Phylogenetics: Syllabus

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EEB 5349: Phylogenetics
Lectures: TuTh 11:00-12:15 (TLS 313)
Lab: Th 2-4 (TLS 313)
Lecture Instructor: Paul O. Lewis
Lab Instructor: Daniel Fan

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. Downloading PDFs from this web site now requires a username and password. All content linked to this page is copyright © 2011 by Paul O. Lewis.

Day Lecture Lab/Homework
Tue., Jan. 18 Introduction
The terminology of phylogenetics, rooted vs. unrooted trees, ultrametric vs. unconstrained, paralogy vs. orthology, lineage sorting, "basal" lineages, crown vs. stem groups
Homework 1: Trees from splits
Thu., Jan. 20 Introduction to optimality criteria and search strategies
Exhaustive enumeration, branch-and-bound search, algorithmic methods (star decomposition, stepwise addition, NJ), heuristic search stragegies (NNI, SPR, TBR), evolutionary algorithms
(1) Nexus data file format, (2) using the cluster, and (3) Introduction to PAUP*
Tue., Jan. 25 Consensus trees , Parsimony and History of Parsimony (PL away: watch Consensus.mov, Parsimony.mov and ParsimonyHistory.mov)
Strict, semi-strict, and majority-rule consensus trees; maximum agreement subtrees; Camin-Sokal, Wagner, Fitch, Dollo, and transversion parsimony; step matrices and generalized parsimony; History of parsimony: Hennig, Edwards, Sokal, Camin, Dayhoff, Kluge, Farris, Fitch, Sankoff, and Wiley; character vs. character state.
Homework 2: Parsimony
Thu., Jan. 27 Bootstrapping and Distance Methods (PL away: watch Bootstrapping.mov and Distances.mov)
Bootstrapping; Distance methods: split decomposition, quartet puzzling, neighbor-joining, least squares criterion, minimum evolution criterion
Snow day, no lab
Tue., Feb. 1 Substitution models (watch ModelsIntro.mov if PL not back yet)
Transition probability, instantaneous rates, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model
Homework 3: Distances
Thu., Feb. 3 Substitution models
Continue discussing models, and/or use the time to answer any questions that have built up while I was away.
Searching (start Python 101 if there is time)
Tue., Feb. 8 Maximum likelihood
Poisson processes; Likelihood: the probability of data given a model, maximum likelihood estimates (MLEs) of model parameters, likelihood of a tree, likelihood ratio test
Homework 4: Likelihood
Thu., Feb. 10 Rate heterogeneity
Proportion of invariable sites, discrete gamma, site-specific rates
Likelihood
Tue., Feb. 15 Amino Acid, codon and secondary structure models
Empirical amino acid rate matrices, transition probabilities by exponentiating the rate matrix, RNA stem/loop structure, compensatory substitutions, stem models, nonsynonymous vs. synonymous rates, codon models
Homework 5: Rate heterogeneity
Thu., Feb. 17 Model selection
Likelihood ratio test (LRT), Akaike Information criterion (AIC), Bayesian Information Criterion (BIC)
Expected number of substitutions for a model
An example calculation for the F81 model
Python 101
Tue., Feb. 22 Long-branch attraction
Statistical consistency, long branch attraction (real), long branch repulsion (real?)
Simulation
How to simulate nucleotide sequence data, and why it's done
Homework 6: Simulation
Thu., Feb. 24 Statistical tests involving phylogenies
ILD parsimony test for combinability, KH test, SH test, SOWH test
ML analyses of large datasets
Tue., Mar. 1 Bayes primer'
Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability density, Markov chain Monte Carlo
No homework this week
Thu., Mar. 3 Bayes primer (continued) Midterm exam
Tue., Mar. 8 Spring break no class
Thu., Mar. 10 Spring break no class
Tue., Mar. 15 Discussion of midterm
Bayes primer (continued)
Phylogenetic applications of Markov chain Monte Carlo
Prior distributions
Commonly-used prior distributions
no homework this week
Thu., Mar. 17 Prior distributions
Commonly-used prior distributions (continued)
Confidence vs. credible intervals
Frequentist confidence intervals differ from Bayesian credible intervals
Using R to explore probability distributions
Tue., Mar. 22 Priors (cont.) (error corrected on slide 27)
Pros and cons, hierarchical models and hyperpriors
Homework 7: MCMC
Thu., Mar. 24 Bayesian model selection (updated to include stepping-stone slides)
Bayes factors, posterior predictive approaches to model selection
MrBayes lab
Tue., Mar. 29 Star tree paradox (updated to include a few slides on BIC)
When posteriors and bootstraps conflict
Homework 8: Larget-Simon move
Thu., Mar. 31 Models for discrete morphological data
DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk model, Tuffley-Steel no-common-mechanism model
Morphology and partitioning in MrBayes
Tue., Apr. 5 The No-Common-Mechanism model
The likelihood model that behaves like parsimony
Discrete character correlation
Pagel's likelihood ratio test
Continuous character correlation
Felsenstein's independent contrasts
Homework 9: Independent contrasts
Thu., Apr. 7 Ancestral states
Likelihood, (empirical) Bayesian and parsimony reconstruction of ancestral states
Phycas
Tue., Apr. 12 Stochastic Character Mapping
Concentrated changes test, stochastic mapping for estimating ancestral states and character correlation, SIMMAP demo
Homework 10: Stochastic Mapping
Thu., Apr. 14 Mixture models
rjMCMC, heterotachy models, Dirichlet process prior models
BayesTraits lab
Tue., Apr. 19 Divergence time estimation (part 1)
Non-parametric rate smoothing, penalized likelihood, cross-validation
no homework assigned
Thu., Apr. 21 Divergence time estimation (part 2)
Bayesian approaches: Thorne/Kishino autocorrelated log-normal model; BEAST uncorrelated log-normal model; coalescent, exponential growth coalescent, and Yule tree priors
BEAST lab
Tue., Apr. 26 Lineages through time analyses Final exam now available
Thu., Apr. 28 Estimating species trees
Evaluations (last part of lecture)
APE Lab
Wed., May 5 Final exam and project reports due by 5:30pm (but if you need an extension, write to me)

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.

Compared to many graduate courses, you will spend less time reading papers and more 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 consist of tutorials that you work through at your own pace using your own laptop computer. In some cases, you will use the UConn Bioinformatics Facility's computing cluster to perform analyses. Please contact Jeff Lary (486-5036) to get an account on the cluster at your earliest convenience.

Homeworks

Your grade will be based on a midterm exam, a final exam and a number of 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. Please get my approval of your chosen topic before doing extensive work on your paper.

Links
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