## 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, RefBayes, 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 11-12:15 (instructor: Paul O. Lewis)

**Lab:** Friday 1:25-3:20

**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

## Syllabus

**Important!** The syllabus below is left over from the Spring 2016 version of the course, and will change somewhat before the Spring 2018 course begins. Homeworks are due 1 week after the date they are assigned in the syllabus.

Date |
Lecture topics |
Lab/Homework |

Tuesday Jan. 19 |
IntroductionThe 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) |

Thursday Jan. 21 |
Optimality criteria, search strategiesExhaustive 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 |

Tuesday Jan. 26 |
Consensus trees, the parsimony criterionStrict, 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) |

Thursday Jan. 28 |
Bootstrapping, distance methodsBootstrapping; Distance methods: split decomposition, quartet puzzling, neighbor-joining, least squares criterion, minimum evolution criterion |
Lab: Python Primer |

Tuesday Feb. 2 |
Transition probability, instantaneous rates, Poisson processes, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR modelSubstitution models (updated after lecture) |
Homework 3: Distances |

Thursday Feb. 4 |
Maximum likelihood criterionLikelihood: the probability of data given a model, maximum likelihood estimates (MLEs) of model parameters, likelihood of a tree, likelihood ratio test |
Lab: Searching |

Tuesday Feb. 9 |
Rate heterogeneityProportion of invariable sites, discrete gamma, site-specific rates |
Homework 4: Likelihood |

Thursday Feb. 11 |
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)Codon, amino acid, secondary structure models |
Lab: Likelihood |

Tuesday Feb. 16 |
Model selectionLikelihood ratio test (LRT), Akaike Information criterion (AIC), Bayesian Information Criterion (BIC) Expected number of substitutionsAn example derivation for the F81 model |
Homework 5: Rate heterogeneity |

Thursday Feb. 18 |
How to simulate nucleotide sequence data, and why it’s doneSimulationStatistical consistency, long branch attractionLong branch attraction |
Lab: ML analyses of large data sets using RAxML and GARLI |

Tuesday Feb. 23 |
ILD, KH, SH, AU and SOWH testsTopology tests |
Homework 6: Simulation |

Thursday Feb. 25 |
Bayesian Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability densitystatistics |
Lab: Exploring probability distributions using R |

Tuesday Mar. 1 |
Markov chain Monte CarloMetropolis algorithm, MCMC, mixing, heated chains, Hastings ratio |
Homework 7: MCMC |

Thursday Mar. 3 |
Commonly-used prior distributions: Beta, Gamma, Lognormal, DirichletPriors used in Bayesian phylogenetics |
Lab: MrBayes 3.2 |

Tuesday Mar. 8 |
Hierarchical models and hyperpriors, Empirical Bayes, Dirichlet process priors, MCMC without dataPrior miscellanyFrequentist confidence intervals differ from Bayesian credible intervalsConfidence vs. credible intervals |
Homework 8: LOCAL move |

Thursday Mar. 10 |
Marginal likelihoods and Bayes factorsBayesian model selection |
Lab: Morphology, partitioning and model selection in MRBAYES |

Tuesday Mar. 15 |
SPRING BREAK | |
---|---|---|

Thursday Mar. 17 |
||

Tuesday Mar. 24 |
DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk modelDiscrete morphological characters |
Review study questions handed out in lecture (will discuss Mar. 29) |

Thursday Mar. 24 |
No lecture (Paul is out of town) | Lab: HyPhy |

Tuesday Mar. 29 |
Discussion of study guide questions | Homework 9: Independent contrasts |

Thursday Mar. 31 |
Pagel’s likelihood ratio testCorrelated discrete character evolutionCorrelated continuous character evolutionFelsenstein’s independent contrasts (simulator shown in class) |
Lab:BayesTraits |

Tuesday Apr. 5 |
Phylogenetic Generalized Least Squares (PGLS)Linear regression with correlation structure of residuals determined by the phylogeny |
Read O’Meara (2012) before Tuesday Apr. 12 |

Thursday Apr. 7 |
Introduction to the use of stochastic character mapping for estimating ancestral states and character correlationStochastic character mapping |
Lab: APE |

Tuesday Apr. 12 |
Discussion of O’Meara (2012)O’Meara, B. C. 2012. Evolutionary inferences from phylogenies: a review of methods. Ann. Rev. Ecol. Evol. Syst. 43:267-285.Mixture modelsMixture of rate matrices, rjMCMC, heterotachy models, covarion models, Dirichlet process models. |
Read Maddison and FitzJohn (2015) before Tuesday Apr. 19 |

Thursday Apr. 14 |
Divergence time estimationThorne/Kishino autocorrelated log-normal model; BEAST uncorrelated log-normal model; Yule tree priors; Fossilized Birth-Death Prior |
Lab: BEAST |

Tuesday Apr. 19 |
Discussion of Maddison & FitzJohn (2015)Maddison, W. P., and FitzJohn, R. G. 2015. The unsolved challenge to phylogenetic correlation tests for categorical characters. Syst. Biol. 64(1):127-136.Coalescent theory needed for understanding the multispecies coalescent modelJust enough coalescent theory |
Final exam (take-home) will be handed out (due May 5 at 5:30pm) |

Thursday Apr. 21 |
*BEAST, ASTRAL2, SVDQuartets.Gene trees within species trees |
Lab: *BEAST |

Tuesday Apr. 26 |
Medley of topicsPolytomy priors and community phylogenetics |
No homework (use time to work on final) |

Thursday Apr. 28 |
Bayesian information contentMedley (cont.) |
Astral-2, SVDQuartets (GARLI bootstrap trees) |

Thursday May 5 | Final exam due by 5:30pm |

## 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.)