Google Deepmind shifts genomics AI from bulk tissue predictions to single cell type resolution with AlphaGenome 2

Google Deepmind has released AlphaGenome 2, a new AI model for genomics research. It predicts the effects of genetic variants on specific human cell types and finds nearly twice as many relevant gene changes as its predecessor.

Every cell in the human body carries the same DNA blueprint, but different cell types switch different genes on or off. A heart muscle cell reads DNA differently than an immune cell. Predicting those differences is one of the biggest unsolved problems in genomics. Google Deepmind aims to take a major step in that direction with AlphaGenome 2. The new model is designed to predict how DNA sequences and their variations affect specific human cell types. It was trained on more than 107 million individual cell profiles from 68 tissues, according to Google Deepmind, and is meant to help researchers uncover the cellular drivers of disease.Ad AlphaGenome 2 shifts from tissue-level overviews to individual cells Its predecessor, AlphaGenome, took in a DNA sequence a million letters long and predicted its properties across thousands of experiments spanning many tissues at once. According to Google Deepmind, that parallel approach spread the model’s capacity too thin and made it hard to capture subtle differences between closely related cell types.Ad AlphaGenome 2 instead works as a conditional model. Along with the DNA sequence, it takes a specific biological state as an extra input and calculates effects for that one cell type. The system was trained on more than 107 million individual cell profiles from 68 tissues in humans and mice. Beyond predicting how strongly a gene is expressed, the model also calculates how DNA is physically packaged in the nucleus, showing which stretches of the genome are accessible. It can now also predict DNA methylation, chemical tags that act as molecular switches to silence or activate genes.Ad AlphaGenome 2 can also infer missing data from a single measurement. If researchers measure only a cell’s gene activity, for instance, the model provides predictions for additional properties, such as how tightly or loosely certain stretches of DNA are packaged. That helps researchers decide which lab experiments are worth running next. AlphaGenome 2 pinpoints disease genes and vaccine responses more accurately On the TenK10K benchmark dataset with 28 primary immune cell types, AlphaGenome 2 identified about 30 percent of causal variants. In 43 percent of cases, it also correctly predicted whether a variant upregulates or downregulates a gene.