Genetic diversity benefits humans, allowing us to fight diseases and adapt to changing environmental conditions. But, too much variation among genes can also cause diseases, like cancer, heart disease and birth defects in children.
Raquel Assis, Ph.D., principal investigator and assistant professor in the College of Engineering and Computer Science and faculty fellow in the Institute for Human Health and Disease Intervention (I-Health), has dedicated her career understanding why and how these variations happen.
Specifically, Assis focuses on developing computerized software programs to help researchers get a closer look at the connection between genes and traits. Ultimately, this can lead to better disease treatment approaches. Her work focuses on the functional outcomes of large-scale mutations like duplications, which create copies of existing genes. Such mutations can lead to either an adaptation such as a stronger immune response to a new pathogen, or a disease like cancer and diabetes. Her new software program, classifies the functional outcomes of duplicate genes.
“Duplication seems to occur faster than all other types of mutation. Learning about its roles in evolution and disease requires an understanding of how DNA sequence changes caused by duplication impact gene functions,” she said.
When Assis learned about the genome sequence, which is the complete blueprint of all the individual genes in the human body, she was entering college as a first-generation student at the University of Florida, where she completed her bachelor’s degree in psychology and zoology in 2006. Studying this dual degree, Assis said was her way of taking neuroscience classes, which was her initial field of interest.
During her time as an undergraduate, the human genome project was new to the scientific community. Assis realized while working in her professor’s biology lab that to better understand specific entities inside the genome, scientists needed better software programs. She wanted to be able to answer questions like why there is so much variation within genes.
The following year in 2007, Assis began a doctorate program at the University of Michigan in bioinformatics, a field that combines computer science and biology to study genomes. There she created her first computer software program called Bridges, which allowed her to identify and search for similarities in the DNA of one human to the next.
For her dissertation, Assis used Bridges to further look at large pieces of DNA inside the genome to determine how and why gene duplication occurs. Her realization that duplication is a common mutation event that leads to dramatic changes in the DNA sequence sparked her interest in studying their functional outcomes.
“By studying duplications that result in beneficial traits, we can gain insight into those that cause disease. Mutations are random events, but knowing their functional outcomes helps us understand what makes them successful or not,” she said.
After completing her doctorate degree in 2011, Assis continued her study of genomes as a National Institute of Health postdoctoral fellow at the University of California, Berkeley, where she worked until 2013. She developed the first genome-wide method for predicting the functional outcomes of gene duplication events. The following year she started her first research and teaching position as an assistant professor in the department of biology at Pennsylvania State University.
Five years later, in 2019, she joined Florida Atlantic University where her work in bioinformatics allowed her to transfer as a joint hire in the College of Engineering and Computer Science and I-Health.
“I get the best of both worlds. My joint position in the department of computer and electrical engineering and computer science and I-Health gives me access to researchers who are interested in both the computational approaches and biological questions important to my work.”
Her newest project in the department, she said, involves developing machine learning programs that can take a learned response like understanding the functional outcomes of gene duplication and apply it to make predictions about future mutation events.
“These machine learning methods lay the groundwork for understanding connections between large-scale mutations and functions,” she said. “They have the potential to contribute to future studies in both evolution and human disease.” ◆
