resources (Google Street, Google Museum...), and would have given more (Google Books - if the negotiation had succeeded). I am grateful.
Google Streetview/maps isn't even the best mapping/'streetview' tool at this point. Filled with ads and bugs, some major bugs have been there for 10+ years.
For maps there's quite a few, just anything that doesn't have the amount of ads beats it. For Streetview apple lookaround is a better experience if it's mapped well in your area.
I believe Google has been trying to improve their Streetview in coverage as apple has become a concurrent, but it still has the same bugs people have been complaining about for years (like going backward when pressing forward type of bad). If I remember correctly the bugs have been 'closed' or something before just to include it in the same slightly different way.
Organic Maps for maps
Why would you be grateful? I don't think any of that should be under private control to begin with.
They said should be under public control, not is under public control.
Be careful when loving government/philanthropy/ngo-driven endeavors; they're a single executive decision away from breaking your heart.
Marriages too
Hopefully you have a bit more impact on the decision making of your marriage shareholders than on public company executives.
Statistically you are very correct.
No AI company is doing that. There is some benefits but that’s not what any AI for profit will ever focus upon
DeepMind objectively is doing that. The world is not so black and white as "evil profit-hungry corporations will never do anything good".
I preemptively acknowledged that in my comment…
If Google does anything good for the world it is entirely accidental, and I am certain they will correct this mistake later.
Mob rule, aka Democracy.
> Market reward is the best proxy for good we have.
Only if the goal is profit. All else be damned.
> What's your alternative? Executive fiat? Mob rule?
It is always bewildering to me that people see the world being raped by corporations and their negative externality, and they think that this is the pinnacle that humanity can achieve.
Nothing else can be better, if all your options are "mob rule" or "executive fiat".
I fancy myself a misanthrope, but damn, next to people in this forum I am probably the last humanist walking through the ruins of civilization.
> Deepmind is objectively good.
Google is objectively bad, and the world would be a much nicer place if they ceased to exist.
Executive fiat, given the right executive body, and undergirded by healthy autarkies or other cell-type self-direction, worked well in most locales for most of the years humans have been humans. Certain flashy civilizations, Sumer, Greece, have tended to draw the spotlight incommensurately with their population sizes. But even these functioned much more like what I describe above than anything modern.
I think they have done some quantifiable good.
Alphafold or Earthquake alerts for example spring to mind.
You could argue about comprehensive "free" services (search, Gmail, YouTube, docs, etc) and "free" browsers and "free" operating systems (yeah plural) being net positives in the world but I know that many people view these as negatives as they (rightly or wrongly) view them as vehicles to sell ads or collect data, but I bet you 100 bucks the typical person on the street using their android phone to search Google, watch YouTube, check their Gmail, write a doc, view photos etc doesn't give a shit about that and values these things - google has ended up as a monopoly as these things are actually pretty useful to people and thus valued, even if we in the tech world disagree with the adtech and data aspects. This monopoly was not a fait acompli or pure market abuse - Nokia, MS, flikr, Hotmail, yahoo etc were all incumbent and market dominant but are now irrelevant because the Google products were viewed as better by end users.
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.
That's an interesting statement: "The gist was, can existing variation provide enough context to infer impact of variation? The answer seemed to be no."
Is this saying that if we were to sequence every human being on the planet, we'd still be unable to explain some phenotype differences caused by variation simply becase there aren't enough humans/enough variation? Interesting, as that's the first time I've heard that claim, and it would suggest that we spend our time working on mechanistic models of variant to phenotype.
Yes, and genetic variant generally do not act in isolation. We currently focus on the small additive effects of variants because we can with small sample sizes—and 1 million humans is marginal using a GWAS cohort to dive into epistasis. But these interaction effects among variants are critical. Now almost completely deprecated.
So I will know which DNAs to change to become a wolverine! yay!
Anyone know if you can use an indel VCF file with this?
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:
Appreciate the load-bearing info.
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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
Cool. But is this actually advanced biology, or just making predictions about biology more accurate? Those aren't the same thing.
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?
Individual SNP predictions are not useful for drug discovery. Pharma might license this, but mainly out of fear of missing out.
Probably. I know Google has partnerships with Pharma: https://www.merck.com/news/merck-and-google-cloud-partner-to...
My understanding is that they already are, via Isomorphic Labs
It's a bigger deal for diagnostics than for drug development.
Hopefully. Tired, as a shareholder, to see the giveaways that deepmind is doing.
Yeah, screw enabling life changing medical research, won't someone please think of the investors' financial returns??
You can do both. Release it, but ask for money.
Astroturfing, eh? This is verbatim the same comment as another user in this thread. Strange...
PunchTornado was first, fr2029 was second
so you just happen to be posting in a thread where some random user said you made a comment but you made no comments whatsoever
You deleted the verbatim comment after it was pointed out. One was in thread and one was a stand alone comment. I was curious which was the throwaway so clicked into both profiles
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?
Just wait a few months, if it is a big deal that will likely be obvious by then
i like this way of thinking ;)
Does lots of good to my anxiety level :)
What are you even talking about? What do you mean catch a break? They’ve made massive contributions over the last decade.
I think orliesaurus meant the PR team is unable to rest because the rest of Deepmind is so prolific and keep coming out with new things for them to announce
This is exactly what I meant!
I don’t think so. The tone of the comment seems derogatory.
No, it's exactly what @dronebetter said
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.
Eye color is not discrete Mendelian. That's only correct to the first order.
Also Mendelian has little to do with promotor sequences or differential transcription in deviations.