Difference between revisions of "Phylogenetics: Syllabus"

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Revision as of 15:41, 11 February 2009

Adiantum.png EEB 349: Phylogenetics
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.

Day Lecture Lab/Homework
Tue., Jan. 20 IntroductionPdficon small.gif
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
Thu., Jan. 22 Introduction to optimality criteria and search strategies Pdficon small.gif
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. 27 Consensus trees Pdficon small.gif and Parsimony Pdficon small.gif
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
Thu., Jan. 29 History of Parsimony Pdficon small.gif, Bootstrapping 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
Tue., Feb. 3 Distance Methods Pdficon small.gif (a few slides added to end of the pdf since last lecture)
Split decomposition, quartet puzzling, DCM, NJ
Homework 3: Distances Pdficon small.gif
Thu., Feb. 5 Substitution modelsPdficon small.gif
Transition probability, instantaneous rates, JC69 model, K2P model, F81 model, F84 model, HKY85 model, GTR model
Python 101
Tue., Feb. 10 Maximum likelihoodPdficon small.gif
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
Thu., Feb. 12 Rate heterogeneity
Proportion of invariable sites, discrete gamma, site-specific rates
Likelihood
Tue., Feb. 17 Codon and secondary structure models
RNA stem/loop structure, compensatory substitutions, stem models, nonsynonymous vs. synonymous rates, codon models
Homework TBA
Thu., Feb. 19 Bootstrapping and topology tests
Bootstrapping, Bremer support, KH test, SH test, SOWH test
GARLI/RaxML lab
Tue., Feb. 24 Simulation (updated)
Stochastic simulation, statistical consistency, long branch attraction, long branch repulsion, likelihood ratio tests, Akaike Information criterion (AIC), Bayesian Information Criterion (BIC)
Homework TBA
Thu., Feb. 26 Data partitioning
ILD test for combinability, using different model for each partition
Lab TBA
Tue., Mar. 3 Bayes primer
Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability density, Markov chain Monte Carlo (start)
Homework TBA
Thu., Mar. 5 Midterm exam Lab TBA
Tue., Mar. 10 Spring break no class
Thu., Mar. 12 Spring break no class
Tue., Mar. 17 Bayesian phylogenetics
MCMC (continued), heated chains, choosing prior distributions
MCMC
Thu., Mar. 19 Prior distributions
Summarizing posterior distributions, commonly-used prior distributions, problem priors, reversible-jump MCMC, star tree paradox
MrBayes
Tue., Mar. 24 Confidence intervals (follow-up on last lecture)
Bayes factors, posterior predictive approaches to model selection
LOCAL move
Thu., Mar. 26 Model Selection, part II (4 extra slides) Mesquite
Tue., Mar. 31 Ancestral Character States
Parsimony approach, ML approach, empirical Bayes approach
Anc. states
Thu., Apr. 2 No lecture today No lab today
Tue., Apr. 7 Models for discrete morphological data
DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk model, estimating morphological branch lengths
Mk model
Thu., Apr. 9 Discrete Character Correlation

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

Partitioning/Morphology
Tue., Apr. 14 Stochastic Character Mapping
Concentrated changes test, stochastic mapping for estimating ancestral states and character correlation, SIMMAP demo
Mapping
Thu., Apr. 16 Stochastic Character Mapping (finish) BayesTraits
Tue., Apr. 21 Divergence Time Estimation
Non-parametric rate smoothing, penalized likelihood, cross-validation, Bayesian approaches
Read chapter and paper for Apr. 25
Thu., Apr. 23 Inferring key innovations
Key innovations, clade contrast approach, stochastic mapping method, what was and was not covered in this course (also course evaluations)
r8s
Tue., Apr. 28 TBA 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.

Links