Genetics contributes to less than half of most chronic complex diseases. Many external forces play importance roles in health and disease. Our general research interests are to understand the complex interplays of genetics and environmental factors based on omics technologies. Metabolomics is the systematic study of all small molecules in a biological system. The metabolome consists of both endogenous metabolites and exogenous compounds derived from diet, gut microbes and environmental exposures. The microbiome, the microbes living in and on us, interact extensively with their host through metabolic exchanges and co-metabolism of foods and drugs, contributing to immune and metabolic diseases such as obesity, diabetes, etc. Finally, the exposome is a systematic, unbiased and omics-scale examination of external factors contributing to disease and health.

We employ knowledge-driven and data-driven approaches for multi-omics integration. The former is based on networks of known interactions (suitable for model organisms), while the latter is based on multivariate statistics to identify coherent patterns (suitable for any organisms).

Data is useless unless it can be understood by us to increase our knowledge or to inform our decisions. A long-term interest in the Xia Lab is to make omics data analytics accessible to broad researcher communities by leveraging cloud, web, statistics and data visualization techniques, coupled with comprehensive domain knowledge to facilitate hypothesis generation, novel discovery, and systems understanding. We use both local supercomputers and public cloud to enable high-performance data analysis. To date, we have developed a series of omics tool suites that are accessed by ~1,000,000 researchers worldwide in their omics data analysis and interpretation.

We are actively exploring new technologies to better educate, engage and empower users for omics data analysis. We are interested in conversational agent (chat-bot) powered by generative AI (such as ChatGPT/LLaMA), and virtual or augmented reality (VR/AR) for better user interactions.

Recent studies have shown that natural populations of C. elegans harbor distinct microbiome, which can be easily established and maintained in the laboratory. We are developing this model to study the effects of different microbiome compositions on worm fitness as well as response to chemical exposures. Distinct phenotypes are further investigated using deep sequencing and metabolomics for mechanistic understandings.

We use high-resolution Orbitrap LC-MS coupled with stable isotope labeling, a powerful microscope, and various phenotypic assays to gain insights into GEM interactions.