Course Dates: February 12-23, 2024, 2pm – 5pm EST (Class meets Monday through Friday)
Where: Zoom or In-Person at Icahn Mount Sinai
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
Dinesh Barupal, PhD
Director, Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL-ME)
Assistant Professor, Department of Environmental Medicine and Public Health (EMPH)
Icahn School of Medicine at Mount Sinai
Questions? Please contact Dr. Barupal at firstname.lastname@example.org.