Title: Addressing Big Data Challenges in Clinical Metabolomics

Speaker: Dr. Tao Huan

Moderator: Dr. Fang Wu

Dr. Tao Huan is an Assistant Professor in the Department of Chemistry at the University of British Columbia. He received his Ph.D. in Analytical Chemistry from the University of Alberta under the supervision of Dr. Liang Li on developing chemical isotope labeling liquid chromatography-mass spectrometry-based metabolomics. After graduation, Dr. Huan did postdoctoral work with Dr. Gary Siuzdak at the Scripps Research Institute (La Jolla, CA) to create metabolomics guided systems biology for an in-depth understanding of disease mechanisms. In July 2018, Dr. Huan was hired as an Assistant Professor in the Department of Chemistry at the University of British Columbia (UBC). At UBC, Dr. Huan’s research focuses on the synergistic development of analytical and bioinformatic methods for mass spectrometry-based metabolomics. Dr. Huan has published 63 peer-reviewed publications in high-impact journals, including Nature Methods, Nature Protocols, Nature Chemical Biology, Cell Metabolism, and Analytical Chemistry, with over 2520 citations and an h-index of 23. Dr. Huan is currently a steering committee and faculty member of the UBC Social Exposome Cluster. In addition, Dr. Huan is an affiliated faculty member in the Graduate Program in Bioinformatics, Genome Science and Technology (GSAT) program, and Djavad Mowafaghian Centre for Brain Health.

Highlights

• The latest analytical and bioinformatic developments in Dr. Huan's lab to improve the analytical accuracy, precision, and sensitivity for MS based metabolomics and its applications in clinical chemistry.

• Introducing recently discovered phenomena of ratio bias and foldchange compression in the linear electrospray ionization (ESI) regions Presenting several machine learning strategies to automatically recognize true metabolic features with good chromatographic peak shapes and clean tandem MS spectra for better metabolite annotation.

• Discussing a novel core-structure-based spectral similarity algorithm, which has been demonstrated to better reveal chemical structural similarities using their spectral similarities.