Phylogenetics: Home
From EEBedia
| This article is still under construction. Expect it to change frequently until this notice is removed. |
| EEB 349: Phylogenetics |
| Lectures: MW 11-12:15 (CUE 320) Lab: M 1-3 (TLS 477) Lecture Instructor: Paul O. Lewis Lab Instructor: Maxi Polihronakis |
[edit] Lecture Topics
The following syllabus is tentative and probably will change without notice numerous times during the semester. Also, the content of linked presentations may change as well (so if you intend to print out lectures before class, do so as late as possible). Changes made after lectures are given will primarily reflect correction of typographical errors. All content linked to this page is copyright © 2007 by Paul O. Lewis.
[edit] Wed., Jan. 17: Significance, history, terminology
Introduction
Building a tree from a table of splits
[edit] Mon., Jan. 22: Rooted/unrooted, ultrametric/free, paralogy/orthology, lineage sorting, "basal" lineages
- Tree Thinking
Nexus File Format
[edit] Wed., Jan. 24: Camin-Sokal, Wagner, Fitch, Dollo, transversion, generalized, step-matrix
- Consensus trees
- Parsimony
- Calculating parsimony scores
[edit] Mon., Jan. 29: Exhaustive, branch-and-bound, star decomposition, stepwise addition, heuristic, genetic algorithms
- Polyphyly vs. Paraphyly Revisited
- Searching
- Parsimony and searching
[edit] Wed., Jan. 31: Least squares criterion, minimum evolution criterion, split decomposition, quartet puzzling, NJ
- Distances
- Calculating sum of squares and split decomposition
[edit] Mon., Feb. 5: Transition probability, instantaneous rates, JC69, K2P, F81, F84, HKY85 and GTR models
- Substitution models
- Distance methods
[edit] Wed., Feb. 7: Likelihood of a DNA sequence, parameter estimation (MLEs), likelihood ratio tests
- Maximum likelihood
- Calculating the likelihood
[edit] Mon., Feb. 12: Proportion of invariable sites, discrete gamma, site-specific rates
- Rate heterogeneity
- Addendum
- Likelihood methods
[edit] Wed., Feb. 14: *** Snow day: no class today ***
- Calculating the likelihood under invariable sites model
[edit] Mon., Feb. 19: Compensatory substitutions, RNA stem models, nonsynon. vs. synon. rates, codon models
- Secondary structure (stem) models
- Codon models
- Using the UConn Bioinformatics Facility cluster
[edit] Wed., Feb. 21: Nonparametric bootstrapping, Bremer support, KH, SH and SOWH tests
- Nonparametric bootstrapping
- Bremer support
- Topology tests
[edit] Mon., Feb. 26: Statistical consistency, long branch attraction, long branch repulsion
- Long branch attraction
- Hy-Phy lab
[edit] Wed., Feb. 28: Molecular clock, branch-specific rate shifts, ILD test for combinability
- Likelihood ratio tests
- Data partitioning
[edit] Mon., Mar. 5: *** Spring break ***
[edit] Wed., Mar. 7: *** Spring break ***
[edit] Mon., Mar. 12: Conditional/joint probabilities, Bayes rule, posterior distribution, probability densities
- Bayes primer
[edit] Wed., Mar. 14: MCMC, slice sampling, heated chains
- Bayesian phylogenetics
[edit] Mon., Mar. 19: Summarizing posterior distributions, partitioned data
- Bayesian phylogenetics (cont.)
- MrBayes lab
[edit] Wed., Mar. 21: Reversible-jump MCMC, mixture models, star tree paradox
- Bayesian phylogenetics (cont.)
[edit] Mon., Mar. 26: LRT, AIC, BIC, Bayes factors, posterior predictive approaches
- Model selection
- Phycas lab
[edit] Wed., Mar. 28: Parsimony, ML and empirical Bayes ancestral state reconstruction
- Ancestral character states
[edit] Mon., Apr. 2: Independent contrasts
- Continuous Character Correlation
[edit] Wed., Apr. 4: Pagel's likelihood ratio test
- Discrete Character Correlation
[edit] Mon., Apr. 9: Ancestral states, character correlation, simmap demo
- Stochastic character mapping
- Mesquite lab
[edit] Wed., Apr. 11: Penalized likelihood, Bayesian approaches
- Divergence time estimation
[edit] Mon., Apr. 16: TBA
- r8s lab
[edit] Wed., Apr. 18: TBA
[edit] Mon., Apr. 23: TBA
- Beast lab
[edit] Wed., Apr. 25: TBA
[edit] Goals of this course
This course is designed to give you the background you need to understand and critically evaluate phylogenetic analyses described in current primary literature, and to design appropriate phylogenetic analyses to address your own research questions.
Unlike many graduate courses, you will not spend a lot of time reading papers in this course. Instead, you will spend time using state-of-the-art software tools and doing homework assignments designed to ensure that you understand the output of the programs.
There is a confusing diversity of programs these days for performing phylogenetic analyses. We will concentrate on only a few so that you will know how to use these well by the end of the course.
[edit] Textbook
No textbook is required for this course, although you might find Joe Felsenstein's 2004 book "Inferring Phylogenies" (published by Sinauer) useful.
[edit] Labs
The laboratory section of this course is held in the MacCarthy computer lab on the fourth floor of Torrey Life Science (TLS 477). The labs will consist of tutorials that you work through at your own pace
[edit] Homeworks
Your grade will be largely based on homework assignments, one of which will be assigned (nearly) every week. You are expected to turn in an assignment one week after it is assigned. These homework assignments should be treated as if they were take-home, open-book exams. You may therefore consult with either me or the TA for the course, but not with fellow students when working on the homeworks.
[edit] Projects
In addition to homeworks, you will prepare a term paper to be due the last week of the course. There is a lot of flexibility in the nature of the term paper. If you have data of your own, you may decide to write a paper describing a phylogenetic analysis of these data, using appropriate methods learned during the course. If you are not yet at the stage of your graduate career where you have data of your own, you can do a thorough re-analysis of an existing data set. Finally, it is ok to simply write a review paper describing a particular topic in phylogenetics in depth. If you choose this route, I will encourage you to write a paper suitable for contribution to Wikipedia (this way, your efforts will survive the course and benefit the broader community). Please get my approval of your chosen topic before doing extensive work on your paper.
