22115 - Computational Molecular Evolution

Overview

This page contains links to video lectures, computer exercises, and other material for the course 22115 - Computational Molecular Evolution, which is part of the MSc in Bioinformatics and Systems Biology at the Technical University of Denmark. The course is taught by Professor Anders Gorm Pedersen, Section for Bioinformatics, Department of Health Technology.

The main goal of this course is to give an introduction to theory and algorithms in the field of computational molecular evolution. We will cover basic evolutionary theory (common descent, natural selection, genetic drift, models of growth and selection), and the main types of algorithms used for constructing and analyzing phylogenetic trees (parsimony, distance based methods, maximum likelihood methods, and Bayesian inference). We will also discuss the role of statistical modeling in science more generally

The course will consist of lectures, computer exercises, and mini-projects. The student will acquire practical experience in the use of a range of computational methods by analyzing sequences from the scientific literature.

Computer setup

Linux

• Linux software installation

Windows

• Windows software installation

MacOS

• MacOS software installation

Lecture Schedule

(Course programs, previous years)

Week 1 (January 31): Introduction to evolutionary theory and population genetics. Models of growth, selection and mutation

Online lectures

• Common descent (11:52)
• Natural selection (14:57)
• Evidence for evolution (part 1) (9:34)
• Evidence for evolution (part 2) (20:54)
• Population growth and selection (18:13)

Course material

• Lecture notes on evolutionary theory and population genetics
• Slides, week 1

Computer exercise

• Population Growth, Fitness, and Selection

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Week 2 (February 7): Neutral mutations and genetic drift. Tree reconstruction by parsimony

Online lectures

• Neutral Theory of Molecular Evolution (11:28)
• Genetic Drift (9:35)
• Trees: Terminology and Representation (9:41)
• Homology and Homoplasy (9:07)
• Maximum Parsimony (7:48)
• The Fitch Algorithm (10:31)
• Searching Tree Space (14:01)

Course material

• Slides, week 2
• PAUP 3.1 manual (note: for older version - contains explanations of parsimony and tree moves)
• PAUP 4beta command reference

Computer exercise

• Phylogenetic Analysis using Parsimony

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Week 3 (February 14): Consensus trees. Distance matrix methods

Online lectures

• Consensus Trees (16:27)
• Distance Matrix Methods, part 1 (6:07)
• Distance Matrix Methods, part 2 (22:28)
• Neighbour Joining (15:28)

Course material

• Handout exercise: Consensus Trees
• Handout exercise: Distance Matrix Methods
• Slides, week 3

Computer exercises

• Consensus Trees
• Distance Matrix Methods

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Week 6 (March 6): Models of sequence evolution. Likelihood methods

Online lectures

• Models of evolution (28:48)
• Maximum likelihood (22:06)
• Ancestral reconstruction (10:45)

Course material

• exercise: Real, Observed, and Expected Change
• Handout exercise: Computation of Likelihood
• Slides, week 6
• Lecture notes: Substitution models
• Optional lecture notes: Matrix exponentials for Markov chains

Computer exercises

• Models of Evolution
• Maximum Likelihood

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Week 7 (March 13): Bayesian inference of phylogeny

Online lectures

• Bayesian Inference (23:48)
• Markov chain Monte Carlo (19:54)

Course material

• Handout exercise: Bayesian estimation of model parameter value
• Slides, week 7
• An Introduction to Bayesian Statistics Without Using Equations
• Background reading: "What is Bayesian statistics?"
• Background reading: "Bayesian computation: a statistical revolution"

Computer exercise

• Bayesian Phylogeny

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Week 10 (April 10): Model Selection

Online lectures

• Model selection, part 1 (15:19)
• Model selection, part 2 (17:20)
• Introduction to computer exercise: detection of selection (15:24)

Course material

• Slides, week 10
• jmodeltest manual

Computer exercise

• Model selection

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Week 11 (April 17): Bayesian Phylogenetics, Part 2

Course material

• A biologist’s guide to Bayesian phylogenetic analysis
• Analysing BEAST output using Tracer
• Identifying convergence problems using Tracer
• Post-processing and improving performance

Computer exercise

• Bayesian phylogenetics: checking convergence
• Bayesian phylogenetics: clock models

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Week 12 + 13 (April 24 + May 1): Mini project 3: Final exam

Details will follow

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