Machine learning identifies the likely “initiator” and enables new predictions about DNA mutations that can cause disease.
Every human cell depends on tens of thousands of genes being switched on at the right time and in the right amount. Specialized stretches of DNA coordinate this activity, ultimately directing the production of enzymes, hormones, proteins, and other components essential to cell structure and function. When that regulation goes wrong, cells can malfunction and contribute to disorders including cancer.
To better understand the DNA sequences that control this process, researchers in the University of California San Diego Professor James T. Kadonaga’s laboratory focused on a crucial region known as the “initiator.” This DNA segment marks the point where information encoded in a gene first begins to be converted, or expressed, into functional products.
AI decoded the initiator’s DNA signature
In work led by graduate student researcher Torrey Rhyne-Carrigg, the researchers measured gene expression activity across approximately 500,000 different versions of the initiator using high-throughput DNA sequencing. They then fed those results into machine learning models that learned the characteristic DNA sequence associated with the initiator. Once that signature was identified, the researchers searched human genes for it and found that roughly 60% contain an 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 model could predict mutation effects
Decoding the initiator gives researchers a way to predict how DNA mutations affecting this region could contribute to different disorders. The study’s data and models could also help scientists create synthetic promoters, DNA sequences designed to switch genes on and off, with specific customized functions.
“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.”
Reference: “Machine learning analysis of the human initiator region reveals key features of different types of core promoters” by Torrey E. Rhyne-Carrigg, Long Vo ngoc, Claudia Medrano, Kassidy E. Gillespie and James T. Kadonaga, 31 July 2026, Genes & Development.
DOI: 10.1101/gad.353623.125
Never miss a breakthrough: Join the SciTechDaily newsletter.
Follow us on Google and Google News.