title : Using Microbial Diversity to Teach Computational Biology and Bioinformatics
author: Sarah M. Boomer1*, Daniel P. Lodge2, Kelly Shipley1, Bryan E. Dutton1
year:
place of pulbish :
1Western Oregon University, Department of Biology, Monmouth, OR 97361
2Oregon State University, Department of Engineering, Corvallis, OR 97331
abstract :
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.
