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 an 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 also cover multiple specialized topics such as single cell genomics, machine learning and genome editing technologies that are now impacting health outcome significantly. 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 practical application of machine learning in genomics. CRISPR/Cas9.
This course will teach the application of bioinformatics to manipulate and analyze large scale dataset using established bioinformatics tools. The bioinformatics module will also provide an overview of approaches and techniques for clinical application. The implementation of genomics data analytical pipelines for diagnostic laboratory will be covered. In addition there will be an introduction to different OMICs technologies and approaches, and strategies to prioritize the pathogenicity of variants. It is also critical for the students to know programming languages (i.e. java, perl etc.) in order to custom design analysis that will aid their graduate research program. Lectures and through the use of simulations, the application of programming languages in genomics will be covered.