On Sept. 8, Google's AI powerhouse announced a freely available database called AlphaGenome Atlas, which contains data on 9 billion possible changes to the human genetic code and estimates how these changes will affect different tissues and cellular processes. What's more, the tool spits out a simple score to help scientists quickly weigh the knock-on impacts of these changes.
Now, one year later, DeepMind has released AlphaGenome Atlas, which the tech giant hopes will make AlphaGenome accessible to more researchers.
"It's not this holy grail," Tuuli Lappalainen, a professor in genomics at KTH Royal Institute of Technology in Stockholm and a senior associate faculty member at the New York Genome Center who isn’t involved with AlphaGenome Atlas, told Live Science.
Geneticists now appreciate that this code, once thought to be "junk" DNA, is instead needed to manage how and when to read the parts of the genome that do encode our molecular building blocks, or coding genes. However, evolution hasn't produced an ordered genome. Instead, some regulatory instructions are distributed widely throughout the code.
You really do not need to be an expert in these methods to be able to go there and look something up in a browser.
But this power came at a price. Until the release of Atlas, AlphaGenome required users to have enough bioinformatics experience to access and use the models' automated programming interface — an area of expertise not all geneticists have. What's more, calculating the effects of each change was a strenuous workout for academics’ computing resources.
"You really do not need to be an expert in these methods to be able to go there and look something up in a browser," Lappalainen said.
Clouds in the crystal ball?
The tool still isn't a perfect predictor, however. A Sept. 11 preprint study from a research team led by Katie Pollard, director of the Gladstone Institute of Data Science and Biotechnology and a professor at the University of California, San Francisco, suggested that while AlphaGenome was adept at finding causal mutations, it persistently underestimated their impact.
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Although these tests are tedious and consume much more time than a search on AlphaGenome Atlas' interface, she said, they are essential for generating the data that AI models need to improve their predictions.
"We're still very much data-limited in biology, and that data needs to be created," Lappalainen said.
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