Temple University 1 credit BIOL
We are in the midst of a genomics revolution, with RNA sequencing (RNA-seq) data at its core and computational biology as its key tool. RNA-seq is an inexpensive and fast way to quantify gene expression and annotate genes and genomes in a high-throughput fashion. This independent research course uses a studio-based model to teach students how to analyze RNA-seq data with the Galaxy platform. Students will team up with faculty from the main campus, the Lewis Katz School of Medicine, and Fox Chase Cancer Center to analyze real-world datasets, gaining hands-on experience in RNA-seq workflows, data interpretation, and communication. The course emphasizes practical skills using Galaxy, a "no-code" platform that allows complex analyses without programming. Galaxy provides a user-friendly framework for turning raw biological data into meaningful insights through "bioinformatics pipelines" allowing students to explore a range of biological questions, analyze genome-wide expression levels, and provide functional annotations. Students also have the choice to run their pipelines in parallel via code (R or python). This class is part of Temple's Bioinformatics Studio, a community of students interested in computational research.
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