Update: these slides have been revised for content as of September 1, 2005. There are still many cosmetic glitches (e.g., odd font choices, improper use of boldface) and we will correct those in a future iteration. As usual, if you notice errors, feel free to report them to us or post them on the discussion boards.
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Quick links:
 Chapter 3
 Chapter 4
 Chapter 5
 Chapter 6
 Chapter 7
 Chapter 8
 Chapter 9
 Chapter 10
 Chapter 11
 Chapter 12
Molecular Biology (Ch 3)  
A comprehensive introduction to molecular biology. Note: This is an extremely large file, so please view the web version first to see if you only need a few slides. If you need the entire PowerPoint, please realize that you're downloading a 15 Megabyte file, and if you need the PDF that you're downloading a 63 Megabyte file! 
DNA Mapping (Ch 4)  
Describes DNA mapping from a biological perspective, then considers some computational problems like the Partial Digest Problem (PDP). 
Brute Force Motif Searching (Ch 4)  
Introduces motif concepts. Describes two strategies for finding them: brute force search in "starting position space" and brute force enumeration of "median strings space". 
Genome Rearrangements (Ch 5)  
Describes what genome rearrangements are. Considers a few bruteforce algorithms for solving the rearrangements problem, and also a greedy approach. Introduces approximation algorithms. 
Edit Distance (Ch 6)  
Introduces Dynamic Programming with the following problems.

Alignment (Ch 6)  
Describes alignment. Some of the material in this presentation covers material in the LCS and Edit Distance presentations as well. 
Multiple Alignment (Ch 6)  
Extends 2sequence alignment to 3sequence alignment, and shows why ksequence alignment is inefficient. Describes progressive multiple alignment through CLUSTALW, and how to score multiple alignments. 
RNA (Ch 6)  
Statistical methods for gene prediction (Ch 6)  
Describes the reasoning behind statistical methods for gene prediction. Covers inframe hexamer count, TestCode, and mentions two popular programs. 
Similaritybased methods for gene prediction (Ch 6)  
Describes and compares two methods for gene predition: exon chaining and spliced alignment (of proteins (or mRNAs) to DNA). Overviews five other popular tools: GenScan, GenomeScan, TwinScan, GenMark, Glimmer 
Linear Space Alignment (Ch 7)  
Describes the reduction of the quadraticspace requirement for naive DP algorithm into linearspace (by doubling computation time). 
Graphs and DNA Sequencing (Ch 8)  
Introduces the notion of a graph, and how it applies to the most important problem in biological data collection: DNA sequencing. 
Mass Spectrometry and Proteomics (Ch 8)  
Describes the background to mass spectrometry. Introduces the spectral convolution algorithm and the spectral alignment algorithm. 
Combinatorial Pattern Matching (Ch 9)  

Clustering (Ch 10)  

Molecular evolution (Ch 10)  
Principles of molecular evolution. 
Hidden Markov Models (Ch 11)  

Randomized Algorithms (Ch 12)  
