Phylogenetics (EEB 5349)
This is a graduatelevel course in phylogenetics, emphasizing primarily maximum likelihood and Bayesian approaches to estimating phylogenies, which are genealogies at or above the species level. A primary goal is to provide an accessible introduction to the theory so that by the end of the course students should be able to understand much of the primary literature on modern phylogenetic methods and know how to intelligently apply these methods to their own problems. The laboratory provides handson experience with several important phylogenetic software packages (PAUP*, GARLI, RAxML, MrBayes, RevBayes, BEAST) and introduces students to the use of remote high performance computing resources to perform phylogenetic analyses.
EEB 5349 is being taught Spring Semester 2018:
Lecture: Tuesday/Thursday 1112:15 (lecture instructor: Paul O. Lewis)
Lab: Friday 1:253:20 (laboratory instructor: Kevin Keegan; office hour Friday 1011)
Room: Torrey Life Science (TLS) 181, Storrs Campus
Text: none required, registered students will receive PDF copies of a textbook I am currently writing (see list of optional texts below)
Syllabus
Important! The syllabus below is partially empty because the course is being taught in a different way this semester and the topics are not aligned exactly as they were in the 2016 version of the course.
Date  Lecture topics  Lab/Homework 
Tuesday Jan. 16 
Introduction The jargon of phylogenetic trees (edges, vertices, leaves, cherries, degree, split, polytomy, taxon, clade); types of genealogies; rooted vs. unrooted trees; newick descriptions; monophyletic, paraphyletic, and polyphyletic groups; why are phylogenies useful? 
Homework 1: Trees From Splits (due in lecture Tuesday Jan 23) 
Thursday Jan. 18 
Optimality criteria, search strategies Exhaustive enumeration, branchandbound search, algorithmic methods (star decomposition, stepwise addition, NJ), heuristic search strategies (NNI, SPR, TBR), evolutionary algorithms 
Friday, Jan. 19 Lab: Using the UConn Bioinformatics Facility cluster; Introduction to PAUP*; NEXUS format 
Tuesday Jan. 23 
Consensus trees, the parsimony criterion Strict, semistrict, and majorityrule consensus trees; maximum agreement subtrees; CaminSokal, Wagner, Fitch, Dollo, and transversion parsimony; step matrices and generalized parsimony 
Homework 2: Parsimony (due in class Tuesday, Jan. 30) 
Thursday Jan. 25 
Bootstrapping, distance methods Bootstrapping; Distance methods: split decomposition, quartet puzzling, neighborjoining, least squares criterion, minimum evolution criterion 
Friday, Jan. 26 Lab: Searching 
Tuesday Jan. 30(individual meetings this week) 
Substitution models Instantaneous rates, expected number of substitutions, equilibrium frequencies, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model 
Homework 3: Work through the Python Primer (nothing to hand in) 
Thursday Feb. 1(individual meetings this week) 
Maximum likelihood criterion Likelihood: the probability of data given a model, likelihood of a “tree” with just one vertex and no edges, why likelihoods are always on the log scale, likelihood ratio tests. 
Friday, Feb. 2 Lab: Likelihood 
Tuesday Feb. 6 
Maximum likelihood (cont.) Likelihood of a tree with 2 vertices connected by one edge, transition probabilities, maximum likelihood estimates (MLEs) of model parameters, likelihood of a tree. (Transition probability applet) 
Homework 4: Site likelihood (due Tuesday, Feb. 13) 
Thursday Feb. 8 
Rate heterogeneity Proportion of invariable sites, discrete gamma, sitespecific rates. 
Friday, Feb. 9 Lab: ML analyses of large data sets using RAxML and GARLI 
Tuesday Feb. 13 
Simulation How to simulate nucleotide sequence data, and why it’s done Long branch attraction Statistical consistency, long branch attraction 
Homework 5: Rate heterogeneity (due Feb. 27) 
Thursday Feb. 15 
Codon and secondary structure models Nonsynonymous vs. synonymous rates, codon models; RNA stem/loop structure, compensatory substitutions, stem models. 
Friday, Feb. 16 Lab: IQTREE 
Tuesday Feb. 20(individual meetings this week) 
Amino acid models
Empirical amino acid rate matrices (PAM, JTT, WAG, LE, etc.); using eigenvectors and eigenvalues to turn rate matrices into transition probability matrices. (Eigenvector/eigenvalue applet.) Model selection 
Continue working on Homework 5: Rate heterogeneity (due Feb. 27) 
Thursday Feb. 22(individual meetings this week) 
Model selection (cont.)
Akaike Information criterion (AIC); Bayesian information criterion (BIC). Topology tests 
Friday, Feb. 23 Lab: Simulating sequence data 
Tuesday Feb. 27 
Bayesian statistics
Bayes’ Rule, prior and posterior probability distributions, marginal probability of the data, probability vs. probability density. (archery priors applet) 
Homework 6: Simulation 
Thursday Mar. 1 
Markov chain Monte Carlo (MCMC)
MCMC “robot” metaphor, MetropolisHastings algorithm, mixing, burnin, and trace plots. (MCMC Robot applet) 
Friday, Mar. 2 Lab: Using R to explore probability distributions 
Tuesday Mar. 5 
MCMCMC, MCMC “moves”
Metropoliscoupled MCMC (i.e. “heated chains”), algorithms (a.k.a. updaters, moves, operators, proposals) for updating parameters and trees during MCMC. (applet showing slider proposal is indeed symmetric.) 
Homework: no homework this week 
Thursday Mar. 7 
Prior distributions used in phylogenetics
Gamma/Exponential/Lognormal distributions for edge lengths and rate ratios, the Beta distribution for proportions, and the Dirichlet distribution for state frequencies and GTR exchangeabilities. 
Friday, Mar. 8 Lab: MRBAYES 
Tuesday Mar. 13 
SPRING BREAK  

