Google DeepMind released AlphaGenome 2 on its blog on 11 November. Its predecessor, AlphaGenome, released last year, predicted the effects of changes to DNA letters, or variants, across whole tissues. If a variant acts in only one cell type, the tissue average can mask the effect.

The condition steers the whole model

AlphaGenome 2 reads a DNA sequence of 1 million bases and a single condition: a tissue, cell type or cell state. DeepMind said the condition steers every stage of the model. This lets the model learn how the same DNA is read in different cells.

Researchers can pick the condition from a ready-made list or build it from their own single-cell RNA sequencing, a method that measures gene activity in individual cells. A sample of a few dozen cells is enough, and the model does not need retraining, DeepMind said.

The model predicts gene expression and chromatin accessibility, among other things, and now also DNA methylation. From a single measurement, it can estimate properties that have not been measured. The training data comprised human and mouse tissue and single-cell data: more than 100 million cell profiles from 68 tissues, according to DeepMind.

More hits, but only a minority of causal variants

DeepMind tested the model on the TenK10K dataset, which covers 28 immune cell types. At 90 per cent precision, the model found 30 per cent of the causal variants estimated to alter gene expression. AlphaGenome 1 fine-tuned on single-cell data found 16 per cent. When 90 per cent of direction predictions were correct, the new model identified the direction of effect for 43 per cent of variants, against 23 per cent for its predecessor.

MarkTechPost reported that the model found 41 per cent of variants in a Parkinson’s disease dataset it had not seen before. Scooby, a model fitted to that specific dataset, found 17 per cent. The publication does not say at what precision threshold the figures were calculated.

MarkTechPost notes that the comparisons are DeepMind’s own. Unite.AI reported that the research report warns that even at high precision, the model finds only a minority of statistically likely causal variants. The model may underestimate effects that arise outside open chromatin, in very distant regulatory regions or only after transcription. Outside open chromatin, direction-of-effect predictions reach an AUROC of only 0.63, MarkTechPost said. A value of 0.5 equals guessing.

Unite.AI also said the report states that the cell-state descriptions are based on observational data, so they capture correlations rather than causal relationships. It is uncertain whether the model predicts, for example, the effects of a gene knockout. Transient, stimulus-activated and disease-related cell states are under-represented in the training data and the precomputed scores, and the model does not distinguish fine-grained neuronal subtypes well.

The Verge reported that the new model will not replace its predecessor any time soon. Žiga Avsec, DeepMind’s head of genomics, told the publication that computing predictions with the new model is more expensive.

Access through an API

AlphaGenome 2 is available for non-commercial research via the AlphaGenome API. Unite.AI reported that, according to the documentation, non-commercial use is free. For commercial research, the model is in limited preview. MarkTechPost said the model weights will be released only once the research is published in its final form.

DeepMind is also sharing precomputed results. They include links between regulatory regions and genes in 640 cell types or states and, according to Unite.AI, scores for more than 6.8 million common or trait- and disease-associated variants in 510 states. The model is intended for research use only and has not been validated for clinical use or diagnostics.