Oasis, The Online Abstract Submission System

Control/Tracking Number : 04-GM-A-4604-ASM
Activity :Abstract
Current Date/Time : 12/19/2003 5:49:38 PM

A Bioinformatics Course Emphasizing Molecular Microbial Diversity

D. P. Lodge, S. M. Boomer, B. E. Dutton;
Western Oregon University, Monmouth, OR.

Given that bioinformatics is central to many sub-disciplines in biology, we developed an undergraduate course in Computational Biology (Biology 301) to better prepare students in this widely-applicable field. The first five weeks of this course, taught by Dr. Dutton, covers phylogenetics using a research-driven plant systematics project that emphasizes morphological traits. The second five weeks (the focus of this presentation) emphasizes bioinformatics using original bacterial 16S rRNA data from our Red Layer Microbial Observatory (RLMO). During week 1, students navigate the National Center for Bioinformatics Information (NCBI) databases, retrieve 16S data, and use BLAST (Basic Local Alignment Search Tool) to analyze RLMO-derived 16S data. During week 2, students use the Biology Workbench (http://workbench.sdsc.edu/) to perform multiple sequence alignment, comparing alignments with known 16S structures from the Comparative RNA Website (http://www.rna.icmb.utexas.edu). During week 3, students learn concepts in molecular phylogenetics. During week 4, students explore protein structure prediction methods using the Biology Workbench. Extending our theme of molecular microbial diversity, we use two protein models for lab exercises: the multidrug efflux pump from E. coli and Influenza Neuraminidase. During week 5, students use NCBI to explore microbial genomes and discuss original research articles. Students are assessed using in-class exercise worksheets, lecture- and lab-based examinations, and an individual project. Individual projects require students to research a protein-encoding gene and perform two types of analysis: (1) phylogenetic comparison of similar genes from multiple sources using both nucleotide and protein information; and (2) predict protein structure using sequence information and compare results with published data about the protein. After twice teaching this course, we found that a list of gene options (viral, bacterial, plant, and mammalian) and emphasizing BLAST-driven dataset selection avoids frustration and phylogenetic errors (e.g. comparing non-homologous genes).

Topic (Complete): W03 Using Inquiry to Foster Learning in Microbiology for Major and Non-Majors
Keyword (Complete): Bioinformatics ; Education ; molecular microbiology
Membership and Grant Information (Complete):
ASM Member (or who has submitted an application): : Sarah Boomer

Status: Complete

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