Recent progress in high-throughput experimental assays and integrative computational methods that enable the collection and analysis of genome-wide datasets have led to changes in the skill sets that are required in the biological and medical sciences. In biology, many exciting basic research questions about cellular function are being answered by analysis of multiple genomes, transcriptomes or epigenomes generated by large-scale coordinated efforts such as the 1000 Genomes Project, The Encyclopedia of DNA Elements and The Roadmap Epigenomics Project. In medicine, data-intensive methods are now routinely used to interpret personal genomes of patients, electronic medical records, results of new imaging methods and physiological tracking. Given these disciplinary changes, it is important that our undergraduate curriculum in Human Biology and related fields prepare students with computational and analytical skills for working with large biological datasets. Our proposed project brings together instructors from Computer Science/Genetics and Human Biology to develop an introductory inter-disciplinary computational biology course with an active-learning format. The course would provide an opportunity for students with minimal programming background to gain hands-on experience working with diverse biological data and to appreciate the applications of computational approaches to biology and medicine. The course would also help prepare students to pursue upper division courses at the biology-computer science interface.
Vice Provost for Teaching & Learning