Our material on phylogenetics in bioinformatics was roughly divided into five "units". Some of the topics that you should especially focus on, with one or a few goals or questions follow each section.

1. concepts of trees and inferences based on trees

- trees as hypotheses of evolutionary history and shared ancestory

- HOMOPLASY: convergence, parallelism, reversal

- gene trees I: orthology, paralogy

- monophyly

- inference of ancestral states using ACCTRAN

* be able to "read" a phylogenetic tree, and draw correct inferences about the monophyly of groups of organisms or sequences

* given a tree and a set of data for a given character, be able to infer the ancestral states of the character using the method of ACCTRAN

2. methods of building phylogenetic trees

- parsimony, distance, likelihood compared and contrasted

- the basic approaches, similarities and differences

- standard (nonparametric) bootstrap in phylogenies: use and interpretation

- strengths and weaknesses of each of the major methods

- PHYLIP as an intro to computer programs for phylogeny

* be able to perform and interpret a small parsimony analysis by hand, as we did in class, or using any of the main approaches including boostrap, with PHYLIP

3. distance models of sequence evolution

* contrast the different distance models for sequence (or protein) evolution. What are some advantages and disadvantages?

4. maximum likelihood as a general tool for hypothesis testing

* what is the likelihood ratio test and how is it used to test a wide variety of possible hypotheses about sequence evolution, such as: rates of evolution, monophyly of group or sequences, similarity of branching history of two trees, etc.

* be able to outline or diagram the goals and basic steps in a parametric bootstrap analysis, and it's use in hypothesis testing in sequence studies.

5. further concepts and their application

- gene families II, reconciled gene trees

- long branch attraction conditions, causes

* The example given in class of phylogenetic analyses of invertebrate animals was a good example of a dataset where different methods gave different results, but exploring the different results led to a better understanding of the history of the sequences. What were some "take home lessons" to be gained from this example?