Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- 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.
by bonsai_spool - 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.by Stevvo
- Can this be used with a 23andMe genome to find pathogenic mutations?by RobotToaster
- 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!
by DoctorOetker - 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.
by adrian_b - Videos. [2] is for the scientists to start using AlphaGenome Atlas from AntiGravity.by leopoldj
- 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.
by searine