Sep - Tao Huan¶
Speaker: Tao Huan
Talk Title: Addressing Big Data Challenges in Mass Spectrometry-Based Metabolomics
Event Details
Date/Time:
Wednesday, Sept 22nd, 2021 11:00am ~ 12:30 pm (Pacific Time)
Location:
Virtually on Zoom.
Affiliation: Assistant Professor, Department of Chemistry, University of British Columbia
Bio:
Dr. 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 labelling -based metabolomics. After graduation, Dr. Huan did postdoctoral work with Dr. Gary Siuzdak at the Scripps Research Institute (La Jolla, CA) to bring metabolomics into 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. 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 56 peer-reviewed publications in high-impact journals, including Nature Methods, Nature Protocols, and Analytical Chemistry with over 1900 citations and an h-index of 22. Dr. Huan is currently a steering committee and faculty member of UBC Social Exposome Cluster. In addition, Dr. Huan is affiliated faculty members in the Graduate Program in Bioinformatics, Genome Science and Technology (GSAT) program, and Djavad Mowafaghian Centre for Brain Health.
Abstract:
Metabolomics is an emerging field of research in the post genomic era of biology. Among the various analytical tools used in metabolomics, mass spectrometry (MS) is the key technique owing to its high sensitivity, specificity, and throughput. This presentation will focus on the latest bioinformatic developments in the Huan lab to address big data challenges in MS-based metabolomics. In particular, I will introduce a recently discovered phenomena of ratio bias and fold-change compression in the linear electrospray ionization (ESI) regions. In addition, I will talk about recently developed deep learning algorithms, which recognize high quality metabolic features and extract bioactive steroids from untargeted metabolomics data. Finally, I will present a novel core-structure-based spectral similarity algorithm, which has been demonstrated to better reveal chemical structural similarities using their spectral similarities.
Introductory Speaker: Shreya Bendre, Benard-Lin lab, BC Cancer Research Centre
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