Large Dataset Analysis for Undergraduates: Discovery Environment, RNA-Seq and Leaf Senescence in Arabidopsis thaliana (Green Line)

Undergraduates learn about Next Generation Sequencing techniques in courses, but have difficulty understanding the impact of data generated by NGS without first-hand experience.  Most molecular biology students do not have programming language experience, but the Discovery Environment (DE) within iPlant Collaborative allows undergraduate students to run a GUI RNA-seq data analysis pipeline.  Datasets collected at various times during the leaf senescence were uploaded to DE.  Leaf senescence is an agriculturally important developmental transition which results in the transport of leaf nitrogen into developing grains and fruit.  Pairs of students were provided with RNA-seq fastq files from two different time points of leaf senescence. Pipeline steps included quality filtering, Tophat alignment and CuffDiff read quantitation. Gene lists were filtered for fold-induction and significance parameters within excel and biological process enrichment was then performed using DAVID and BiNGO.  For wet lab experience, students picked genes from enriched biological processes with high FPKM values for expression confirmation in a biological replicate using SYBR green real-time qPCR.  This course was run successfully for the first time in 2014, and numerous difficult-to-understand concepts were identified and will be incorporated into publicly-available lesson plans via the”RNA-seq for the Next Generation” web site being developed by the DNA Learning Center.