Training Students in Analyzing "Big Data": Response of Maize Seedlings to Cold

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 quantitative trait loci (QTL) mapping and 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 stages 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.  Students utilize recombinant inbred lines (RILs) to conduct genetic mapping of loci responsible for differences in response to cold and analyze a large RNA-seq dataset to explore regulation of global genome expression in response to cold stress.  We developed curriculum materials (worksheets and case studies) that guide students through QTL and RNA-Seq data analyses 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.