Research

Our lab studies epigenetics and DNA methylation, focusing on how chemical changes to DNA regulate gene activity and influence human health. By combining genomics, bioinformatics, and computational modeling, we aim to understand how epigenetic signals change during development, infection, and disease. Our work integrates experimental genomics with data-driven analysis to identify biological signals that can improve disease prediction and deepen our understanding of gene regulation.

Epigenetics and Human Disease

Our lab studies how the human epigenome responds to infection, immune stress, and organ transplantation, with a focus on DNA methylation dynamics in human disease. Using targeted bisulfite sequencing (TBS) and genome-wide methylation profiling, we analyze how epigenetic changes in immune cells reflect disease risk and immune adaptation. In transplant biology studies, our work has identified DNA methylation signatures that predict infection risk and viral reactivation in transplant recipients, providing new insight into how the immune system adjusts after transplantation. We also investigate epigenetic responses during viral infections, including host methylome changes associated with COVID-19 progression, with the goal of developing epigenetic biomarkers for disease prediction, immune monitoring, and precision medicine.

Computational Epigenomics

A central strength of the lab is bioinformatics, statistical modeling, and computational epigenomics. We develop analytical frameworks for interpreting DNA methylation sequencing data from TBS and other epigenomic platforms, enabling high-resolution analysis of epigenetic variation across tissues and disease states. Our work includes cell-type deconvolution methods for methylation sequencing data, statistical models for identifying disease-associated methylation patterns, and visualization tools for genome-wide methylation landscapes. We also routinely analyze single-cell transcriptome datasets to characterize cellular heterogeneity and immune cell states in complex biological systems. By integrating epigenomic sequencing, single-cell transcriptomics, and machine learning approaches, our research translates large-scale biological data into insights that advance studies of human disease, immune regulation, and transplant biology.

Development and Germ Cell Biology

We also study how epigenetic regulation shapes human development and germ cell formation. Our work has identified an important role for the epigenetic regulator TET1 in early human germ cell specification, providing insight into how epigenetic reprogramming guides cell fate decisions during development. These studies contribute to a better understanding of reproductive biology, developmental epigenetics, and germline gene regulation.

Data-Driven Biomedical Discovery

Our research bridges genomics, computational biology, and biomedical applications. By integrating large-scale genomic data with computational modeling, we aim to uncover epigenetic mechanisms that influence disease risk and cellular identity. Our work has contributed to studies published in journals including Nature Communications, Nature Cell Biology, Proceedings of the National Academy of Sciences (PNAS), iScience, and Epigenetics. We actively welcome collaborations in human disease research, precision health, and the development of new bioinformatics and AI-based methods for analyzing complex biological datasets.