Google DeepMind has unveiled AlphaGenome 2, an upgraded DNA sequence model that predicts how genetic variants affect different human cell types, helping researchers investigate the genetic mechanisms underlying disease.
The model builds on the original AlphaGenome, which maps molecular activity across a broad range of tissues and covers both coding and non-coding regions of the genome. This provides a unified approach to assessing the potential effects of genetic variants.
Earlier this year, the company introduced the AlphaGenome Atlas, a separate predictive resource mapping the effects of every possible single-letter DNA change in the human genome.
According to the company, AlphaGenome 2 moves beyond broad tissue-level averages to analyse individual cell types and states. This allows researchers to examine how genetic variants affect disease-relevant cells and how their effects may differ across cellular conditions.
The model was trained on more than 107 million individual cell profiles from human and mouse samples spanning 68 tissues. Unlike its predecessor, which generated predictions across multiple tissues in parallel, the updated model takes a specific biological condition alongside a DNA sequence as input.
Researchers can select a cell type from a reference catalogue or use their own single-cell data to examine a previously unseen cellular state. The model can also estimate missing experimental measurements, such as chromatin accessibility, from other available data.
In a benchmark using TenK10K, a single-cell dataset covering 28 primary immune cell types, AlphaGenome 2 identified 30% of causal variants, compared with 16% for the original model fine-tuned on single-cell data and other specialised tools, according to Google DeepMind.
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