AlphaGenome
Decoding the Non-coding Genome with AI
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Experience Google DeepMind's breakthrough AI that predicts functional effects across 98% of the human genome. AlphaGenome processes 1Mb DNA sequences to predict gene expression, chromatin states, and variant effects at single-base resolution.
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What is AlphaGenome?
AlphaGenome is Google DeepMind's revolutionary AI model that decodes the "dark matter" of the genome - the 98% of DNA that doesn't encode proteins but controls when, where, and how much genes are expressed.
Unlike traditional approaches that focus only on protein-coding regions, AlphaGenome can predict the functional effects of any DNA sequence, including regulatory elements, enhancers, and non-coding variants that contribute to human disease.
Built on an advanced U-Net architecture combined with CNN and Transformer layers, AlphaGenome processes 1Mb DNA sequences and outputs 5,930 prediction tracks covering gene expression, chromatin accessibility, histone modifications, and 3D genome structure.
Latest Development
Released January 2025
Complementary to AlphaMissense for protein-coding variants. Together, they cover the entire genome.
Key Features
Advanced capabilities for comprehensive genome analysis and variant interpretation
Core Capabilities
1Mb input context for capturing long-range regulatory interactions
5,930 prediction tracks covering diverse cell types and tissues
Single-base resolution for precise variant effect prediction
11 prediction modalities including gene expression and chromatin state
98% genome coverage (non-coding regions that encode 98% of disease variants)
Hi-C contact prediction for 3D genome structure analysis
11 Prediction Modalities
AlphaGenome predicts functional effects across diverse molecular readouts
ATAC-seq
Chromatin accessibility mapping
CAGE
Transcription start site activity
ChIP-seq
Histone modifications and TF binding
DNase-seq
Open chromatin regions
Hi-C
3D chromatin contacts
MPRA
Massively parallel reporter assays
PRO-seq
Nascent transcription
RNA-seq
Gene expression levels
STARR-seq
Enhancer activity
TRIP
Transcription initiation
Conservation
Evolutionary constraint
Innovative Technologies
Hybrid Architecture
U-Net + CNN + Transformer Design
AlphaGenome uses a novel hybrid architecture that combines the strengths of U-Net for multi-scale feature extraction, CNNs for local pattern recognition, and Transformers for capturing long-range dependencies across the 1Mb input window.
U-Net Encoder
Multi-resolution feature extraction for capturing patterns at different genomic scales
Transformer Layers
Self-attention for modeling long-range regulatory interactions up to 1Mb
CNN Decoder
Single-base resolution output for precise predictions across 5,930 tracks
Variant Effect Prediction
Predict the functional impact of any genetic variant:
SNV Analysis
Predict effects of single nucleotide variants on gene expression and regulation
Indel Effects
Assess impact of insertions and deletions on chromatin structure
Regulatory Variants
Identify variants affecting enhancers, promoters, and silencers
Disease Prioritization
Score variants for disease relevance based on functional predictions
Non-coding DNA Analysis
Unlock the 98% of the genome that controls gene expression:
Genome coverage (non-coding regions)
Disease variants in non-coding regions
Context for regulatory element discovery
Use Cases
Cancer Research
Identify non-coding mutations affecting tumor suppressor genes and oncogene regulation. Discover enhancer hijacking and structural variant effects.
Precision Medicine
Prioritize disease-causing variants in patient genomes. Interpret variants of uncertain significance (VUS) in non-coding regions.
Drug Target Discovery
Identify regulatory elements controlling drug target genes. Predict effects of CRISPR edits on gene expression.
How to Use AlphaGenome
Get started with DNA sequence analysis and variant effect prediction
Prepare DNA Sequence
Provide a DNA sequence (up to 1Mb) in FASTA format, or specify genomic coordinates (hg38) for sequence retrieval.
Run Predictions
Select prediction modalities and submit for analysis.
