AlphaGenome 2：解读细胞如何读取 DNA，从而更深入理解疾病

Adam Gayoso, Natasha Latysheva, Pushmeet Kohli and Žiga Avsec Our next-generation DNA sequence model bridges single-cell biology and human genetics, predicting how genetic variants affect hundreds of human cell types. Today, we are announcing AlphaGenome 2, our next-generation model that predicts how DNA sequences and their variations impact specific human cell types, giving scientists a powerful new tool to uncover the cellular drivers of disease. Cells are the building blocks of life. Every cell in our body carries the same DNA blueprint, yet each cell type and state—like a heart muscle cell or an immune cell fighting an infection—switches different genes on or off to do its job. Global efforts like the Human Cell Atlas are mapping this gene expression for millions of cells, but it is still an unsolved challenge to predict how subtle DNA variations affect this expression in cells, which is a key driver of human traits and disease. Last year, we launched AlphaGenome to predict how DNA variants affect molecular activity across broad tissues. We expanded its reach with AlphaGenome Atlas , a platform containing predictions for all 9 billion possible single-letter changes in the human genome. These tools help researchers prioritize and interpret variant effects, driving discoveries in rare diseases, complex traits, and fundamental biology. By zooming in from broad tissue averages to individual cell types and cell states, and redesigning the model’s architecture, AlphaGenome 2 represents a step-change improvement in variant predictive performance. Importantly, it allows researchers to predict variant effects in disease-relevant cells that would be missed in broad-tissue models, as well as predictions for cell and disease states that the model hasn’t seen before. AlphaGenome 2 identifies nearly twice as many variants that change gene expression in cells, compared to our previous state-of-the-art model. Trained on more than 100 million individual cell profiles across 68 tissues, the model turns static cell atlases into a predictive engine for human genetics. AlphaGenome 2 is available today for non-commercial research via the AlphaGenome API, and in private preview for commercial research. Comparing how AlphaGenome 1 and 2 treat cell types . AlphaGenome produces fixed parallel outputs of bulk tissues and cell lines from a 1-million-letter DNA sequence. AlphaGenome 2 is a conditional model, taking a specific biological condition alongside an input DNA sequence, either chosen from a reference catalog of cell types or generated on the fly from a scientist’s own single-cell experiment via our cell encoder. The first AlphaGenome model took a million-letter DNA sequence and predicted properties across thousands of experiments from many tissues all at once. While efficient, this parallel approach spread the model’s capacity thin and made it hard to capture subtle differences between closely related cell types. To improve this, we fundamentally redesigned how cell types are treated by the model. Instead of predicting in parallel, AlphaGenome 2 is a conditional model that predicts DNA effects for one condition - defined as a tissue, cell type or state - at a time. Alongside the million-letter DNA sequence, it uses the target biological condition as input at every stage of its architecture. This allows it to learn how the same stretch of DNA is read differently across cell types. A conditional model also allows cross-modality imputation, enabling AlphaGenome 2 to predict missing experimental data. The model separates a cell’s biological state from its outputs, so if a scientist inputs just one measurement, like single-cell gene activity, AlphaGenome 2 can then predict unmeasured properties, such as chromatin accessibility. This can then help researchers prioritize which experiments to do next. We trained AlphaGenome 2 on both bulk and single-cell data from human and mouse samples, including over 107 million individual cell profiles