Google DeepMind Releases AlphaGenome 2: Predicts DNA Impact in Specific Cell States, Self-Tested Immune Variant Detection Rises to 30%

Google DeepMind has released AlphaGenome 2, a new generation of DNA prediction models. Unlike AlphaGenome 1 launched last year and the AlphaGenome Atlas database released in September, AlphaGenome 2's core breakthrough lies in its ability to predict impacts for specific cell types and their physiological states, rather than providing only tissue-level averages. This capability aims to help researchers more accurately understand how genetic variations influence gene activity, thereby revealing disease causes and identifying new drug targets.

According to heise online, AlphaGenome 2 analyzes DNA segments up to one million base pairs long. Researchers can computationally modify individual DNA building blocks to predict their effects on gene activity across different cell types. The model was trained using human and mouse data, supplemented by 107 million single-cell profiles from 68 human tissues to adapt to new cell states. Additionally, users can input their own measurement data, allowing the model to explore unknown cell states without retraining.

In terms of performance, Google DeepMind's self-tests show that among 28 immune cell types, AlphaGenome 2 identified 30% of DNA variants likely responsible for changes in gene activity, compared to 16% for the previous model. A case study on chronic inflammatory bowel diseases demonstrated that the model successfully identified the key DNA variant causing overactivation of the ETS2 gene in specific immune cells, along with the affected gene and involved cell types.