UCSD - University of California - San Diego

08/21/2026 | Press release | Distributed by Public on 08/21/2026 13:25

Researchers Use AI to Decode Key DNA Sequence in Gene Activation

Published Date

August 21, 2026

Article Content

Key Takeaways

  • Scientists have revealed the DNA sequence code of the "initiator," a crucial sequence that kick-starts gene activation.
  • A machine learning analysis of a half million experimental data points yielded an AI model that has deciphered the identity of the initiator.
  • With the initiator's sequence known, researchers can now scan for mutations that may lead to diseases such as cancer.

Precise activation of tens of thousands of genes is critical for healthy development and growth. Specialized segments of our DNA are responsible for carefully orchestrating genetic sequences that result in the production of enzymes, hormones, proteins and other crucial components underlying cell structure and function. But if these genes are not correctly activated, cells can stop functioning or result in various disorders, including cancer.

To fully understand specific sequences within DNA that enable gene activation, researchers in University of California San Diego Professor James T. Kadonaga's laboratory set out to decipher an important segment of DNA known as the "initiator." This is the site at which the instructions coded in genes are first converted, or expressed, into functional products.

In the new study led by graduate student researcher Torrey Rhyne-Carrigg, scientists used high-throughput DNA sequencing technology to determine the gene expression activity of approximately 500,000 different versions of the initiator. With this information, they employed machine learning, a type of artificial intelligence, to create an AI model that then decoded the initiator's signature DNA pattern. With the initiator's DNA identity unmasked, the researchers could then search for its telltale sequence, finding that about 60% of human genes contain the initiator.

"These AI models were found to provide, for the first time, strong predictions of the presence or absence of the initiator in human genes, and were thus able to decode the DNA base sequence pattern of the initiator," said Kadonaga, a professor in the UC San Diego Department of Molecular Biology, School of Biological Sciences.

The newly uncovered information gives researchers the ability to predict the effects of DNA mutations that can lead to various disorders tied to the initiator. The data and models resulting from the new study could also be used to design synthetic promoters, sequences that turn genes on and off, with customized functions.

This work is a step forward in the combined use of laboratory experiments and AI to decipher the information that is embedded in the sequence of the DNA bases in humans. - Professor James T. Kadonaga

"More globally, this work is a step forward in the combined use of laboratory experiments and AI to decipher the information that is embedded in the sequence of the DNA bases in humans," said Kadonaga. "Ultimately, within the six billion bases of DNA in each of our cells, there is a gene expression code that specifies when, where and to what extent each of our genes should be turned on or off. If we had an AI model for the entire gene expression code, we would be able to predict the activity of each of the different variants of genes in different people. The new AI model for the initiator is a small but important part of this gene expression code, and I am optimistic that we will expand our AI models of the human gene expression code in the not-too-distant future."

The study, "Machine learning analysis of the human initiator region reveals key features of different types of core promoters," was authored by Torrey E. Rhyne-Carrigg, Long Vo ngoc, Claudia Medrano, Kassidy E. Gillespie and James T. Kadonaga. The researchers used the Expanse CPU at the San Diego Supercomputer Center through the Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support Program (project BIO230152; National Science Foundation (NSF) grants 2138259, 2138286, 2138307, 2137603 and 2138296). Other support was provided by the National Institutes of Health (grant T32 GM133351 to UC San Diego, Biological Sciences Pathways in Biological Sciences Training Program); NSF Graduate Research Fellowship (GRFP) DGE-2545911; and NIH grant R35 GM118060.

Learn more about research and education at UC San Diego in: Artificial Intelligence,

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