Thursday Mar. 15 

Tuesday Mar. 20 
Bayesian phylogenetics (continued)
Tree length vs. edge length prior, credible vs. confidence intervals, hierarchical models vs. empirical Bayes 
Homework 7: MCMC 
Thursday Mar. 22 
Bayes factors and Bayesian model selection
Bayes factors, steppingstone estimation of marginal likelihood. 
Friday, Mar. 23 Lab: Morphology and partitioning in MrBayes 
Tuesday Mar. 27 
Morphology models, Correlation in Discrete Traits
Conditioning on variability; Pagel’s (1994) test for correlated evolution among discrete traits; reversiblejump MCMC; the “No Common Mechanism” (NCM) model. (individual meetings this week) 
Homework 8: Read Maddison and Fitzjohn (2015) 
Thursday Mar. 29 
Evolutionary Correlation: Continuous Traits
Independent Contrasts and Phylogenetic Generalized Least Squares (PGLS). (individual meetings this week) 
Friday, Mar. 30 Lab: BayesTraits 
Tuesday Apr. 3 
Trait evolution (cont.)
PGLS (cont.), estimating ancestral states (PGLS slides)

Homework 9: Independent contrasts (Independent Contrasts slides) 
Thursday Apr. 5 
Estimating ancestral states
Ancestral state estimation for discrete traits (we also spent time discussing the Maddison and Fitzjohn paper and Jack’s ancestral state estimation problem.) 
Friday, Apr. 6 Lab: APE 
Tuesday Apr. 10 
Mixture models
rjMCMC (polytomies,heterotachy), covarion models, Dirichlet process mixture models 
Homework 10: Read Degnan and Rosenberg (2009) 
Thursday Apr. 12 
Species trees vs. gene trees
The coalescent, deep coalescence, incomplete lineage sorting (ILS) 
Friday, Apr. 13 Lab: ggtree 
Tuesday Apr. 17 
Species Tree Estimation
Gene tree discordance due to ILS, estimating species trees using the multispecies coalescent 
Homework 11: Simulate a coalescent tree 
Thursday Apr. 19 
Species Tree Estimation (cont.)
The SVDQuartets and ASTRAL species tree methods. Guest lecturer: David Swofford. (slides). 
Friday, Apr. 20 Lab: SVDQuartets, ASTRAL 
Tuesday Apr. 24 
Divergence time estimation
Strict vs. relaxed clocks, correlated vs. uncorrelated relaxed clocks, calibrating the clock using fossils 
No homework assignment this week 
Thursday Apr. 26 
Divergence times (cont.)
Fossilized birthdeath model, information content 
Friday, Apr. 27 Lab: Divergence time estimation using BEAST2 
Finals week  Individual meetings this week 
Books on phylogenetics
This is a list of books that you should know about, but none are required texts for this course. Listed in reverse chronological order.
Yang, Z. 2014. Molecular evolution: a statistical approach. Oxford University Press.
Baum, D. A., and S. D. Smith. 2013. Tree thinking: an introduction to phylogenetic biology. Roberts and Company Publishers, Greenwood Village, Colorado. (This book is probably the most useful companion volume for this course, introducing the methods in a very accessible way but also providing lots of practice interpreting phylogenies correctly.)
Garamszegi, L. Z. 2014. Modern phylogenetic comparative methods and their application in evolutionary biology: concepts and practice. SpringerVerlag, Berlin. (Wellwritten chapters by current leaders in phylogenetic comparative methods.)
Hall, B. G. 2011. Phylogenetic trees made easy: a howto manual (4th edition). Sinauer Associates, Sunderland. (A guide to running some of the most important phylogenetic software packages.)
Lemey, P., Salemi, M., and Vandamme, A.M. 2009. The phylogenetic handbook: a practical approach to phylogenetic analysis and hypothesis testing (2nd edition). Cambridge University Press, Cambridge, UK (Chapters on theory are paired with practical chapters on software related to the theory.)
Felsenstein, J. 2004. Inferring phylogenies. Sinauer Associates, Sunderland. (Comprehensive overview of both history and methods of phylogenetics.)
Page, R., and Holmes, E. 1998. Molecular evolution: a phylogenetic approach. Blackwell Science (Very nice and accessible preBayesianera introduction to the field.)
Hillis, D., Moritz, C., and Mable, B. 1996. Molecular systematics (2nd ed.). Sinauer Associates, Sunderland. Chapters 11 (“Phylogenetic inference”) and 12 (“Applications of molecular systematics”). (Still a very valuable compendium of preBayesianera phylogenetic methods.)