Advancing regulatory variant effect prediction with AlphaGenome

The paper

Avsec et al. (2026). Advancing regulatory variant effect prediction with AlphaGenome. Nature. https://doi.org/10.1038/s41586-025-10014-0

Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance 1–5 . We present AlphaGenome, a unified DNA sequence model, which takes as input 1 Mb of DNA sequence and predicts thousands of functional genomic tracks up to single-base-pair resolution across diverse modalities. The modalities include gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chromatin contact maps, splice site usage and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest available external models in 25 of 26 evaluations of variant effect prediction. The ability of AlphaGenome to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically relevant variants near the TAL1 oncogene 6 . To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.

Contribution

algorithm

Data basis

secondary

Document

full article

Review stage

peer reviewed

Disciplines

genomics, machine learning

Access

open

What it can do

• Explain the methodexplain_method

  Describe how the model predicts regulatory effects, in the paper's own terms.

  genomicsvariant-effect
• Report an evaluationreport_evaluation

  Return what the paper reports for a benchmark, assay or tissue, with the quote.

  benchmarkevaluation

Provenance

Each check stands on its own. There is no single “verified” state: whether an agent faithfully implements its paper is a curation judgement, not something this registry can mechanically prove.

Code: https://github.com/google-deepmind/alphagenome

Machine-readable: paper.jsonld

Agent: org.agenticjournal/paper-alphagenome · v0.1.0