Unlocking the Power of Omics Data Sciences

Are you involved in the metabolomics, genomics, transcriptomics, or proteomics? Data analysis is an integral part of knowledge development and gaining insight into the complexities of the fields. TMIC node leader Dr. Jianguo (Jeff) Xia is an Associate professor at McGill University and focuses his work on developing cutting-edge computational frameworks. These frameworks incorporate cloud computing, machine learning, and visual analytics, aiming to streamline and enhance the analysis of complex biological data.

Dr. Xia joined TMIC as a node leader during the launch of TMIC 2.0. His web-platform, MetaboAnalyst, is globally recognized as an easy-to-use, comprehensive platform for metabolomics data analysis, visualization and interpretation. The platform gained the attention of more than 500,000 users and continues to attract metabolomic researchers across the world.

Undergraduates, graduate students, postdoctoral fellows, researchers, and PIs interested in opportunities surrounding omics data analysis are encouraged to join the upcoming educational sessions offered through Xia Lab Analytics. The sessions are conducted virtually and consist of the Summer Bootcamp (August 5 -9) and Regular Sessions (Sept. – Nov. 2024).

The session covers the following topics: transcriptomics, biological networks, metabolomics, microbiomics, and multi-omics. When discussing each topic, the session will dive into core concepts, key tools, hands-on demo, and practices. If you are interested in covering solely one specific topic, XiaLab Analytics also provides you with the opportunity to sign up for individual topic.

Researchers will learn how to streamline their data analysis processes using MetaboAnalyst and other cutting-edge platforms, saving time and improving accuracy in their research projects. This course also covers the integration of different types of omics data, enabling researchers to gain a more comprehensive understanding of biological systems and uncovering insights that may not be apparent from a single type of data. By leveraging the comprehensive training and resources provided in the course, researchers can significantly enhance the quality and impact of their research outcomes, leading to more robust and publishable results.

After taking the course, the participant will be able to

  1. Develop a good knowledge of the concepts and approaches for omics data analysis;
  2. Understand the subtleties or uniqueness of common omics data analysis workflows;
  3. Perform effective omics data analysis using our user-friendly omics tools (community or pro) through web interface
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