To better understand the DNA sequences that control this process, researchers in the laboratory of University of California San Diego Professor James T. Kadonaga focused on an important DNA element known as the “initiator.” The initiator marks the location where the information encoded in a gene begins to be converted, or expressed, into a functional product.
AI Decodes the Initiator Sequence
In the new study, led by graduate student researcher Torrey Rhyne-Carrigg, the team used high-throughput DNA sequencing to measure gene expression activity across approximately 500,000 different versions of the initiator.
The researchers then used those results to train a machine learning system, a form of artificial intelligence, to identify the characteristic DNA pattern associated with the initiator. Once the model had decoded that signature, the team searched human genes for the sequence and found that roughly 60% 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.
Predicting the Effects of DNA Mutations
The findings could help researchers anticipate how mutations affecting the initiator may alter gene activity and contribute to a range of disorders. The study’s data and AI models may also support the design of synthetic promoters, sequences that can switch genes on or off, with functions tailored for specific purposes.
More broadly, the research shows how laboratory experiments and artificial intelligence can be combined to uncover information encoded in human DNA.
“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.”
