Evaluating peer-to-peer bioinformatics education: a case study of student learning outcomes and community impact in an undergraduate multi-omic data analysis course

Computational methodology has become ubiquitous in biomedical research with the rise of big data analysis and popularity of artificial intelligence and machine learning. However, undergraduate bioinformatics education has largely struggled to keep pace with the demand for bioinformatics skills, due to a combination of social and resource-based barriers. In this case study, we discuss the application and outcomes of Multi-Omic Data Analysis, a peer-to-peer learning-based undergraduate bioinformatics course offered by the Department of Quantitative and Computational Biology at the University of Southern California. Over eight semesters, a cohort of student instructors taught 2-3 weekly lectures to 107 undergraduate students in the Quantitative Biology Bachelor of Science degree program. Lectures covered a range of topics, including R and Python data analysis, scientific communication, and general research readiness as undergraduate students. We find that bioinformatics education courses structured around peer-to-peer learning have great potential to overcome many of the obstacles to comprehensive undergraduate bioinformatics education, and provide additional benefits related to student cohesion and community. We further discuss the longevity and feasibility of such courses, both specific to our program and in undergraduate universities at large.