Phylogenetics: Syllabus
From EEBedia
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| 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 |
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.
| Day | Lecture | Lab/Homework |
| Wed., Jan. 17 | Introduction   Significance, history, terminology |  Tree from splits
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| Mon., Jan. 22 | Tree thinking Rooted/unrooted, ultrametric/free, paralogy/orthology, lineage sorting, "basal" lineages |  Nexus data files
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| Wed., Jan. 24 | Consensus trees Parsimony Camin-Sokal, Wagner, Fitch, Dollo, transversion, generalized, step-matrix | Parsimony
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| Mon., Jan. 29 | Polyphyly vs. Paraphyly Revisited Searching Exhaustive, branch-and-bound, star decomposition, stepwise addition, heuristic, genetic algorithms | Searching
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| Wed., Jan. 31 | Distances Least squares criterion, minimum evolution criterion, split decomposition, quartet puzzling, DCM, NJ | Distances
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| Mon., Feb. 5 | Substitution models Transition probability, instantaneous rates, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model | Distance methods
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| Wed., Feb. 7 | Maximum likelihood Likelihood of a DNA sequence, likelihood of a pair of sequences, parameter estimation (MLEs), likelihood of a tree, likelihood ratio test, simulation | Likelihood
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| Mon., Feb. 12 | Rate heterogeneity Addendum Proportion of invariable sites, discrete gamma, site-specific rates | Likelihood
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| Wed., Feb. 14 | *** Snow day: no class today *** (but do begin working on homework 5) | Rate Het.
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| Mon., Feb. 19 | Secondary structure (stem) models Codon models RNA stem/loop structure, compensatory substitutions, stem models, nonsynonymous vs. synonymous rates, codon models | Using the cluster
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| Wed., Feb. 21 | Bootstrapping Bremer support Topology tests Bootstrapping, Bremer support, KH test, SH test, SOWH test | No homework assignment this week |
| Mon., Feb. 26 | Simulation (updated) Long branch attraction Model selection, part I Stochastic simulation, statistical consistency, long branch attraction, long branch repulsion, likelihood ratio tests, Akaike Information criterion (AIC), Bayesian Information Criterion (BIC) | Hy-Phy
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| Wed., Feb. 28 | Data partitioning Expected substitutions (updated) ILD test for combinability, using different model for each partition | Simulation
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| Mon., Mar. 5 | Spring break no class | no class |
| Wed., Mar. 7 | Spring break | no class |
| Mon., Mar. 12 | Bayes primer Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability density, Markov chain Monte Carlo (start) | ModelTest/Gamma dist.
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| Wed., Mar. 14 | Bayesian phylogenetics MCMC (continued), heated chains, choosing prior distributions | MCMC
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| Mon., Mar. 19 | Prior distributions Summarizing posterior distributions, commonly-used prior distributions, problem priors, reversible-jump MCMC, star tree paradox | MrBayes
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| Wed., Mar. 21 | Confidence intervals (follow-up on last lecture) Model Selection, part II Bayes factors, posterior predictive approaches to model selection | LOCAL move
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| Mon., Mar. 26 | Model Selection, part II (4 extra slides)
| Mesquite
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| Wed., Mar. 28 | Ancestral Character States Parsimony approach, ML approach, empirical Bayes approach | Anc. states
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| Mon., Apr. 2 | No lecture today | No lab today |
| Wed., Apr. 4 | Models for discrete morphological data DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk model, estimating morphological branch lengths | Mk model
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| Mon., Apr. 9 | Discrete Character Correlation
Continuous Character Correlation | Partitioning/Morphology
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| Wed., Apr. 11 | Stochastic Character Mapping Concentrated changes test, stochastic mapping for estimating ancestral states and character correlation, SIMMAP demo | Mapping
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| Mon., Apr. 16 | Stochastic Character Mapping (finish) | BayesTraits
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| Wed., Apr. 18 | Divergence Time Estimation Non-parametric rate smoothing, penalized likelihood, cross-validation, Bayesian approaches | Read chapter and paper for Apr. 25 |
| Mon., Apr. 23 | Inferring key innovations The Remainder Key innovations, clade contrast approach, stochastic mapping method, what was and was not covered in this course (also course evaluations) | r8s
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| Wed., Apr. 25 | Alignment (guest lecture by Karl Kjer) Reading: Chapter  Paper Toner-saver version of lecture (note: some details may be obscured by conversion to pure black and white):
| No homework today |
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.
Textbook
No textbook is required for this course, although you might find Joe Felsenstein's 2004 book "Inferring Phylogenies" (published by Sinauer) useful.
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
Homeworks
Your grade will be largely based on homework assignments, one of which will be assigned (nearly) every week. 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.
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.
Links
