Computational Evolutionary Biology

BSC 5936-6 (CRN: 11476)

Advanced computational methods are becoming increasingly important in biology. A wide range of applications --- including, for instance, identifying pathogens, tracing viral transmission pathways, and reconstructing the geographic expansion of humans out of Africa --- rely on evolutionary inference. This course will cover the methods currently used for evolutionary inference, the stochastic models and inference principles they are based on, and how they are implemented in practice. In a separate lab session, the students will get hands-on experience in developing computational software implementing these methods. We expect that the students leave the course with the necessary skills to develop their own ideas and methods.

Lecture: MW 9:05-11:00 a.m. 152 DSL
Lab: M 11:00-1:15 p.m. 152 DSL

Prerequisite: Introduction to bioinformatics or equivalent, Programming for biologists or equivalent (some background in programming), some statistics background desirable

Syllabus: download PDF

Date	Content	Material
08/29	Introduction Phylogenetic trees: definitions of parts, alternative tree data structures	Lecture notes Software assignment
08/31	Parsimony. Counting evolutionary change Fitch parsimony Wagner parsimony Sankoff cost matrices	Lecture notes
09/07	Searching for the best tree(s) How many trees are there Explicit enumeration (exhaustive search) Branch and bound method Heuristic methods	Lecture notes
09/12	Stochastic models of evolution I Discrete model to continous model Nucleotide models: JC, F81, HKY, GTR Rate variation among sites: Gamma distribution	Lecture notes Software assignment 2
09/14	Stochastic models of evolution II Protein models Codon models Stem-Loop RNA model	Lecture notes
09/19	Stochastic models of evolution III Infinite alllele mutation model, infinite sites mutation model Microsatellite mutation models: stepwise mutation model and Brownian motion model Dominant markers: RFLP, AFLP Substitution rate versus mutation rate	Lecture notes Software assignment 3
09/21	Stochastic models of evolution IV Quantitative characters Morphology	Lecture notes
09/26	Hidden Markov models General introduction to HMMs Rate variation across sites: the autocorrelated gamma Mixture models	Lecture notes Software assignment 4
09/28	Maximum likelihood inference I General principles Coin-tossing Statistical consistency and efficiency Conditional likelihoods	Lecture notes
10/03	Maximum likelihood inference II Conditional likelihoods Optimizing branch length	Lecture notes Software assignment 5
10/05	Bayesian inference General principle Coin tossing Statistical consistency and efficiency Priors	Lecture notes
10/10	Markov chain Monte Carlo I Gibbs and Metropolis samplers Metropolis coupling	Lecture notes Software assignment 6
10/12	Markov chain Monte Carlo II Proposal distributions Calculation of Hastings-ratio using Green's method Convergence	Lecture notes
10/17	Review session
10/19	Midterm (closed books)
10/24	Trees and tree models Molecular clocks Relaxed clock models Birth-death process	Lecture notes Assignment 7
10/26	Pairwise distance method Additive tree methods Minimal evolution Genetic distance measures Neighbor-Joining	Lecture notes Software assignment 8
10/31	Population models The coalescent I The simple n-coalescent Relation to population models	Lecture notes
11/02	The coalescent II The structured coalescent Coalescent and other population genetics forces	Lecture notes
11/07	The coalescent III: implementation Maximum likelihood Bayesian inference	Lecture notes Assignment 9
11/09	The coalescent and phylogeny Speciation Estimation of speciation time	Lecture notes
11/14	Phylogeography Haplotype network Statistical phylogeography	Lecture notes
11/16	Historical biogeography General overview of problems Parsimony reconstruction of vicariance scenarios Parsimony study of dispersal events Statistical approaches	Lecture notes
11/21	Gene trees and species trees Overview of the field Parsimony methods Statistical methods	Lecture notes
11/23	Johan Nylander: Model selection and model averaging I Hierarchical and nonhierarchical models Hierarchical likelihood ratio test Akaike information criterion Bayes information criterion	Lecture notes
11/28	Johan Nylander: Model selection and model averaging I The Bayesian approach: Bayes factors Model averaging	Lecture notes
11/30	Bootstrapping and jackknifing Nonparametric and parametric approaches to confidence Phylogenetic implementations Bootstrap corrections Bootstraps and posterior probabilities	Lecture notes
12/05	Statistical multiple sequence alignment Dynamic programming TKF model Bayesian multiple alignments	Lecture notes
	Final exam Labs Parsing and printing trees (two week) Calculating the parsimony score of a tree (one week) Searching for the best tree (exhaustive, branch and bound) Searching for the best tree (heuristic) Simulate data on a tree (one week) Calculate likelihood of a tree (one week) Optimization of branch length in maximum likelihood context MCMC sampling of phylogenetic models Simulate a coalescent tree (one week) Individual project (four weeks) Oral presentation of individual project