Google DeepMind has released AlphaGenome Atlas：https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/, 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：https://alphagenome.google/atlas for academic use, through the AlphaGenome API：https://github.com/google-deepmind/alphagenome, and as a skill in Google Antigravity：https://antigravity.google/use-cases/science.
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：https://github.com/google-deepmind/alphagenome_research and for commercial use on Model Garden on Google Cloud：https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/open-models/alphagenome.
AlphaGenome：https://deepmind.google/blog/alphagenome-ai-for-better-understanding-the-genome/, 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：https://deepmind.google/blog/alphafold-reveals-the-structure-of-the-protein-universe/, 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.
The Atlas exposes 4 linked resources:
DeepMind team reports that the AVI score delivers best-in-class performance across many variant pathogenicity and rare disease benchmarks. The technical report：https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-change-in-the-human-genome/alphagenome-atlas.pdf 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.
Click any letter to swap it. Coding bases get an AlphaMissense protein term; non-coding bases rely on AlphaGenome alone.
Three research groups used the Atlas before launch, and their results anchor the announcement:
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