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Advancing Regulatory Variant Effect Prediction With Alphagenome.

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Advancing Regulatory Variant Effect Prediction With Alphagenome.

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Advancing regulatory variant effect prediction with AlphaGenome.

Žiga Avsec¹, Natasha Latysheva², Jun Cheng²

¹Google DeepMind, London, UK. avsec@google.com.

|January 28, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

AlphaGenome is a novel deep learning model that predicts thousands of functional genomic tracks from 1 megabase DNA sequences at single-base-pair resolution. This unified approach overcomes limitations of existing methods, enhancing variant effect prediction across diverse genomic modalities.

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Area of Science:

Genomics and Bioinformatics
Computational Biology
Deep Learning Applications

Background:

Predicting functional genomic measurements from DNA sequences is crucial for understanding the genetic regulatory code.
Current deep learning models face limitations due to a trade-off between input sequence length and prediction resolution, restricting their scope and performance.
Diverse functional genomic modalities, including gene expression, chromatin accessibility, and transcription factor binding, require integrated analysis.

Purpose of the Study:

To develop a unified deep learning model, AlphaGenome, capable of predicting numerous functional genomic tracks from long DNA sequences at high resolution.
To overcome the limitations of existing methods by integrating diverse genomic modalities within a single framework.
To provide accurate variant effect predictions across multiple functional genomic layers.

Main Methods:

Developed AlphaGenome, a deep learning model that accepts 1 megabase (Mb) of DNA sequence as input.
Trained AlphaGenome on human and mouse genomes to predict thousands of functional genomic tracks, including gene expression, chromatin accessibility, transcription factor binding, and more, at single-base-pair resolution.
Evaluated AlphaGenome's performance on variant effect prediction against existing state-of-the-art models across 26 different evaluations.

Main Results:

AlphaGenome successfully predicts thousands of functional genomic tracks across diverse modalities with single-base-pair resolution.
The model matches or surpasses the performance of the strongest external models in 25 out of 26 variant effect prediction evaluations.
AlphaGenome accurately recapitulates the mechanisms of clinically relevant variants, demonstrated by its analysis of variants near the TAL1 oncogene.

Conclusions:

AlphaGenome represents a significant advancement in deep learning for genomics, offering a unified approach to predict functional genomic information from long DNA sequences.
The model's high resolution and broad modality scope enable more accurate and comprehensive variant effect predictions.
Tools are provided to facilitate the use of AlphaGenome for genome track and variant effect predictions, promoting wider research applications.