GoKawiilThe human genome is made up of roughly 3 billion bases, or letters, of DNA.Credit: Yuichiro Chino/Getty
The human genome is an easy place to get lost. Only 2% of its 3 billion letters encode proteins and the rest is diabolically hard to decipher. An AI-generated ‘atlas’ of the human genome unveiled1 today by Google DeepMind aims to guide scientists through our biological code.
One of the most common types of variation in the human genome are changes to individual nucleotides, or letters, which contribute to differences between people including disease risk; some rare single letter changes can directly cause disease.
The AlphaGenome Atlas charts the effects of 9 billion single DNA letter changes to the human genome — every possible mutation — using predictions generated by the AlphaGenome AI model released by DeepMind in London last year. It is freely available for non-commercial use.
The atlas could help to diagnose rare, unexplained diseases and uncover the hidden biology of common illnesses and biological traits, say researchers. It might even reveal some of the hidden rules by which DNA sequences control gene activity.
But it won’t replace experiments or, in the case of diagnosing disease, accounting for details of individual cases, says Martin Kircher, a bioinformatician at the Max Delbrück Centre for Molecular Medicine in Berlin. “This is a useful and generous way to scale up access to a strong model.”
Instant access
Since AlphaGenome’s release, around 9,000 researchers have accessed the model’s predictions through an automated programming interface (API), says Dhavi Hariharan, a DeepMind product manager. But doing so requires writing software code — a barrier for some biologists, she says.
To create the AlphaGenome Atlas, DeepMind computed predictions for each of the 3 possible nucleotide changes across every DNA letter in the human genome — 1 petabyte’s worth of data. It also captures more than 100 million short insertions or deletions observed in human genomes. The effort was inspired by DeepMind's AlphaFold database of more than 200 million protein-structure predictions, which has been accessed by millions of users, according to DeepMind.
“If you remove the friction you also increase the curiosity for people to dive in,” says Žiga Avsec, who leads the AlphaGenome team. “Instant access is something that feels magical.”
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