Course Dates: July 15 to July 26, 2024 from 2pm to 5pm EST (Class meets Mon through Fri)
Where: Zoom
Course Fee: $2,000
Description: The Computational Metabolomics for Clinical Research course will cover innovative software, online tools and knowledge bases to analyze metabolomics datasets. Metabolomics enables discoveries of metabolic mechanisms and predictive biomarkers that can be translated into new prevention and treatment strategies for human diseases. Metabolomics data for human specimens (blood, tissue, urine, saliva, etc) are mainly collected using a liquid or gas chromatography connected to a high-resolution mass spectrometry (HRMS) instrument. Signals for a few thousand chemical compounds can be observed in such data collected for specimens from human studies. Sample sizes in these studies are often >100. Consequently, these datasets are large and complex, and need to be processed using specialized computational approaches to extract, annotate, analyze and interpret them in the context of human metabolic systems. These approaches include signal processing, chemoinformatics, biostatistics, bioinformatics, data visualization and knowledge integration. This two-week long course will cover critical discussions, mentored-practices and homework exercises on how to prepare, analyze and report metabolomics datasets.
Targeted trainees: Researchers utilizing raw GC/LC HRMS data files or processed data matrices.
Programming language requirement : Basic R
Computer requirements : Two monitors (one for R-scripting and another for attending the Zoom session). Each participant will receive access to an online server for two months to practice the computational workflows.
Course Director
Dinesh Barupal, PhD
Director, Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL-ME)
Associate Professor, Department of Environmental Medicine and Climate Change
Icahn School of Medicine at Mount Sinai