Artificial intelligence tools predict DNA’s regulatory role and 3D structure: Sequence modeling algorithms may eventually lead to new ways to fight diseases caused by genetic mutations

According to two recent studies, newly developed artificial intelligence (AI) programs accurately predicted the role of regulatory elements and three-dimensional (3D) structure of DNA, based entirely on its raw sequence. Nature Genetics. Study author Jian Zhou, PhD, assistant professor, said these tools could eventually shed new light on how genetic mutations cause disease and how genetic sequences affect the spatial organization and function of chromosomal DNA in the nucleus. , can lead to a new understanding of it. in the Lydia Hill Department of Bioinformatics at UTSW.

Dr. Zhou, a member of the Harold C. Simmons Comprehensive, said, “Taken together, these two programs provide a more complete picture of how the DNA sequence changes, its spatial organization and function, even in non-coding regions. can have a dramatic effect.” Cancer Center, a Lupe Murchison Foundation Scholar in Medical Research, and a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar.

Only 1% of human DNA encodes the instructions for making proteins. Research in recent decades has shown that much of the remaining non-coding genetic material consists of regulatory elements – such as promoters, enhancers, silencers and insulators – that control the way coding DNA is expressed. How the sequence controls the functions of most of these regulatory elements is not well understood, Dr. Zhou explained.

To better understand these regulatory components, he and collaborators at Princeton University and the Flatiron Institute developed a deep learning model called SeI, which precisely translates these snippets of non-coding DNA into 40 “sequence classes,” or jobs. Sorts by – eg, as an enhancer for stem cell or brain cell gene activity. These 40 sequence classes, developed using approximately 22,000 data sets from previous studies studying genome regulation, cover more than 97% of the human genome. In addition, SeI can score any sequence by its predicted activity in each of the 40 sequence classes and predict how mutations affect such activities.

By applying SeI to human genetics data, the researchers were able to characterize the regulatory architecture of 47 traits and diseases recorded in the UK Biobank database and explain how mutations in regulatory elements lead to specific pathologies. Such capabilities may help to achieve a more systematic understanding of how genomic sequence changes are associated with diseases and other traits. The findings were published this month.

In May, Dr. Zhou reported the development of a different tool called Orca, which predicts the 3D architecture of DNA in chromosomes based on its sequence. Using existing data sets of DNA sequences and structural data obtained from previous studies, Dr. Zhou trained the model to make connections and evaluated the model’s ability to predict structure at various length scales.

The findings showed that the orca predicted DNA structures based on their sequences with high accuracy, which contain mutations associated with various health conditions, including forms of leukemia and organ malformations. The orca also enabled researchers to generate new hypotheses about how the DNA sequence controls its local and large-scale 3D structure.

Dr. Zhou said he and his colleagues plan to use Sei and Orca, which are publicly available on web servers and as open-source code, to determine the molecular and physical manifestations of diseases that cause genetics. To explore the role of mutations – research that could eventually lead to new ways of treating these conditions.

The ORCA study was supported by grants from CPRIT (RR190071), the National Institutes of Health (DP2GM146336), and the UT Southwestern Endowed Scholars Program in Medical Sciences.

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