Apr - Jianguo (Jeff) Xia¶
Speaker: Jianguo (Jeff) Xia
Talk Title: Towards principled approaches for multi-omics integrations
Wednesday, April 14th, 2021 11:00am ~ 12:30 pm (Pacific Time)
Virtually on Zoom.
ID: 619 3068 4501
Affiliation: Associate Professor and Canada Research Chair, Institute of Parasitology and Department of Animal Science, University of McGill
Dr. Xia is an Associate Professor and Canada Research Chair (bioinformatics and big data analytics) at McGill University, Quebec Canada. His research explores innovative and practical ways to address the current challenges in big data analytics arising from biomedical and environmental research, focusing on metabolomics, transcriptomics, microbiomics and multi-omics integration. His group is actively developing new-generation computational framework integrating cloud computing, machine learning and visual analytics to enable intuitive and high-throughput data analysis. Many of the tools are broadly used by researchers worldwide. To date, Dr. Xia has authored >75 journal publications and 8 book chapters. Since 2019, he has been ranked as Global Highly Cited Researchers (citations: >20,000, H-index: 41).
Multi-omics studies promise to provide more holistic pictures of the underlying diseases or biological processes. However, how to obtain such pictures remains to be elucidated. A wide variety of concepts and approaches have been proposed and implemented over the past decade. How to navigate such a complex landscape of multi-omics integration has become a significant challenge for most researchers, including many practitioners in computational biology and bioinformatics. In this talk, I will present our journey over the past three years, comment on conceptual advances, and share practical tips regarding the current practices in multi-omics integration. Finally, I will introduce our platforms that allow intuitive, exploratory analysis of multi-omics datasets.
Introductory Speaker: Fatih Karaoglanoglu (Dr. Hach’s lab, UBC)
Talk Title: Genion, an accurate tool to detect gene fusion from long transcriptomics reads