Jianguo (Jeff) Xia

Image by Alex Tran.

Associate Professor - Large Data Analysis

Canada Research Chair (Tier 2) Bioinformatics and Big Data Analytics

(Cross-appointed with the Institute of Parasitology)

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)

Short Bio

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. He joined McGill University as an Assistant Professor in 2015, and has become an Associate Professor since 2020. His lab focuses on leveraging bioinformatics, metabolomics and systems biology to study the effects of biological (i.e. gut microbiome and helminths) and environmental factors on health and disease.

Awards and Recognitions

  • 2019 - current: Global Highly Cited Researchers
  • 2019: McGill Principal’s Prize for Outstanding Emerging Researchers
  • 2016-2018: FRQNT New University Researcher Award
  • 2012-2014: Killam Postdoctoral Fellowship
  • 2012-2014: CIHR Postdoctoral Fellowship
  • 2007-2011: Alberta Ingenuity Studentship

Active Affiliations

Canadian Bioinformatics Workshops series (core faculty)
International Society for Computational Biology
Center for Host-Parasite Interactions
Microbiome and Disease Tolerance Center
Metabolomics Society

Research Interests

Understanding important biological, environmental and nutritional factors on health and disease; developing computational solutions for high-throughput experiments.

Current Research

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.


BINF 531 Statistical Bioinformatics 3 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer

BTEC 501 Bioinformatics 3 Credits
    Offered in the:
  • Fall
  • Winter
  • Summer


View a list of current publications on Google Scholar

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