BRIGHT EVIDENCE PACK / Demonstrated
Reading more of a DNA change's consequences
AlphaGenome is a unified DNA-sequence model that takes up to 1 Mb of DNA and predicts thousands of functional genomic tracks, including gene expression, splicing, chromatin features, transcription-factor binding, and contact maps.
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Dates and assessment
- Source published
- 2026-01-28
- Bright published
- 2026-09-07
- Substantive update
- None recorded
- Evidence state
- Demonstrated
- Independent verification
- Not established by this source review
- Last source review
- 2026-09-07
The claim in context
The human problem
Most observed human genetic variation is non-coding, and interpreting its possible biological effects remains difficult.
The prior constraint
Existing sequence-to-function methods traded off input sequence length against prediction resolution and often covered only selected biological modalities.
AI’s actual role
A deep-learning model jointly predicts many molecular genomic measurements and scores the likely effect of a sequence variant across those modalities.
The documented result
In the paper's external variant-effect evaluations, AlphaGenome matched or exceeded the strongest available external models in 25 of 26 evaluations; it also recapitulated mechanisms of clinically relevant variants near the TAL1 oncogene.
Why it may matter
Researchers may be able to generate more integrated hypotheses about how a DNA variant changes gene regulation.
Limitations
- The model predicts molecular effects; it does not diagnose a patient, establish causal disease mechanisms, or prove a treatment works.
- Training and evaluation draw on existing human and mouse experimental datasets.
- The paper describes non-commercial API access, not universal clinical availability.
Original evidence
Attribution
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