Developed by Dr. Anton E. Weisstein (Truman State University) and Mingchao Xie (TA for Bio 4342), this lecture and exercise introduce students to the core algorithm (dynamic programming) used by many sequence alignment tools (e.g., BLAST). The exercise includes a spreadsheet with a dynamic programming matrix that allows students to explore the impact of different types of alignments (i.e. global, semiglobal, and local) and scoring systems on the resulting sequence alignment.
Developed by Dr. Anton E. Weisstein (Truman State University), Zongtai Qi and Zane Goodwin (TAs for Bio 4342), this curriculum introduces students to the idea of Hidden Markov Models (HMM) that forms the core component of most gene predictors. The lecture by Zongtai Qi uses weather prediction to illustrate the key concepts of the HMM, whereas the lecture by Zane Goodwin focuses on the HMM that models a splice donor site. The exercise developed by Dr. Weisstein includes a spreadsheet with a simple HMM exercise that models a splice donor site. The spreadsheet allows students to examine the impact of different transition and emission probabilities on splice site predictions. A video recording of Dr. Weisstein’s HMM presentation during the 2014 GEP Alumni Workshop is also available online. This HMM curriculum is also available on CourseSource.