CAREER: Computational tools for analyzing and interpreting DNA methylation

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

DNA methylation is a critical biological process that plays such an important role in gene regulation that it has often been referred to as the “fifth base of DNA.” In addition to playing an important role during development and throughout aging, aberrations in DNA methylation have been discovered to cause disease, including cancer and neurological disorders. Yet much is still unknown regarding where DNA methylation changes occur and which genes they impact.

This, coupled with its dynamic nature, which can vary across regions of the body, throughout the life cycle, and in response to the environment, makes accurately identifying the association of specific methylation patterns to biological phenomena of interest and interpreting their downstream impacts challenging. The project will result in a suite of new tools to enable analysis of methylation sites across experimental technologies, analyze methylation data in the context of different tissues and cell types, and predict the downstream impacts of methylation sites that are changing across conditions.

These tools will enable researchers to better interpret their methylation data in light of existing knowledge, which can in turn result in improved understanding of fundamental biological processes (e.g., development, aging). In addition to making all methods available as open-source software, databases of predictions and interactive visualizations will be developed and accessible online.

By doing so, biological researchers with no programming experience can use a query-based system to easily make and explore predictions in the context of their own data. This project also has a local outreach component to help increase the diversity and persistence of underrepresented minorities in STEM by engaging high school biology teachers and community college students with newly designed data driven curricula as well as research opportunities.

The research will adapt cutting edge deep learning methods used for imputation in other domains to increase the coverage of DNA methylation platforms that profile