Adiantum (maidenhair fern) pinnule with inlaid phylogeny

Phylogenetics (EEB 5349)

This is a graduate-level 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 hands-on experience with several important phylogenetic software packages (PAUP*, GARLI, RAxML, MRBAYES, BEAST) and introduces students to the computing resources of the UConn Bioinformatics Facility.

EEB 5349 is being taught Spring Semester 2016:

Lecture: Tuesdays 11-12:15 and Thursdays 9-10:15  (instructor: Paul O. Lewis)
Lab: Thursdays, 10:15-12:15 (instructor: Suman Neupane)
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)
Grade: based on midterm exam, final exam, homeworks, and project presentation


This syllabus has been updated for the Spring 2016 version of the course, but will continue to be updated periodically throughout the semester. I will post PDF versions of each lecture after they are given. Homeworks are due 1 week after the date they are assigned in the syllabus.

Date Lecture topics Lab/Homework
Jan. 19
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 (due in lecture Tuesday Jan 26)
Jan. 21
Optimality criteria, search strategies
Exhaustive enumeration, branch-and-bound search, algorithmic methods (star decomposition, stepwise addition, NJ), heuristic search strategies (NNI, SPR, TBR), evolutionary algorithms
Lab: Using the UConn Bioinformatics Facility cluster; Introduction to PAUP*NEXUS format
Jan. 26
Consensus trees, the parsimony criterion
Strict, semi-strict, and majority-rule consensus trees; maximum agreement subtrees; Camin-Sokal, Wagner, Fitch, Dollo, and transversion parsimony; step matrices and generalized parsimony
Homework 2: Parsimony (due in class Tuesday, Feb. 2)
Jan. 28
Bootstrapping, distance methods
Bootstrapping; Distance methods: split decomposition, quartet puzzling, neighbor-joining, least squares criterion, minimum evolution criterion
Lab: Python Primer
Feb. 2
Substitution models (updated after lecture)
Transition probability, instantaneous rates, Poisson processes, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model
Homework 3: Distances
Feb. 4
Maximum likelihood criterion
Likelihood: the probability of data given a model, maximum likelihood estimates (MLEs) of model parameters, likelihood of a tree, likelihood ratio test
Lab: Searching
Feb. 9
Rate heterogeneity
Proportion of invariable sites, discrete gamma, site-specific rates
Homework 4: Likelihood
Feb. 11
Codon, amino acid, 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. (Eigenvector demo)
 Lab: Likelihood
Feb. 16
Model selection
Likelihood ratio test (LRT), Akaike Information criterion (AIC), Bayesian Information Criterion (BIC)
Expected number of substitutions
An example derivation for the F81 model
Homework 5: Rate heterogeneity
Feb. 18
How to simulate nucleotide sequence data, and why it’s done
Long branch attraction
Statistical consistency, long branch attraction
Lab: ML analyses of large data sets using RAxML and GARLI
Feb. 23
Topology tests
KH test, SH test, SOWH test
Homework 6: Simulation
Feb. 25
Bayesian statistics
Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability density
Lab: TBA
Mar. 1
Markov chain Monte Carlo
Metropolis algorithm, MCMC, mixing, heated chains, Hastings ratio
Homework 7: MCMC
Mar. 3
Priors used in Bayesian phylogenetics
Commonly-used prior distributions: Beta, Gamma, Lognormal, Dirichlet
Lab:  Exploring prior distributions using R
Mar. 8
Prior miscellany
Hierarchical models and hyperpriors, Empirical Bayes, Dirichlet process priors, MCMC without data
Confidence vs. credible intervals
Frequentist confidence intervals differ from Bayesian credible intervals
No homework assigned: study for test
Mar. 10
Bayesian model selection
Marginal likelihoods and Bayes factors
Discrete morphological characters
DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk model
 Lab: MrBayes 3.2
Mar. 15
Mar. 17
Mar. 24
Midterm Exam Homework 8: LOCAL move
Mar. 24
Lab: Morphology, paritioning and model selection in MRBAYES
Mar. 29
Correlated discrete character evolution
Pagel’s likelihood ratio test
Correlated continuous character evolution
Felsenstein’s independent contrasts
Homework 9: Independent contrasts
Mar. 31
Ancestral state estimation
Likelihood, (empirical) Bayesian and parsimony reconstruction of ancestral states
Stochastic character mapping
Introduction to stochastic character mapping
Lab: Compositional heterogeneity
Apr. 5
Stochastic character mapping (continued)
SIMMAP demo: using stochastic mapping for estimating ancestral states and character correlation
Mixture models
Mixture of Rate Matrices, rjMCMC, heterotachy models
Homework 10: Character Mapping
Apr. 7
Mixture models (cont.), BIC, plus Polytomies and Bayesian Analyses
How is the Bayesian Information Criterion Bayesian? The Bayesian Star Tree Paradox and an rjMCMC solution
Lab: BayesTraits
Apr. 12
Divergence time estimation
Thorne/Kishino autocorrelated log-normal model; BEAST uncorrelated log-normal model; coalescent, exponential growth coalescent, and Yule tree priors
No homework: work on presentation
Apr. 14
Divergence time estimation (cont.)
Dirichlet process and birth-death methods, random local clocks approach, nonparametric rate smoothing/penalized likelihood
Apr. 19
Species tree estimation
Mostly *BEAST, but will mention BUCKy, ASTRAL2
No homework: work on presentation
Apr. 21
Miscellanous final topics
A few topics we did not yet get a chance to talk about
 Lab: FDPPDiv and Seq-Gen
Apr. 26
 Student presentations No homework: work on presentation
Apr. 28
 Student presentations  Lab: student presentations
Tuesday May 3 Final Exam (10:30-12:30, TLS 181)

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.)

Hall, B. G. 2011. Phylogenetic trees made easy: a how-to 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 pre-Bayesian-era 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 pre-Bayesian-era phylogenetic methods.)