Phylogenetics (EEB 5349)

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THIS PAGE IS EXPERIMENTAL

I was in the process of transitioning away from using a big table for the syllabus, but never finished the job. Hence this page is just partially complete. For the real Phylogenetics EEB 5349 home page, last used for the Spring 2009 edition of the course, please visit Phylogenetics: Syllabus instead.

INFORMATION BELOW THIS POINT IS PROBABLY VERY OBSOLETE

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Lectures: TTh 12:30-1:45 (TLS 313)

Lab: Th 2-4 (TLS 313)

Lecture Instructor: Paul O. Lewis

Lab Instructor: Jessica Budke

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 © 2009 by Paul O. Lewis.

Introduction ((Tue., Jan. 20))

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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 Pdficon small.gif

Optimality criteria and search strategies ((Thu., Jan. 22))

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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*

Consensus trees and Parsimony ((Tue., Jan. 27))

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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 Pdficon small.gif

History of Parsimony, Bootstrapping (Thu., Jan. 29)

Pdficon small.gif Pdficon small.gif, and Distance Methods
History of parsimony: Hennig, Edwards, Sokal, Camin, Dayhoff, Kluge, Farris, Fitch, Sankoff, and Wiley; character vs. character state; bootstrapping, least squares criterion, minimum evolution criterion Searching

Distance Methods (Tue., Feb. 3)

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Split decomposition, quartet puzzling, DCM, NJ

Homework 3: Distances Pdficon small.gif

Substitution models(Thu., Feb. 5)

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Transition probability, instantaneous rates, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model Python 101

Maximum likelihood (Tue., Feb. 10)

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Poisson processes; Likelihood: the probability of data given a model, maximum likelihood estimates (MLEs) of model parameters, likelihood of a tree, likelihood ratio test, simulation

Homework 4: Likelihood Pdficon small.gif

Rate heterogeneity (Thu., Feb. 12)

Proportion of invariable sites, discrete gamma, site-specific rates

Likelihood

Codon and secondary structure models(Tue., Feb. 17)

RNA stem/loop structure, compensatory substitutions, stem models, nonsynonymous vs. synonymous rates, codon models

Homework TBA

Topology tests (Thu., Feb. 19)

Bremer support, KH test, SH test, SOWH test GARLI/RaxML lab

Simulation (Tue., Feb. 24)

Stochastic simulation, statistical consistency, long branch attraction, long branch repulsion, likelihood ratio tests, Akaike Information criterion (AIC), Bayesian Information Criterion (BIC)

Homework TBA

Data partitioning(Thu., Feb. 26)

ILD test for combinability, using different model for each partition

Lab TBA

Bayesian statistics (Tue., Mar. 3)

Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability density, Markov chain Monte Carlo (start)

Homework TBA

Midterm exam(Thu., Mar. 5)

Lab TBA

Spring break (Tue., Mar. 10)

no class

Spring break (Thu., Mar. 12)

no class

Bayesian phylogenetics (Tue., Mar. 17)

MCMC (continued), heated chains, choosing prior distributions

MCMC

Prior distributions (Thu., Mar. 19)

Summarizing posterior distributions, commonly-used prior distributions, problem priors, reversible-jump MCMC, star tree paradox

MrBayes

Bayesian model selection (Tue., Mar. 24)

Bayes factors, posterior predictive approaches to model selection

 LOCAL move

Model Selection (Thu., Mar. 26)

, part II (4 extra slides)

Mesquite

Ancestral Character States (Tue., Mar. 31)

Parsimony approach, ML approach, empirical Bayes approach

Anc. states

TBA (Thu., Apr. 2)

No lab today

Models for discrete morphological data(Tue., Apr. 7)

DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk model, estimating morphological branch lengths

Mk model

Character Correlation (Thu., Apr. 9)

Pagel's likelihood ratio test, Felsenstein's threshhold model, Felsenstein's independent contrasts

Partitioning/Morphology

Stochastic Character Mapping (Tue., Apr. 14)

Concentrated changes test, stochastic mapping for estimating ancestral states and character correlation, SIMMAP demo

Mapping

TBA (Thu., Apr. 16)

BayesTraits

Divergence Time Estimation (Tue., Apr. 21)

Non-parametric rate smoothing, penalized likelihood, cross-validation, Bayesian approaches Read chapter and paper for Apr. 25

Key innovations (Thu., Apr. 23)

Key innovations, clade contrast approach, stochastic mapping method, what was and was not covered in this course (also course evaluations)

r8s 

TBA (Tue., Apr. 28)

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 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.

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