Posted by utiiiD 19 hours ago
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.
I would not group AlphaGenome into the pile of failed predictions of other models. AlphaGenome deserves to get evaluated based off its own merits.
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!
Also Mendelian has little to do with promotor sequences or differential transcription in deviations.
As someone who regularly investigates whole genomes I would love to use this as a tool on novel mutations. These folks are the edge cases no one else could figure out that I get a crack at. Beyond the DNA we have the symptoms and lab work and I can usually narrow it down to a handful of guesses, but it sure would be nice to use this to help rank where to invest efforts.
For now i'll treat it as just another fun Google project that might come out of beta one day (or not).
23andMe used a custom Illumina Infinium microarray designed around segments of particular interest.
It's unlikely that they would luck into testing some unknown SNP which turned out to be relevant for disease.
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.
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.
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.
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.
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.
Imagine if the Apple EV had actually happened, you think the EV enthusiasts would roll their eyes like you are?
Very excited to see that happen here.