Project Summary The advent of high-throughput genomics and associated data science technologies has enabled genome-based decisions to improve human personal and public health. The advance of genomic medicine relies on developing a diverse workforce in computational genomics and data science (CGDS). Critical gaps, however, hamper the expansion of the genomics workforce: (1) a lack of diversity continues despite decades of effort. Large sectors of the US population remain underrepresented in CGDS; (2) a lack of access to resources and training opportunities limits student populations at the undergraduate and master’s levels to obtain the knowledge and skills needed for a career in CGDS. The University of Texas at San Antonio (UTSA) is uniquely positioned to address these gaps. Being a primarily minority-serving institution, with 67% of enrollment from under-represented minority (URM) groups, UTSA is committed to promoting an inclusive community of learners and narrowing the nationwide gender and racial gaps in the STEM field. In addition, UTSA has identified biomedical science and data science as fundamental building blocks for developing its research base. CGDS, a discipline at the interface of biomedical and data science, is a priority area receiving significant institutional support. This proposed UE5 program aims to develop, implement, and evaluate classroom educational content and cloud-based hands-on analytical exercises in CGDS to serve students from diverse backgrounds, including those underrepresented in the genomics workforce. Aim 1. To develop cloud-based instruction materials utilizing existing NIH cloud resources for teaching CGDS at undergraduate and master’s levels. These materials include lecture slides, video presentations, demonstrations, hands-on practice problems, assignments, and project ideas, organized into flexible modules suiting the needs of diverse student backgrounds and learning paths. Aim 2. To iteratively refine the developed content and modules by teaching them in a hybrid mode across different departments and collecting feedback from students and faculty. Aim 3. To evaluate the effectiveness of these materials by conducting formal evaluation and analysis and sharing with the broader CGDS community at large.

Project Narrative The proposed research is relevant to public health because it entails new strategies to teach computational genomics and data science to a broader community, in particular under-represented minority students. Thus, the proposed work will enhance the diversity in the genomics workforce, and advance the likelihood that underserved or health disparity populations participate in, and benefit from health research and genomic medicine.