Google DeepMind has announced the release of AlphaGenome, a new AI model designed to predict how genetic variants affect gene regulation across the entire genome. It represents a significant advancement in computational genomics by integrating long-range sequence context with base-pair resolution in a single, general-purpose architecture.
AlphaGenome processes up to 1 million base-pairs of DNA at once and outputs high-resolution predictions across thousands of molecular modalities, including gene expression, chromatin accessibility, transcription start sites, RNA splicing, and protein binding. It allows researchers to evaluate the effects of both common and rare variants, not just in protein-coding regions, but in the far more complex non-coding regulatory regions that constitute 98% of the human genome.
Technically, AlphaGenome combines convolutional neural networks (CNNs) to detect local sequence motifs and transformers to model long-range interactions, all trained on rich multi-omic datasets from ENCODE, GTEx, 4D Nucleome, and FANTOM5. The architecture achieves state-of-the-art performance across a broad range of genomic benchmarks, outperforming task-specific models in 24 out of 26 evaluations of variant effect prediction.
A notable innovation is AlphaGenome’s ability to directly model RNA splice junctions, a feature crucial for understanding many genetic diseases caused by splicing errors. The model can also contrast mutated and reference sequences to quantify the regulatory impact of variants across tissues and cell types — a key capability for studying disease-associated loci and interpreting genome-wide association studies (GWAS).
Training efficiency was also improved: a full AlphaGenome model was trained in just four hours on TPUs, using half the compute budget of DeepMind’s earlier Enformer model, thanks to optimized architecture and data pipelines.
The model is now available via the AlphaGenome API for non-commercial research use, enabling scientists to generate functional hypotheses at scale without needing to combine disparate tools or models. DeepMind has indicated plans for further extension to new species, tasks, and fine-tuned clinical applications.
This release also aligns with a broader conversation around the interpretability and emotional context of AI in medicine. As Graevka Suvorov, an AI alignment researcher, commented:
The true frontier for MedGemma isn’t just diagnostic accuracy, but the informational and psychological state it creates in the patient. A diagnosis without context is a data point that can create fear. A diagnosis delivered with clarity is the first step to healing. An AI with a true ‘informational bedside manner’—one that understands it’s not just treating an image, but a person’s entire reality—is the next real leap in AGI.
AlphaGenome pushes the field closer to that vision, enabling deeper, more accurate interpretations of the genome and offering a unified model for understanding biology at the sequence level.
