DNA is the blueprint for life, influencing everything about us—including our health. We know that our genes, the genetic “words” that encode proteins, play a major role in health and disease. But the vast majority of our genome—more than 98 percent, in fact—consists of DNA that doesn’t build proteins. Once disregarded as “junk DNA,” scientists now know that this molecular dark matter is crucial for determining gene activity in ways that keep us healthy—or cause disease.
Exactly how this DNA shapes gene expression is a mystery—but now the AI lab Google DeepMind has built a model that it says can predict the function of long stretches of noncoding DNA. The information it turns up could help solve the problem of predicting how these chunks of DNA influence our health.
Called AlphaGenome, the model takes in sequences of up to one million DNA letters, also known as base pairs, and predicts how mutations in those stretches affect gene expression. The model is described today in Nature. The tool, a version of which DeepMind has made freely available to other researchers, could help scientists narrow down theories for how certain DNA changes affect gene function. In turn, this knowledge could help scientists craft better treatments for genetic diseases.
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“Ever since the human genome was sequenced, people have been trying to understand the semantics of it—this has been a longstanding goal for DeepMind,” says Pushmeet Kohli, the company’s vice president for science and a coauthor of the new study. “It’s like you have a huge book of three billion characters and something wrong happened in this book.”
“AlphaGenome can be used to say, ‘If you change these words, what would be the effect?’” he adds.
AlphaGenome works by combining information from several datasets focused on different aspects of gene expression—how genes are turned on or off. The model is a successor of sorts to DeepMind’s AlphaFold, an AI model that predicts the structure of almost every known protein from its amino acid sequence—a central problem in biology. The researchers behind that effort shared the Nobel Prize in Chemistry in 2024. And in 2023 DeepMind released AlphaMissense, another AI tool that predicts how mutations in the regions of the genome that do generate proteins affect gene function.
AlphaGenome’s developers say it performs as well or better than most other specialized models they tested. Previous tools generally required a trade-off between the length of a DNA sequence that could be used as input and accuracy. A key advance of AlphaGenome’s approach is the ability to make accurate predictions about the function of extremely long genome sequences.
“The genome is like the recipe of life,” Kohli said in a press briefing about the work. “And really understanding ‘What is the effect of changing any part of the recipe?’ is what AlphaGenome sort of looks at.”
AlphaGenome is a research tool—it’s not meant to be used clinically and its results can’t be easily applied to individual humans. But it could have applications in understanding how the genome regulates genes in different types of cells or tissues. It could also help us understand diseases through massive genome-wide association studies or assist in studying cancer, because tumors can have many different genetic mutations, and it’s not always clear which ones cause illness. The tool could even be useful for diagnosing rare conditions and designing new gene therapies.
“For all the best evaluations we have, AlphaGenome looks like they pushed [the field] forward a little bit,” says David Kelley, a principal investigator at Calico Life Sciences, a subsidiary of Google’s parent company Alphabet. Kelley was not involved with the study but has collaborated with the authors on a previous AI model. “I think the long sequence length that they’re able to work with here is definitely one of those major engineering breakthroughs,” he says, adding that the new AI is “incremental but real progress.”
AlphaGenome has its limitations. It was trained on just two species—humans and mice—so isn’t applicable to other species yet. And the tool might predict that a given DNA variant has no effect on gene expression when in fact it does.
Predicting how a disease manifests from the genome “is an extremely hard problem, and this model is not able to magically predict that,” says Žiga Avsec, a research scientist leading DeepMind’s genomics initiative. But AlphaGenome can narrow down the pool of possible mutations involved in a disease, making it useful for prioritizing research to pinpoint which gene variants are actually causing problems, he says.
DeepMind’s researchers acknowledge that the model is imperfect. The company’s researchers are working to both boost what its predictive power is and better report how uncertain those predictions are.