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Generating Multiple Sequence Alignments with ClustalW

Dr. Susan Parrish (McDaniel College) developed a basic lecture and weblem exercise (found at the end of the lecture) on using ClustalW to generate multiple sequence alignments, phylograms, and cladograms. This lecture and exercise are given prior to beginning the GEP annotation projects. Students who submit their GEP annotation projects early are then asked to generate multiple sequence alignments and phylograms of the putative proteins encoded within their assigned contig or fosmid, compared to those related proteins encoded by other Drosophila species of interest to the GEP.

Generating Multiple Sequence Alignments with Clustal Omega

Developed by Dr. Susan Parrish (McDaniel College), this PowerPoint presentation describes how Clustal Omega can be used to produce multiple sequence alignments. The multiple sequence alignments of the nucleotide sequences surrounding the transcription start sites can be used to identify core promoter motifs, while the multiple sequence alignments of protein sequences can be used to identify conserved domains. The presentation also includes a discussion of two strategies (UPGMA and Neighbor Joining) that are often used to construct phylogenetic trees.

Overview of Multiple Sequence Alignment Algorithms

Developed by Yu He (TA for Bio 4342), this PowerPoint presentation provides a basic overview of the common algorithms used to generate multiple sequence alignments. The presentation also illustrates how one could use Clustal Omega to generate a multiple sequence alignment for a set of orthologous proteins in order to identify conserved domains within the protein.

RNA Quantitation from RNA-Seq Data

Developed by Dr. Jeremy Buhler, this PowerPoint presentation provides an overview of the approaches for quantifying transcript abundance based on RNA-Seq data. The presentation includes a discussion on the benefits and limitations of the two approaches commonly used for RNA quantitation – RPKM and TPM.

Behavior and Limitations of Motif Finding

Developed by Dr. Jeremy Buhler, this exercise uses MEME to discover putative regulatory motifs in a collection of D. melanogaster promoter sequences. It also illustrates some of the challenges associated with motif finding and the limitations of motif finding programs.

Introduction to Motifs and Motif Finding

This document contains the notes from a lecture on motif finding given by Dr. Jeremy Buhler in the Bio 4342 course at WU. The lecture covers the different approaches used to represent sequence motifs and to search for sequence motifs in a genome.

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.