Researchers create better tools to read the hidden instructions in our DNA

DNA isn’t just a long string of genetic code, but an intricate 3D structure folded inside each cell. That means the tools used to study DNA need to be just as sophisticated—able to read not only the code itself, but also how it’s arranged in space.

Researchers at Case Western Reserve University compared different computer tools used to analyze how DNA folds and interacts inside individual cells. Their work, published in Nature Communications, could help scientists better understand how to read the body's genetic "instruction manual" in different circumstances—such as understanding what goes wrong when diseases develop or how cells change their jobs as we grow.

“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 School of Medicine. “Understanding this structure is crucial for figuring out how diseases develop and how we might treat them.”

The team addressed a key challenge: existing tools for analyzing DNA structure often produced inconsistent results. It’s like having multiple translators who can’t agree on what a foreign language text says, he said. 

Jin was joined in the research by Jing Li, the Arthur L. Parker Professor in the Department of Computer and Data Sciences at Case School of Engineering, and Yan Li, associate professor and vice chair of research in the genetics and genome sciences department.

The researchers tested 13 software tools on 10 datasets from mice and humans and found that different computer tools work better for different types of data. They also discovered that changing how data is prepared before analysis can dramatically improve results. Artificial intelligence computer programs work especially well with lower-quality and complex datasets.

“We’re essentially helping scientists find or build better microscopes to see how DNA works inside individual cells,” Jin said. “This could lead to a better understanding of genetic diseases and potentially new treatment strategies.”

Jin said the improved tools could help scientists see which genes switch on or off in diseased cells, explain why treatments work for some patients but not others and track how cells change during early development.

The research team also created a software package other scientists can use to find the best method to analyze their specific research—like how a GPS app finds the best route for your destination.

“Instead of researchers having to guess which tool might work best, our software can test multiple approaches and recommend the optimal one,” Jin said.

The methods are freely available to scientists worldwide through GitHub, an open-source platform that allows developers to create, store, manage and share their code. Jin said the widespread accessibility has the potential to accelerate discoveries across multiple fields of biomedical research.

“This is a significant step toward making sense of the massive genetic data from modern sequencing—and toward understanding how our genetic blueprint truly works,” Jin said.