Using Microbial Diversity to Teach Computational Biology and Bioinformatics

Given that computational skills are central to many sub-disciplines in biology, we developed an undergraduate course called Computational Biology to better prepare students in this widely-applicable field. In this report, we have summarized available resources and original protocol for computational curriculum, all of which have applications beyond microbiology. We have also described specific microbial models that were uniquely selected and employed for class analysis. Using diverse microbial sequences and genomes, students navigated the National Center for Biotechnology Information (NCBI) with an emphasis on database structure, data annotations, effective database searching, understanding genome data archiving and display issues, and using analytical software to identify and rank similar sequences. Next, using original bacterial 16S rRNA sequences from our Red Layer Microbial Observatory project, students assembled and aligned multiple sequence datasets using several tools on the Biology Workbench (BW). Using resulting 16S rRNA alignments, students produced and statistically evaluated phylogenetic trees. Finally, students used a combination of software and data selected from NCBI and BW to analyze model microbial proteins, emphasizing how to view and analyze determined structure data, and how to predict protein structure using sequence information. Repeating all these methods, each student completed an original research project, comparing 20 homologous sequences to address a specific hypothesis of their own design. To complete this report, we summarized and discussed course impact and extensions. Introduction Over the last four years, we have developed and improved an undergraduate biology course called Computational Biology. A 300level, 4-credit course, Computational Biology requires two 1-hour lectures and two 2-hour computer-based labs each week. To promote exposure to different areas of computational biology, this course is team-taught by two instructors: During the first five weeks, B. Dutton has employed a plant systematics model to teach phylogenetic analysis using morphological traits. During the last five weeks, S. Boomer has employed a microbial diversity model to teach bioinformatics and molecular phylogenetics. The latter molecular microbiology-driven bioinformatics unit provides the focus of this paper. We have described a core set of computational methods and tools that provide resources for teaching not only bioinformatics but also important concepts in evolution, molecular biology, and biochemistry. To facilitate mastery of these methods and concepts, we have developed and summarized microbial diversity-based datasets and models. In implementing this curriculum, we have attempted to emphasize the science behind the programs and to stimulate interest in exploring and asking scientific questions through researchdriven models and exercises that use primary data and research papers. Although we hope other educators will find such models and curricula worthy of reproduction, we hope more than anything to inspire others to mine readily-accessible databases and develop equally motivating models for educating students using any number of model biological systems.