DNA’s three-dimensional structure influences how genes function and interact, affecting processes such as development and disease progression. Scientists at Case Western Reserve University have compared computational tools that map this structure to determine which methods most accurately capture how DNA folds inside cells.
“The 3D structure of DNA affects how genes interact with each other, just like the layout of a house affects how people move through it,” said Fulai Jin, professor in the Department of Genetics and Genome Sciences at the Case Western Reserve School of Medicine. “Understanding this structure is crucial for figuring out how diseases develop and how we might treat them.”
Jin’s team, which included Jing Li and Yan Li, tested 13 analysis programs on 10 datasets from human and mouse cells. They found that different algorithms perform better depending on the data type and preparation method. Adjusting how the data are processed before analysis can substantially improve accuracy, particularly when artificial intelligence models are used to interpret lower-quality or more complex datasets.
“We’re essentially helping scientists find or build better microscopes to see how DNA works inside individual cells,” Jin said.
The group also released a software package that automatically tests and recommends the most suitable tool for a given dataset, similar to how a GPS suggests the fastest route. The open-source resource is available through GitHub to support other researchers in evaluating gene interactions and regulatory mechanisms.
Jin said the improved computational approaches could aid studies of gene regulation in disease, explain variable patient responses to treatment, and track gene activity during early development.
Publication Details
Plummer, D., Lang, X., Zhang, S. et al. A comprehensive benchmark of single-cell Hi-C embedding tools. Nat Commun 16, 9119 (2025). https://doi.org/10.1038/s41467-025-64186-4
“The 3D structure of DNA affects how genes interact with each other, just like the layout of a house affects how people move through it,” said Fulai Jin, professor in the Department of Genetics and Genome Sciences at the Case Western Reserve School of Medicine. “Understanding this structure is crucial for figuring out how diseases develop and how we might treat them.”
Jin’s team, which included Jing Li and Yan Li, tested 13 analysis programs on 10 datasets from human and mouse cells. They found that different algorithms perform better depending on the data type and preparation method. Adjusting how the data are processed before analysis can substantially improve accuracy, particularly when artificial intelligence models are used to interpret lower-quality or more complex datasets.
“We’re essentially helping scientists find or build better microscopes to see how DNA works inside individual cells,” Jin said.
The group also released a software package that automatically tests and recommends the most suitable tool for a given dataset, similar to how a GPS suggests the fastest route. The open-source resource is available through GitHub to support other researchers in evaluating gene interactions and regulatory mechanisms.
Jin said the improved computational approaches could aid studies of gene regulation in disease, explain variable patient responses to treatment, and track gene activity during early development.
Publication Details
Plummer, D., Lang, X., Zhang, S. et al. A comprehensive benchmark of single-cell Hi-C embedding tools. Nat Commun 16, 9119 (2025). https://doi.org/10.1038/s41467-025-64186-4