AlphaGenome

Long-context, high-resolution modeling: AlphaGenome overcomes prior trade-offs between sequence length and resolution by combining convolutional and transformer layers, enabling precise predictions of gene start/end points, RNA expression, splicing, chromatin accessibility, and protein binding across tissues. It achieves this with just half the compute budget of Enformer.

Multimodal and variant-aware: It can efficiently score the regulatory effects of genetic mutations by contrasting predictions between wild-type and mutated sequences, providing comprehensive insight into how variants might disrupt gene regulation.

Breakthrough splice-junction modeling: AlphaGenome is the first sequence model to explicitly predict RNA splice junction locations and their expression levels, unlocking a better understanding of diseases like spinal muscular atrophy and cystic fibrosis.

Benchmark leader: It outperforms existing models on 22/24 single-sequence benchmarks and 24/26 variant effect benchmarks, while being the only model able to predict all tested regulatory modalities in one pass.

Scalable and generalizable: AlphaGenome’s architecture supports adaptation to other species or regulatory modalities and allows downstream fine-tuning by researchers via API access. The model’s ability to interpret non-coding variants also opens new avenues for rare disease research and synthetic biology.