AlphaGenome

DeepMind’s AI for Understanding the Genome

What is AlphaGenome?

‍ AlphaGenome is Google DeepMind’s breakthrough AI model purpose-built to decode the genetic “dark matter” of our DNA, especially the mysterious, non-coding regions that make up 98% of the genome. Unlike earlier genome AIs that focused mainly on protein-coding segments, AlphaGenome can process up to 1 million base pairs of DNA in one pass, predicting the molecular impact of both common and rare variants across protein-coding and regulatory sequences. This unified model helps researchers interpret how DNA changes affect gene expression, splicing, and chromatin activity, powers that are foundational for advances in disease research, drug discovery, and personalized medicine.

Key Features of AlphaGenome

Ultra-Long Sequence Analysis

Analyzes up to 1M base pairs, including non-coding regions.

Variant Effect Prediction

Predicts mutation impacts on gene expression, splicing, and regulation across tissues.

High-Resolution Function Modeling

Delivers base-pair–level predictions for gene features and regulatory signals.

Hybrid Deep Learning Architecture

Uses CNNs for local patterns and transformers for long-range DNA interactions.

State-of-the-Art Benchmarking

Outperforms other sequence-to-function models on 24/26 key genomics tasks.

Research-Ready Deployment

Accessible via API for academic research; broader applications planned.

Powered by Multi-Omics Data

Trained on large-scale datasets (ENCODE, GTEx, 4D Nucleome, FANTOM5) for broader accuracy.

Use Cases of AlphaGenome

Identify and prioritize variants that impact gene regulation, pinpoint splicing mutations, and understand the genetic basis of complex diseases.

Guide experimental design for CRISPR gene editing, and speed the discovery of regulatory elements and disease-linked loci.

Simulate effects of thousands of variants in silico, streamlining drug target validation for biotech and pharma R&D.

Enhance the interpretation of genome-wide association study (GWAS) data by predicting which variants are likely to have functional consequences.

Provide insights into variant pathogenicity in individual genomes, accelerating actionable findings in clinical genetics.

AlphaGenome

vs

Previous and Other Genomics AI

Feature	AlphaGenome	Previous Genomics AI	Other Seq-to-Function Models
Input Sequence Length	Up to 1M base pairs	≤32K base pairs	Varies (often ≤100K)
Variant Effect Prediction	Yes (multi-modal)	Limited (single output)	Partial
Non-Coding “Dark Matter” Support	Yes (98% of genome)	Minimal	Minimal
Model Architecture	CNN + Transformer	CNN only	CNN or RNN
Benchmark Accuracy	SOTA (24/26 tasks)	Moderate	Moderate - High
Deployment	Research API (now)	Open/source	Varies