University of Missouri researchers have developed a groundbreaking new AI tool. This clever tool not only helps predict the three-dimensional shapes of chromosomes inside individual cells. Scientists Yanli Wang and Jianlin “Jack” Cheng are at the helm of this revolutionary advance. It offers new perspectives on genetic architectures of common variation and disease.
This AI tool implements SO-equivariant graph neural networks to recreate complex 3D structures of chromosomes. We’ve now developed the technology to allow researchers to visualize how DNA is structured in cells. Each human cell has about six feet of DNA and none of it would fit inside its cell without being folded many times over. Since chromosome structure can vary from cell to cell, the new tool allows scientists to see how chromosomes behave genetically in precise ways.
Wang and Cheng’s study, published in NAR Genomics and Bioinformatics, demonstrates the importance of this discovery. View the publication here under the doi 10.1093/nargab/lqaf027. The duo emphasizes that “every single cell can have a different chromosome structure,” which is crucial for understanding the underlying mechanisms of gene regulation.
“This is important because even cells from the same part of the body can have chromosomes folded in very different ways,” Wang explained. He further noted that “that folding controls which genes are turned on or off.” We use an AI tool to predict detailed 3D models of chromosome structures. This kind of innovation represents a new frontier for scientists to study these behavioral differences.
The consequences of this study go far beyond just creating a fancy map. Cheng, who serves as a Curators’ Distinguished Professor of Electrical Engineering and Computer Science, commented, “Our tool helps scientists study those differences in detail—which can lead to new insights into health and disease.” The need for an improved understanding of the structural organization of chromosomes to make predictions about genetic disorders or develop therapeutic approaches.
Take a look at a sample of the 3D models generated by the new AI tool. It highlights the tremendous opportunity we have to transform genetic and biomedical research. This widely adopted advance is a big step forward for the field. It allows researchers to more precisely explore the complicated relationships between chromosome architecture and gene activity.