New AI model for DNA learns from evolution to unlock secrets of the human genome

More than two decades after scientists first sequenced the entire human genome —  all 3 billion “letters,” or base pairs, of DNA code — the meaning of much of this code remains a mystery.

While an estimated 1 to 2% of human DNA codes for proteins, the rest is a mix of “junk DNA” — evolutionary holdovers that no longer code for anything — and regulatory elements that control when, where and how strongly genes are expressed. These non-coding regions of the genome could hold the key to understanding a variety of inherited traits, including those that lead to diseases such as cancer, heart disease and autism. But first, scientists have to understand how variants in this DNA contribute to the multitude of traits that make each of us unique.

Researchers at UC Berkeley have created a new genomic language AI model, called GPN-Star, that far outpaces its competitors at identifying the most important genetic variants that contribute to inherited traits, including those that lead to disease. It is also far more computationally efficient than larger models, requiring only a fraction of the time and computing resources to train.

“Our model excels in making predictions about the pathogenicity of genetic variants, and identifying functional versus non-functional elements in the genome,” said study senior author Yun Song, a professor of computer science and statistics at Berkeley and an investigator at the Innovative Genomics Institute.