Google DeepMind announced AlphaGenome 2 on November 11, 2026, moving its DNA sequence model from broad tissue-level predictions toward specific human cell types and states. Researchers can access it now through the AlphaGenome API for non-commercial research. Commercial research access is a separate private preview, according to DeepMind’s announcement.
The release gives genetics researchers two useful capabilities: predicting a variant’s molecular effects in a chosen biological context, and supplying their own single-cell RNA sequencing data to define contexts outside the model’s reference catalog. DeepMind also reports stronger variant identification results, although those figures are company-reported benchmarks, not independent clinical validation.
The consequential change is how researchers can frame a question. A DNA change might have little apparent effect across a whole tissue but matter in one specialized cell population. AlphaGenome 2 makes that population part of the prediction request.
How Cell-Type Conditioning Changes the Model
AlphaGenome 1 took a DNA sequence roughly one million letters long and predicted molecular properties across thousands of tissue and cell-line experiments in parallel. Its outputs were fixed: researchers received predictions for the contexts the model had been designed to cover.
AlphaGenome 2 instead predicts for one biological condition at a time. That condition can describe a tissue, cell type or cell state. DeepMind says the model receives the condition alongside the DNA sequence at every stage of its architecture, allowing the same sequence to produce different predictions in different cellular contexts. Both versions use million-letter sequence windows. The announcement explains this architectural change.
A tissue contains multiple kinds of cells. Averaging their activity can obscure a regulatory effect that appears strongly in one population and weakly elsewhere. A conditional model gives researchers a way to ask about that population directly, such as a particular immune cell type in blood.
My view is that this change matters more for experimental planning than the headline training-data count. A researcher deciding which cells to study needs a context-specific hypothesis. A large aggregate score alone provides less guidance about where to look.
The architecture also supports cross-modality imputation: predicting an unmeasured molecular property from an available measurement. DeepMind gives the example of supplying single-cell gene activity and predicting chromatin accessibility, which describes how accessible regions of DNA are to regulatory machinery.
