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Tuesday, July 15 • 11:30am - 12:00pm
DNA Subway: Making Genome Analysis Egalitarian

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DNA Subway bundles research-grade bioinformatics tools, high-performance computing, and databases into easy-to-use workflows. “Riding” on different lines, students can predict and annotate genes in up to 150kb of raw DNA sequence (Red Line), identify homologs in sequenced genomes (Yellow Line), identify species using DNA barcodes and construct phylogenetic trees (Blue Line), and examine RNA-Seq datasets for transcript abundance and differential expression (Green Line). With support for plant and animal genomes, DNA Subway engages students in their own learning, bringing to life key concepts in molecular biology, genetics, and evolution. Integrated DNA barcoding and RNA extraction wet-lab experiments support a variety of inquiry-based projects using student-generated data. Products of student research can be exported, published, and used in follow-up experiments. To date DNA Subway has over 8,000 registered users who have produced 44,000 projects.

Based on the popular Tuxedo Protocol, the Green Line was introduced in January 2014 as an easy-to-use workflow to analyze RNA-Seq datasets. The line uses iPlant’s APIs (http://agaveapi.co/) to access high performance compute resources of NSF’s Extreme Scientific and Engineering Discovery Environment (XSEDE), providing the first easy “on ramp” to biological supercomputing. We believe that the Green Line can make the computational analysis of whole genomes egalitarian – in the same way that gel electrophoresis opened up the biochemical analysis of individual genes.

We are currently upgrading the existing Red Line workflow to incorporate JBrowse and WebApollo, a user-friendly rewrite of the Apollo annotation editor. The Red Line will enable an easy annotation “round trip,” as locally generated RNA-Seq data is automatically transferred from the Green Line as biological evidence. The workflow will readily accept any type of GFF file – including output from MAKER and evidence from other genome resources. This seamless integration will create a “power desktop” that allows faculty and students to explore genome structure and large-scale variation in gene expression on their personal computers.

Tuesday July 15, 2014 11:30am - 12:00pm EDT

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