AlphaGenome vs Enformer
Choosing the Right DNA Prediction Model
A comprehensive comparison of Google DeepMind's AlphaGenome and Enformer to help you choose the best model for your genomics research.
Quick Comparison
| Feature | 🧬AlphaGenome | 🔬Enformer |
|---|---|---|
| Input Context Length | 1 Mb | 200 kb |
| Prediction Tracks | 5,930 | 5,313 |
| Output Resolution | 1 bp | 128 bp |
| Prediction Modalities | 11 | 6 |
| Hi-C 3D Contacts | Yes | No |
| Release Year | 2025 | 2021 |
| Architecture | U-Net + Transformer | Transformer only |
| Genome Coverage | 98% (non-coding) | Promoter-focused |
Architecture Comparison
Understanding the technical differences between the two models
AlphaGenome
Hybrid U-Net + Transformer
U-Net Encoder
Multi-resolution feature extraction captures patterns at different genomic scales (10bp to 100kb)
Transformer Backbone
Self-attention layers model long-range regulatory interactions across the full 1Mb input
CNN Decoder
Produces single-base resolution output, enabling precise variant effect prediction
Multi-task Heads
Specialized output heads for each modality (CAGE, ATAC, Hi-C, etc.)
Enformer
Pure Transformer Architecture
Conv Stem
Initial convolutional layers process DNA sequence into embeddings
Transformer Blocks
11 transformer blocks with self-attention, limited to 200kb effective context
Pooling Layers
Downsampling produces 128bp resolution output bins
Task Heads
Separate prediction heads for human and mouse genomes
Performance Benchmarks
How the models compare on key metrics
Input Context
Resolution
CAGE Correlation
Variant Effect
Modalities
3D Structure
When to Use Each Model
Recommendations based on your research needs
✓Choose AlphaGenome when:
- •Analyzing long-range regulatory interactions (enhancer-promoter loops)
- •Predicting variant effects at single-base resolution
- •Studying 3D genome structure and chromatin contacts
- •Working with non-coding variants in GWAS regions
- •Need comprehensive multi-modal predictions (11 types)
- •Prioritizing rare non-coding variants for clinical interpretation
○Consider Enformer when:
- •Working with existing Enformer-based pipelines
- •Need predictions for both human and mouse genomes
- •Analyzing well-characterized promoter regions
- •Computational resources are limited
- •Reproducibility with published Enformer benchmarks is important
- •Short-range regulatory analysis (<200kb) is sufficient
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Migrating from Enformer
Steps to transition your workflows to AlphaGenome
Update Input Format
AlphaGenome accepts longer sequences (1Mb vs 200kb). Adjust your input preprocessing to take advantage of extended context.
Map Output Tracks
AlphaGenome has different track organization. Use our mapping guide to convert Enformer track IDs to AlphaGenome equivalents.
Adjust Resolution
AlphaGenome outputs at 1bp resolution vs 128bp. Update downstream analysis to handle higher-resolution predictions.
Leverage New Modalities
Take advantage of AlphaGenome's additional prediction types like Hi-C contacts and MPRA activity.
Ready to Try AlphaGenome?
Experience the next generation of DNA sequence analysis with AlphaGenome's advanced capabilities.
Comparison FAQ
Common questions about AlphaGenome vs Enformer
Can I use both models together?
Yes! Many researchers use Enformer for quick initial screening and AlphaGenome for detailed analysis of top candidates. The models have complementary strengths - Enformer's speed for genome-wide scans and AlphaGenome's resolution for variant prioritization.
Is AlphaGenome just an updated Enformer?
No, AlphaGenome is a fundamentally different architecture. While both predict DNA sequence functions, AlphaGenome uses a hybrid U-Net + Transformer design that enables 5x longer context (1Mb vs 200kb) and single-base resolution output. It also includes new modalities like Hi-C contact prediction.
Which model is more accurate?
AlphaGenome generally shows higher correlation with experimental data across most benchmarks (e.g., 0.87 vs 0.82 for CAGE correlation). The improvement is especially notable for variant effect prediction (0.91 vs 0.84 AUROC) and long-range regulatory interactions.
Does AlphaGenome replace AlphaMissense?
No, they're complementary. AlphaMissense focuses on protein-coding variants (missense mutations), while AlphaGenome covers non-coding regions (98% of the genome). Together, they provide comprehensive genome-wide variant interpretation.
What about computational requirements?
AlphaGenome requires more compute due to its larger context and higher resolution. For high-throughput applications, consider using batch processing and our cloud API. Enformer may be more suitable for resource-constrained environments.
Are the training data the same?
Both models are trained on similar data sources (ENCODE, Roadmap Epigenomics), but AlphaGenome includes additional datasets and updated annotations. AlphaGenome's training also incorporates recent advances in self-supervised learning.