Continuation of the discussion of how to efficiently
compute the similarity of two sequences. Introduction
to the traceback operation to find the
optimal alignment.

Lecture 6: Computing similarity using an alignment graph

This course covers fundamental algorithms for efficient analysis of biological sequences and for building evolutionary trees. This is an undergraduate course focusing on the ideas and concepts behind the most central algorithms in biological sequence analysis. Dynamic Programming, Alignment, Hidden Markov Models, Statistical Analysis are emphasized.

Some suggestions of where the student can get more
exposure to algorithms for bioinformatics and computational biology.

Postscript: Where to go next

Building evolutionary trees from sequence data. The Maximum Parsimony criteria, the special case of Perfect Phylogeny, and the Fitch-Hartigon dynamic program to minimize mutations when the tree and a sequence alignment are known.

Lecture 30: Maximum Parsimony and minimum mutation methods

Additive trees and their construction. The Neighbor-Joining algorithm and its use with near-additive data.  Bootstrap values and their misuse.

Lecture 29; Additive trees and the Neighbor-Joining algorithm

Algorithms for constructing an Ultrametric Tree from an Ultrametric Matrix, and the relationship of ultrametrics to the molecular clock.

Lecture 28: Algorithms for Ultrametric trees — molecular clocks

lntroduction to trees that represent evolution. We start with the case of perfect data: the Ultrametric tree case.

Lecture 6: Computing similarity using an alignment graph

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