Training Students in Analyzing Big Data: A Case of Plant Stress Response

Although many biology fields rely on analysis of large data sets, most undergraduate students consistently show the lack of skills in analysis, graphical presentation, and interpretation of large data sets. To introduce “big data” into standard biology curriculum, we developed, implemented, and assessed a series of laboratory exercises on RNA-seq data analysis that provides students with authentic research experiences. Maize is a thermophilic plant species that is highly sensitive to low temperature at all stage of development. Understanding how maize plants respond to cold and discovering genes responsible for cold resistance is important for developing novel cold resistant maize varieties. We created a large RNA-seq dataset that includes several maize inbreds subjected to various environmental stresses, such as cold, heat, high salt, and UV exposure. This data set can be interrogated to answer a variety of questions. We developed curriculum materials (worksheets and case studies) that guide students through RNA-Seq data analysis and prepare them to ask and answer their own questions using the RNA- seq dataset elucidating stress response in mazie. Follow up implementations and extensions of this project will be discussed as well as the data on assessment of student learning and their evaluation of research experiences provided by this project.