Development and Integration of Next Generation Sequencing (NGS) Active Learning Modules into Undergraduate Curriculum

Undergraduate students learn about Next Generation Sequencing (NGS) platforms in courses, but often have difficulty understanding the impact of these techniques without hands-on experience analyzing actual NGS data. Here I describe a classroom-tested set of course-embedded activities focusing on RNA-sequencing (RNA-seq) data analysis using a combination of bioinformatics and wet lab workflows tailored for implementation into diverse undergraduate classroom settings. These modular workflows can be applied to a variety of novel or publicly available RNA-seq datasets.

To create an undergraduate active learning research experience focusing on NGS that complements my research program, I developed an RNA-seq dataset to identify differentially expressed genes between embryonic day 8 (E8) and E18 chicken retinas. Computational and wet lab modules analyzing this RNA-seq dataset were implemented into an upper level undergraduate Advanced Molecular Biology course. Computational modules investigated raw sequencing quality, sequence mapping to a reference genome, and pathway analysis of candidate genes. These modules were used in conjunction with wet lab activities constructing cDNA libraries from total RNA followed by quantitative reverse transcriptase PCR (qRT-PCR) using student-designed and validated qPCR primers. Downloadable lab modules and lesson plans for this and other courses are publicly available to educators via the “RNA-seq for the Next Generation” website developed by the Cold Spring Harbor Laboratory DNA Learning Center as well as my personal James Madison University SelectedWorks faculty website (https://works.bepress.com/raymond_enke/).