Difference between revisions of "Phylogenetics: Syllabus"

From EEBedia
Jump to: navigation, search
Line 127: Line 127:
 
|- style="background:#eeee00"
 
|- style="background:#eeee00"
 
| Thu., Apr. 23
 
| Thu., Apr. 23
| '''Divergence time estimation (part 2)'''<br/>Bayesian approaches
+
| '''Divergence time estimation (part 2)'''{{pdf|{{SERVER}}/people/plewis/courses/phylogenetics/lectures/22_DivTimeBayesianBEAST.pdf}}<br/>Bayesian approaches
 
| [[Phylogenetics: BEAST Lab|BEAST lab]]
 
| [[Phylogenetics: BEAST Lab|BEAST lab]]
 
|-
 
|-

Revision as of 15:27, 23 April 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. Downloading PDFs from this web site now requires a username and password. 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
Homework 4: Likelihood Pdficon small.gif
Thu., Feb. 12 Rate heterogeneity Pdficon small.gif
Proportion of invariable sites, discrete gamma, site-specific rates
Likelihood
Tue., Feb. 17 Codon and secondary structure models Pdficon small.gif
RNA stem/loop structure, compensatory substitutions, stem models, nonsynonymous vs. synonymous rates, codon models
Homework 5: Rate heterogeneity Pdficon small.gif
Thu., Feb. 19 Model selection Pdficon small.gif
Likelihood ratio test (LRT), Akaike Information criterion (AIC), Bayesian Information Criterion (BIC)
Expected number of substitutions for a model Pdficon small.gif
An example calculation for the F81 model
ML analyses of large datasets
The RAxML CAT model Pdficon small.gif
Tue., Feb. 24 SimulationPdficon small.gif
How to simulate nucleotide sequence data, and why it's done
Long-branch attraction Pdficon small.gif
Statistical consistency, long branch attraction (real), long branch repulsion (real?)
Homework 6: Simulation Pdficon small.gif
Thu., Feb. 26 Statistical tests involving phylogenies Pdficon small.gif
ILD parsimony test for combinability, KH test, SH test, SOWH test
Generating exponential random numbersPdficon small.gif
For Geert
HyPhy
Tue., Mar. 3 Bayes primer'Pdficon small.gif
Conditional/joint probabilities, Bayes rule, prior vs. posterior distributions, probability mass vs. probability density, Markov chain Monte Carlo
No homework this week
Thu., Mar. 5 Bayes primer (continued) Midterm exam
Tue., Mar. 10 Spring break no class
Thu., Mar. 12 Spring break no class
Tue., Mar. 17 Discussion of midterm
Bayes primer (continued)
Introduction to Markov chain Monte Carlo
no homework this week
Thu., Mar. 19 Bayesian phylogeneticsPdficon small.gif
Heated chains and MCMCMC, stepping through tree space and parameter space
Using R to explore probability distributions
Tue., Mar. 24 Confidence intervalsPdficon small.gif
Frequentist confidence intervals differ from Bayesian credible intervals
Prior distributionsPdficon small.gif
Commonly-used prior distributions
Homework 7: MCMC Pdficon small.gif
Thu., Mar. 26 Priors (cont.)Pdficon small.gif
Pros and cons, hierarchical models and hyperpriors
MrBayes lab
Tue., Mar. 31 Star tree paradoxPdficon small.gif
When posteriors and bootstraps conflict (slightly modified since last week)
Bayesian model selectionPdficon small.gif
Bayes factors, posterior predictive approaches to model selection
Homework 8: Larget-Simon movePdficon small.gif
Thu., Apr. 2 Models for discrete morphological dataPdficon small.gif
DNA sequences vs. morphological characters, Symmetric vs. asymmetric 2-state models, Mk model, Tuffley-Steel no-common-mechanism model
Morphology and partitioning in MrBayes
Tue., Apr. 7 Discrete character correlationPdficon small.gif
Pagel's likelihood ratio test
Continuous character correlationPdficon small.gif
Felsenstein's independent contrasts
Homework 9: Independent contrasts Pdficon small.gif
Thu., Apr. 9 Estimating Bayes factorsPdficon small.gif
Harmonic mean method, more accurate approaches
Ancestral statesPdficon small.gif
Likelihood, (empirical) Bayesian and parsimony reconstruction of ancestral states
Phycas
Tue., Apr. 14 Stochastic Character MappingPdficon small.gif
Concentrated changes test, stochastic mapping for estimating ancestral states and character correlation, SIMMAP demo
Homework 10: Stochastic Mapping Pdficon small.gif
Thu., Apr. 16 Mixture modelsPdficon small.gif
rjMCMC, heterotachy models, Dirichlet process prior models
BayesTraits lab
Tue., Apr. 21 Divergence time estimation (part 1)Pdficon small.gif
Non-parametric rate smoothing, penalized likelihood, cross-validation
no homework assigned
Thu., Apr. 23 Divergence time estimation (part 2)Pdficon small.gif
Bayesian approaches
BEAST lab
Tue., Apr. 28 Lineage through time plots Final exam handed out
Thu., Apr. 30 Estimating species trees Lab: APE
Wed., May 6 Final exam and project reports due by 5:30pm

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