- • Gene expression (CAGE, RNA-seq)
- • Chromatin accessibility (ATAC, DNase)
- • Histone modifications (ChIP-seq)
- • 3D contacts (Hi-C)
Analyze Results
Visualize predictions and interpret variant effects.
- • Track browser visualization
- • Variant effect scores
- • Cell type comparisons
- • Regulatory element annotations
Important Considerations
Research Use Only
AlphaGenome is designed for research purposes. Clinical applications require appropriate validation and regulatory approval.
Experimental Validation
Predictions should be validated experimentally. Use multiple lines of evidence when prioritizing variants for functional studies.
AlphaGenome vs Other Models
Understanding what makes AlphaGenome unique for genomics research
AlphaGenome
DeepMind Genomics AI
- ✓1Mb input context (vs 200kb for Enformer)
- ✓11 prediction modalities for comprehensive analysis
- ✓Single-base resolution across all tracks
- ✓Hi-C contact prediction for 3D genome
- ✓Complementary to AlphaMissense for full genome coverage
- ✓State-of-the-art variant effect prediction
Comprehensive genomics analysis, variant prioritization, regulatory element discovery
Enformer
Previous Generation Model
- ○200kb input context limits long-range analysis
- ○Fewer prediction tracks available
- ○No Hi-C 3D structure predictions
- ○Lower resolution in some tracks
- ○Released 2021, architecture predates recent advances
- ○Limited cell type coverage
Basic gene expression prediction, promoter analysis
Frequently Asked Questions
Common questions about AlphaGenome
What makes AlphaGenome different from Enformer?
AlphaGenome offers 5x longer input context (1Mb vs 200kb), more prediction modalities (11 vs fewer), Hi-C 3D structure predictions, and is designed to complement AlphaMissense for complete genome coverage. The hybrid U-Net + Transformer architecture enables better capture of long-range regulatory interactions.
Can AlphaGenome predict the effects of rare variants?
Yes, AlphaGenome can predict the functional effects of any DNA sequence variant, including rare and novel variants. By comparing predictions for reference and alternate alleles, you can assess variant impact on gene expression, chromatin accessibility, and other molecular phenotypes.
What is the input format for AlphaGenome?
AlphaGenome accepts DNA sequences up to 1Mb in length. You can provide sequences in FASTA format or specify genomic coordinates (hg38) for automatic sequence retrieval. For variant effect prediction, provide both the reference sequence and the variant position.
How does AlphaGenome handle non-coding variants?
AlphaGenome is specifically designed for non-coding DNA analysis. It can identify regulatory variants affecting enhancers, promoters, silencers, and other non-coding elements. This complements AlphaMissense, which focuses on protein-coding variants.
What are the 11 prediction modalities?
AlphaGenome predicts: (1) ATAC-seq for chromatin accessibility, (2) CAGE for TSS activity, (3) ChIP-seq for histone marks, (4) DNase-seq for open chromatin, (5) Hi-C for 3D contacts, (6) MPRA for enhancer activity, (7) PRO-seq for nascent transcription, (8) RNA-seq for expression, (9) STARR-seq for enhancers, (10) TRIP for transcription initiation, and (11) Conservation scores.
Is AlphaGenome suitable for clinical use?
AlphaGenome is currently designed for research purposes. While it can help prioritize variants for clinical review, any clinical applications should involve appropriate validation, expert review, and compliance with relevant regulations.
How does AlphaGenome relate to AlphaMissense?
AlphaGenome and AlphaMissense are complementary models from DeepMind. AlphaMissense predicts the pathogenicity of missense variants in protein-coding regions (2% of genome), while AlphaGenome covers non-coding regions (98% of genome). Together, they provide comprehensive genome-wide variant interpretation.
What cell types and tissues does AlphaGenome cover?
AlphaGenome's 5,930 prediction tracks cover hundreds of cell types and tissues from ENCODE, Roadmap Epigenomics, and other major consortia. This includes primary cells, cell lines, and tissues relevant to major organ systems and disease contexts.