GENOMICS, PROTEOMICS, AND METABOLOMICS: A DEEP DIVE INTO OMICS DATA ANALYSIS
Advances in medicine are increasingly driven by "big data" analyses, including proteomics, genomics, and metabolomics. Basic knowledge of analyzing these datasets enables the generation and testing of hypotheses with the potential to transform a field. Provides foundational instruction in "omics" methodologies, heritability studies, and analytical techniques, offering the skills needed to conduct future 'omics analyses and apply scientific inquiry to advance medical practice.
Prerequisites & course information
Requisites: MED SC-M 810 , 811 , 812 and 813 ; or Declared in Cellular and Molecular Pathology Graduate Program Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Examine and critically evaluate databases for adequacy of data. Audience: Graduate 2. Demonstrate understanding of biological variability, data scaling, and data normalization schemes Audience: Graduate 3. Demonstrate a basic understanding of statistical associations using bioinformatics, such as hierarchical clustering and correlation matrices. Audience: Graduate 4. Demonstrate a basic understanding of analyte pathways using bioinformatics databases such as Uniprot. Audience: Graduate 5. Demonstrate appropriate consideration of false discovery. Audience: Graduate 6. Develop hypothetical models of molecular interactions. Audience: Graduate 7. Synthesize knowledge of molecular pathways and apply this knowledge to potential health and clinical factors. Audience: Graduate 8. Prepare and defend a hypothetical model of molecular interactions. Audience: Graduate 9. Demonstrate critical thinking skills and an understanding of the underlying science behind analytical methodologies Audience: Graduate Learning Outcomes: 1. Examine and critically evaluate databases for adequacy of data. Audience: Graduate 2. Demonstrate understanding of biological variability, data scaling, and data normalization schemes Audience: Graduate 3. Demonstrate a basic understanding of statistical associations using bioinformatics, such as hierarchical clustering and correlation matrices. Audience: Graduate 4. Demonstrate a basic understanding of analyte pathways using bioinformatics databases such as Uniprot. Audience: Graduate 5. Demonstrate appropriate consideration of false discovery. Audience: Graduate 6. Develop hypothetical models of molecular interactions. Audience: Graduate 7. Synthesize knowledge of molecular pathways and apply this knowledge to potential health and clinical factors. Audience: Graduate 8. Prepare and defend a hypothetical model of molecular interactions. Audience: Graduate 9. Demonstrate critical thinking skills and an understanding of the underlying science behind analytical methodologies Audience: Graduate
Sources
- Course catalogue ↗2026-2027