Google DeepMind has released AlphaGenome Atlas, a catalogue of precomputed predictions for the molecular effects of every possible single-nucleotide variant in the human genome. That is roughly 9 billion single-letter changes. The release also introduces the AlphaGenome Variant Impact (AVI) score, a single number that ranks variants by predicted impact, plus per-variant feature attributions and a genome-wide motif collection. The resource ships as a free web portal for academic use, through the AlphaGenome API, and as a skill in Google Antigravity.
Is it deployable? Partially. The Atlas is queryable today for non-commercial research via the portal and API, and commercial access on Google Cloud is listed as “coming soon”. The underlying AlphaGenome model is already available for academic use on GitHub and for commercial use on Model Garden on Google Cloud.
From one model to a genome-wide map
AlphaGenome, released in June 2025, predicts how a DNA variant changes molecular processes such as gene expression and RNA splicing. It has been used widely, but always one variant or one region at a time. The Atlas changes the unit of work. DeepMind team ran AlphaGenome across all 9 billion single-nucleotide variants and stored the outputs, producing a 1-petabyte dataset. This is more than 30 times larger than the AlphaFold Database, which holds over 200 million protein structure predictions.
Testing 9 billion mutations in a lab is not feasible, and running a large model on demand for each candidate variant is slow for genome-scale studies. A lookup table with attached interpretation removes both bottlenecks.
What is inside the Atlas
The Atlas exposes 4 linked resources:
- Molecular effect predictions: thousands of predictions per variant, covering multiple aspects of gene regulation across hundreds of human and mouse cell types and tissues.
- AVI score: a single impact number per variant. It combines AlphaGenome’s regulatory predictions with AlphaMissense, DeepMind’s model for protein-altering variants, so it works in both coding regions (about 2% of the genome) and non-coding regions (the other 98%).
- AVI feature attributions: each score is decomposed into additive contributions from interpretable categories such as chromatin accessibility, splicing, and conservation, so a researcher can see which process a variant is predicted to disrupt.
- DNA sequence motifs: a compendium of over 2,500 recurrent short sequences, with genomic locations, including transcription factor binding sites.
DeepMind team reports that the AVI score delivers best-in-class performance across many variant pathogenicity and rare disease benchmarks. The technical report carries the benchmark details.
The Atlas already holds a precomputed prediction for every one of the 9 billion single-letter changes in the human genome. This demo shows what a single lookup returns.
1. Mutate one base
Click any letter to swap it. Coding bases get an AlphaMissense protein term; non-coding bases rely on AlphaGenome alone.
coding (2%)non-coding (98%)
AlphaGenome Variant Impact (AVI)
0.00
No variant selected.
Illustrative numbers. The bars mimic how the Atlas splits one AVI score into additive feature attributions. Real values come from the Atlas portal, not this widget.
2. How the Atlas is built
9B variants→
AlphaGenome→
thousands of molecular effects→
AVI score→
attributions + 2,500+ motifs
0single-nucleotide variants scored
0dataset size, 30x the AlphaFold Database
0more non-coding associations found in 54,000+ UK Biobank genomes
0recurrent DNA motifs catalogued
Early results from external collaborators
Three research groups used the Atlas before launch, and their results anchor the announcement:
- Rare disease: Working with the GREGoR Consortium, Laura Covill and Anne O’Donnell-Luria at the Broad Institute used the AVI score to reprioritize variants that earlier analyses had overlooked. The score surfaced a variant in DNM1, a gene strongly linked to epileptic encephalopathy. The underlying AlphaGenome predictions showed the mechanism: the variant created an incorrect splice site that abnormally extended the resulting protein. Experimental screens validated the prediction and found nearby variants with similar effects.
- Population genetics: Gareth Hawkes, a Medical Research Council fellow at the University of Exeter, applied the Atlas to whole-genome data from over 54,000 UK Biobank participants. Grouping rare variants by predicted molecular effect uncovered 22% more non-coding associations than would otherwise be detectable, pinpointing regulatory variants that drive circulating levels of proteins such as PLA2G7 and EGLN1. Filtering to the 1% of non-coding variants that the Atlas rates most impactful, Hawkes identified 19 genomic regions associated with body mass index.
- Regulatory grammar: Julia Zeitlinger and Melanie Weilert at the Stowers Institute for Medical Research used the motif resource to separate transcription factors that only change DNA accessibility from those that also switch genes on and off.
Key Takeaways
- AlphaGenome Atlas precomputes molecular effects for all 9 billion human single-nucleotide variants in a 1-PB dataset.
- The AVI score merges AlphaGenome and AlphaMissense into 1 rankable number for coding and non-coding variants.
- Feature attributions and 2,500+ motifs explain why a variant scores high, not just that it does.
- Collaborators found a validated DNM1 splice variant and 22% more non-coding associations in 54,000+ UK Biobank genomes.
- Free portal and API for academic use today; Google Cloud commercial access is coming soon; no clinical approval.
Check out the Paper, DeepMind announcement, the Google Technical Post, and the Atlas Portal. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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