AlphaGenome predicts how DNA variants affect thousands of gene-regulation signals from 1 Mb of sequence
DeepMind's AlphaGenome reads up to 1 million DNA bases and predicts 5,930 human (1,128 mouse) genomic signals, including expression, chromatin accessibility and splicing, at base-pair resolution. It covers the 98% of the genome that does not code for proteins. Published in Nature on 28 Jan 2026.
Key facts
- Input: up to 1 Mb of DNA; outputs 5,930 human tracks
- State of the art on most variant-effect benchmarks at announcement
- Nature paper 28 Jan 2026 (vol 649); API for non-commercial research
Science result
- Field
- biology / regulatory genomics
- Problem
- Predicting the molecular effect of non-coding genetic variants
- Result
- Unified sequence-to-function model predicting thousands of regulatory signals and variant effects.
- AI system
- AlphaGenome
- Human role
- Human-designed model
- Verification
- Peer-reviewed in Nature (2026)
- Status
- confirmed
What happened
DeepMind extended from protein structure to how DNA sequence controls gene activity, releasing a model and API.
Why it matters
Most disease-linked variants are non-coding. AlphaGenome gives researchers a way to predict what they do.
Changelog
- 2026-09-29: created
Related events
- AlphaMissense classifies 89% of all 71 million possible human missense mutations ★★★
- AlphaGenome Atlas predicts the effect of all ~9 billion possible single-letter human DNA variants ★★★
Sources (3)
- officialDeepMind: AlphaGenome — AI for better understanding the genome
- paperNature vol 649 issue 8099 (AlphaGenome paper)
- discussionScience Media Centre: expert reaction to AlphaGenome
id: 2025-06-25-alphagenome · updated 2026-09-29 · open in the interactive timeline