This course will introduce a solid foundation of genomic landscape and its complexities. This course will provide  an in-depth overview of DNA sequencing technologies, OMICs (transcriptomics/ proteomics) data analysis,  genome editing techniques, single-cell genomics, actionable pharmacogenetic tests, genetic counseling and clinical application of bioinformatics resources. The expectation is that students will develop in-depth knowledge and critical thinking ability regarding the applications of genomics in healthcare delivery and research. Using a diverse set of teaching modalities such as lectures, critical appraisal of research articles and simulations, this course will allow students to conduct productive research in their graduate program.

The course will cover multiple specialized topics such as single-cell genomics, machine learning and genome editing technologies that are now impacting molecular research outcome and medicine. Single-cell genomics will be taught to differentiate cell types and cellular heterogeneity. Machine learning is a topic that is significantly impacting big data driven research and this course will show the practical application of machine learning in genomics. CRISPR/Cas9 model system detail will be introduced within the context of conditional experiments to characterize genetic mutations. This course will teach the application of bioinformatics to manipulate and analyze large scale datasets using established bioinformatics tools and programming languages. The bioinformatics module will also provide an overview of approaches and techniques for clinical application.