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Advanced Student

Using FlyBase RNA-Seq Tools to Investigate Gene Expression Profiles

This walkthrough illustrates how GBrowse and the RNA-Seq tools at FlyBase can be used to identify genes with similar expression patterns. This walkthrough provides an introduction to the TopoView RNA-Seq track in FlyBase GBrowse, the FlyBase Expression Profile Search tool, and the Expression Similarity Search tool. It also illustrates how the Gene2Function web site can be used to infer the function of a D. melanogaster gene based on the functions of its orthologs in other model organisms.

Eukaryotic Comparative Genomics

This lecture from Dr. Barak Cohen discusses the major factors to consider (e.g., species to analyze, model for conserved sequences) when using phylogenetic footprinting to identify conserved regions across multiple species. The lecture also illustrates how the weight matrix model can be used to describe sequence motifs, and it provides an introduction to Magma, which uses the multiple species with multiple genes approach to discover conserved motifs.

From Smith-Waterman to BLAST

This lecture from Dr. Jeremy Buhler discusses the limitations of the Smith-Waterman local alignment algorithm and the heuristics used by the BLAST program in order to reduce the search space and to quickly produce high-scoring local alignments.

Introduction to Dynamic Programming

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.

An Introduction to Hidden Markov Models

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.
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