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Artificial intelligence has gotten a bad reputation lately, and often for good reason. But a team of scientists at Google’s DeepMind now claims to have found a revolutionary use case for AI: helping humanity unravel the “dark matter” of our genome more effectively than ever before.
In a study published today in Nature, DeepMind researchers debuted their deep learning model, dubbed AlphaGenome. Compared to existing models, AlphaGenome can predict the function of much longer sequences of DNA while still maintaining a similar level of accuracy, the researchers claim. The team is hopeful its model can become a valuable tool to analyze how subtle variations in human DNA can affect our health and biology, particularly in the vast majority of the genome that works silently in the background.
“We are thrilled to introduce AlphaGenome: our solution to deciphering the complex regulatory code,” said Pushmeet Kohli, vice president of research at Google DeepMind, in a press briefing held Tuesday.
A guide to our genetic dark matter
Our DNA contains the instructions for building and regulating every biological aspect of ourselves. But only a tiny portion of our genes, 2% or so, actually carry the code for the tens to hundreds of thousands of proteins that perform the functions a body needs to survive, such as insulin or collagen. The other 98% of our DNA is made of non-coding regions, more eloquently known as the dark matter of our genome. Scientists once assumed our genetic dark matter was comprised of worthless junk DNA, but we now know that it contains sequences vital to regulating our protein-making genes.
While scientists have mapped out most of the human genome, we still know very little about how many of these genes work, especially those found in non-coding regions; we’re also largely in the dark about how variations in these genes can affect their functioning. Long before AI became a cultural buzzword (and punching bag), scientists had been using deep learning models—trained on lab data—to more efficiently sift through the mountains of the human genome and to predict a gene or DNA sequence’s function. But DeepMind researchers say AlphaGenome is the most comprehensive and accurate DNA sequence model to date.
The DeepMind researchers trained the model on both human and mouse genomes. It can reportedly analyze up to 1 megabase (Mb)—about 1 million DNA letters—at a time, compared to older models capable of analyzing upwards of 500 kilobases (kb), though at some cost. From that sequence, the model is said to “predict thousands of functional genomic tracks.” These tracks don’t just include how a gene or DNA sequence is expressed but also other less visible functions. These include the interactions between coding and non-coding regions of DNA, or the structure of chromatins (the loose packages of genetic material typically found in a cell; chromosomes are the more neatly packaged version).
In the paper, the researchers also detailed how AlphaGenome matched or outperformed other existing AI models in 25 out of 26 tests measuring how well it could predict the effects of a genetic variant. More than just accuracy, however, the model can also do more at once; it can simultaneously predict nearly 6,000 human genetic signals tied to specific functions, according to the researchers.
The future of AI genomics
At least some outside scientists have praised the capabilities of AlphaGenome, while noting that it can’t solve every lingering mystery about our genetic code just yet.
“At the Wellcome Sanger Institute we have tested AlphaGenome using over half a million new experiments and it does indeed perform very well,” Ben Lehner, head of Generative and Synthetic Genomics at the University of Cambridge’s Wellcome Sanger Institute, told the Science Media Center. “However, AlphaGenome is far from perfect and there is still a lot of work to do. AI models are only as good as the data used to train them. Most existing data in biology is not very suitable for AI—the datasets are too small and not well standardized.”
All that said, the DeepMind researchers—and others in the field—believe AlphaGenome marks a true milestone in AI genomics, one that could help make the technology practical for broader use. They argue that AlphaGenome, or similar models, could now be used to better diagnose rare genetic diseases, identify mutations that drive cancer, or uncover new drug targets.
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Ed Cara
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