RNAseq – an entryway to Undergraduate Big Data Analysis:
A Simple HPC Workflow for RNA-Seq in the Classroom
Nash, E. Bruce, Spector M., Williams, J., Ghiban, C., Lauter, S., Yang, C., and Micklos, D.
In the last decade, “next-generation” sequencing (NGS) methods have decreased DNA sequencing costs by 10,000-fold. This means that undergraduate biology students could potential work with the same data and tools as high-level researchers. Surveys by the Cyverse (formerly iPlant Collaborative), an NSF-funded national cyberinfrastructure for biology, reveal that 95% of graduate students and researchers say they are currently, or soon will be, using large sequence datasets. However, two-thirds have little or no experience with bioinformatics and only a third have access to sufficient computational resources needed to analyze these data.
We have created an extensible infrastructure and training program that enables faculty to integrate NGS into undergraduate instruction. Free access to these tools democratizes NGS, allowing faculty at any undergraduate institution to engage their students in cutting-edge biological research.
Many biology faculty are also attempting to scale research projects into course-based undergraduate research experiences (CUREs). To allow NGS CURES, we developed the Green Line of DNA Subway (http://www.dnasubway.org) as a user-friendly graphical interface that allows access to compute resources of NSF’s Extreme Scientific and Engineering Discovery Environment (XSEDE) to perform computationally intense RNA-Seq analysis through a web browser.
The project focuses on RNA-Seq as a tractable whole genome analysis for those new to NGS. The Green Line supports analysis of transcriptomes to measure gene expression or novel transcripts.. Collaborating with a faculty Working Group, we developed week-long workshops to introduce RNA-Seq to faculty. These workshops are now being adapted into online training materials.
Details of the program, online tools, and the results and effects of implementation in CURES and independant student research will be presented.