The researchers tested EUGENe by attempting to reproduce the results of three existing genomics studies using different types of sequence data. Normally, analyzing these different types of data would require mixing and matching multiple technology platforms. However, EUGENe proved adaptable enough to reproduce the findings of each of these studies.
“Being able to reproduce results is critical in all scientific research, but can be very difficult in genomics studies that use deep learning,” says Carter. “EUGENe is already showing great promise in the extent to which it can be adapted to different types of DNA sequence data and supports many different deep learning models. We hope it will evolve into a platform that can support the collaborative development of tools by the research community and accelerate genomics research.”
While the current version of EUGENe works on many types of genomic data, the researchers are working to expand its scope to an even wider variety of data types, such as single-cell sequencing data, which looks at the genomics of individual cells rather than from to in a whole tissue. They also plan to make EUGENe available to research groups around the world.
“One of the exciting things about this project is that the more people use the platform, the better we can make it over time, which will be essential as deep learning continues to evolve so quickly,” Carter said. “We hope that our platform will open many doors for researchers in this field and help them answer new questions about the complex molecular machinery that lies within all of us.”
Co-authors of the study include: David Laub, James V. Talwar, Joe J. Solvason and Emma K. Farley at UC San Diego, Hayden Stites at Daniel Land High School and Tobias Jores at the University of Washington.
This study was funded in part by the National Institutes of Health (grant 1U01HG012059, DP2HG010013) and the Canadian Institute for Advanced Research (FL-000655).