Assistant Professor - Large Data Analysis
(Cross-appointed with the Department of Animal Science)
T: 514-398-8668 | jeff.xia [at] mcgill.ca (Email )| Parasitology Building P-011 | Website
BMed (Peking University Health Science Center)
MSc, PhD (University of Alberta)
Dr. Xia obtained his Bachelor of Medicine (5-year program) from Peking University Health Science Center, China, in 2001. He then moved to Canada in 2004 and obtained his MSc degree (research area: Immunology & Genetics) in 2006, followed by his PhD (research area: Bioinformatics & Metabolomics) in 2011, both at the University of Alberta, Canada. From 2012-2014, he did his postdoctoral training (research area: Next-generation sequencing & Systems biology) at the University of British Columbia, Canada. Since 2015, he has been an Assistant Professor at McGill University. His lab focuses on leveraging bioinformatics, statistics and large data to study the effects of biological (i.e. gut microbiota and helminths), environmental and nutritional factors on health and disease.
Awards and Recognitions
2016-2018: FRQNT New University Researcher Award
2012-2014: Killam Postdoctoral Fellowship
2012-2014: CIHR Postdoctoral Fellowship
2007-2011: Alberta Ingenuity Studentship
Canadian Bioinformatics Workshops series (core faculty)
International Society for Computational Biology
Center for Host-Parasite Interactions
Microbiome and Disease Tolerance Center
Understanding important biological, environmental and nutritional factors on health and disease; developing computational solutions for high-throughput experiments.
Theme I: Host-Environment Interactions
Hygiene hypothesis: To understand the effects of helminths and gut microbiome in immunity and metabolism. Within the context, we are interested in elucidating the roles of small molecules (microRNAs and metabolites) in cross-species communications. The research is mainly based on developing novel algorithms and mining big data in public repositories.
Food and environmental exposure: To study the effects of food and environmental chemicals in health and diseases, using multiple omics technologies (metagenomics, epigenomics, RNAseq and metabolomics). The research is part of several ongoing large-scale team projects funded through CIHR and Genome Canada.
Theme II: Toxicogenomics & Systems Metabolomics
The recent advances in the development of automatic C. elegans tracking and image analysis system have provided a strong foundation for high-throughput phenotype screening at organism level. For molecular-level characterization, metabolomics has proven to be a powerful technology in both drug toxicity screening and environmental toxicology.
We are developing a systems biology framework coupling high-throughput C. elegans phenotype screening with downstream transcriptomics and metabolomics profiling for risk assessment and mechanistic understanding of the toxic effects of different chemicals of interest.
Theme III: Bioinformatics for Big Data Analytics
The main challenges in dealing with biological big data are more from its “complexity” rather than its size. My research focuses on integrating statistics, visualization and domain knowledge to help understand complex omics data through web and cloud platform.
Bioinformatics and statistics: The research involves developing new-generation bioinformatics software and statistical approaches to address practical data analysis challenges arising from several “omics fields” including metabolomics, transcriptomics, metagenomics and epigenomics.
Visual analytics and systems biology: A long-term interest in my laboratory is to develop new-generation computational frameworks that integrate high-performance computing and novel data visualization techniques, coupled with comprehensive knowledge base to facilitate systems-level understanding and hypothesis generation.