22115 - Computational Molecular Evolution: Difference between revisions

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; Overview  [[File:Darwin logo2 medium.png |right|border|550px]]
; Overview  [[File:Darwin logo2 medium.png |right|border|550px]]
: This page contains links to video lectures, computer exercises, and other material for the course [https://kurser.dtu.dk/course/22115 22115 - Computational Molecular Evolution], which is part of the [https://www.dtu.dk/english/education/msc/programmes/systems_biology MSc in Bioinformatics and Systems Biology] at the [https://www.dtu.dk/english Technical University of Denmark]. The course is taught by Professor Anders Gorm Pedersen, [https://www.healthtech.dtu.dk/english/Research/Research-Sections/Section-Bioinformatics Section for Bioinformatics], [https://www.healthtech.dtu.dk/english Department of Health Technology].
: This page contains links to video lectures, computer exercises, and other material for the course [https://kurser.dtu.dk/course/22115 22115 - Computational Molecular Evolution], which is part of the [https://www.dtu.dk/english/education/graduate/msc-programmes/bioinformatics MSc in Bioinformatics and Systems Biology] at the [https://www.dtu.dk/english Technical University of Denmark]. The course is taught by Professor Anders Gorm Pedersen, [https://www.healthtech.dtu.dk/english/Research/Research-Sections/Section-Bioinformatics Section for Bioinformatics], [https://www.healthtech.dtu.dk/english 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 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
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=='''Computer setup'''==
=='''Computer setup'''==


===Linux===
===MacOS===
:* [[Linux software installation]]
:* [[MacOS software installation]]
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===Windows===
===Windows===
:* [[Windows software installation]]
:* [[Windows software installation]]
<!--:* [[Notes on using Windows for exercises]] '''UNDER CONSTRUCTION''' -->
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===MacOS===
===Linux===
:* [[MacOS software installation]]
:* [[Linux software installation]]
<!-- :* [[Notes on using MacOS for exercises]] '''UNDER CONSTRUCTION''' -->
<!-- :* [[Notes on using Linux for exercises]] '''UNDER CONSTRUCTION''' -->
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===VirtualBox===
:* Use this only if you can't install natively on MacOS, Windows, or Linux. Runs a virtual Linux on top of your own OS.
:* [[VirtualBox installation]]
:* [[Notes on using VirtualBox for exercises]]
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== '''Lecture Schedule''' ==
== '''Lecture Schedule''' ==
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:([[27615 Previous course programs|Course programs, previous years]])
:([[27615 Previous course programs|Course programs, previous years]])


===Week 1 (January 31): Introduction to evolutionary theory and population genetics. Models of growth, selection and mutation===
===Week 1 (February 4): Introduction to evolutionary theory and population genetics. Models of growth, selection and mutation===


:; Online lectures
:; Online lectures
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===Week 2 (February 7): Neutral mutations and genetic drift. Tree reconstruction by parsimony===
===Week 2 (February 11): Neutral mutations and genetic drift. Tree reconstruction by parsimony===


:; Online lectures
:; Online lectures
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===Week 3 (February 14): Consensus trees. Distance matrix methods===
===Week 3 (February 18): Consensus trees. Distance matrix methods===


:; Online lectures
:; Online lectures
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===Week 4+5 (February 21 + 28): Mini project 1===
===Week 4+5 (February 25 + March 4): Mini project 1===


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===Week 6 (March 6): Models of sequence evolution. Likelihood methods===
===Week 6 (March 11): Models of sequence evolution. Likelihood methods===


:; Online lectures
:; Online lectures
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:; Course material
:; Course material
:* [https://teaching.healthtech.dtu.dk/material/22115/handout_real_exp_change.pdf|Handout exercise: Real, Observed, and Expected Change]
:* [[Media: Expectedchange.pdf | Handout exercise: Real, Observed, and Expected Change]]
:* [https://teaching.healthtech.dtu.dk/material/22115/Handout_likelihood.pdf Handout exercise: Computation of Likelihood]
:* [https://teaching.healthtech.dtu.dk/material/22115/Handout_likelihood.pdf Handout exercise: Computation of Likelihood]
:* [https://teaching.healthtech.dtu.dk/material/22115/Slides_week4.pdf Slides, week 6]
:* [https://teaching.healthtech.dtu.dk/material/22115/Slides_week4.pdf Slides, week 6]
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===Week 7 (March 13): Bayesian inference of phylogeny===
===Week 7 (March 18): Bayesian inference of phylogeny===


:; Online lectures
:; Online lectures
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===Week 8+9 (March 20 + April 3): Mini project 2===
===Week 8+9 (March 25 + April 8): Mini project 2===


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===Week 10 (April 10): Model Selection===
===Week 10 (April 15): Model Selection===


:; Online lectures
:; Online lectures
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===Week 11 (April 17): Bayesian Phylogenetics, Part 2 ===
===Week 11 (April 22): Bayesian Phylogenetics, Part 2 ===
:; Course material
:; Course material
:* [https://www.researchgate.net/publication/319965471_A_biologist%27s_guide_to_Bayesian_phylogenetic_analysis A biologist’s guide to Bayesian phylogenetic analysis]
:* [https://www.researchgate.net/publication/319965471_A_biologist%27s_guide_to_Bayesian_phylogenetic_analysis A biologist’s guide to Bayesian phylogenetic analysis]
:* [https://beast.community/analysing_beast_output Analysing BEAST output using Tracer]
:* [https://beast.community/analysing_beast_output Analysing BEAST output using Tracer]
:* [https://beast.community/tracer_convergence Identifying convergence problems using Tracer]
:* [https://beast.community/tracer_convergence Identifying convergence problems using Tracer]
:* [https://taming-the-beast.org/tutorials/Troubleshooting/ Post-processing and improving performance]
:* [https://taming-the-beast.org/tutorials/Troubleshooting-initialization-issues/ Troubleshooting initialization issues]
:* [https://taming-the-beast.org/tutorials/Troubleshooting-convergence-issues/ Troubleshooting convergence issues]


:; Computer exercise
:; Computer exercise
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===Week 12 + 13 (April 24 + May 1): Mini project 3: Final exam===
===Week 12 + 13 (April 20 + May 6): Mini project 3: Final exam===


'''Details will follow'''  
'''Details will follow'''  


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Latest revision as of 12:04, 11 February 2026

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

MacOS

Windows

Linux

Lecture Schedule

(Course programs, previous years)

Week 1 (February 4): Introduction to evolutionary theory and population genetics. Models of growth, selection and mutation

Online lectures
Course material
Computer exercise

Week 2 (February 11): Neutral mutations and genetic drift. Tree reconstruction by parsimony

Online lectures
Course material
Computer exercise

Week 3 (February 18): Consensus trees. Distance matrix methods

Online lectures
Course material
Computer exercises

Week 4+5 (February 25 + March 4): Mini project 1

Week 6 (March 11): Models of sequence evolution. Likelihood methods

Online lectures
Course material
Computer exercises

Week 7 (March 18): Bayesian inference of phylogeny

Online lectures
Course material
Computer exercise

Week 8+9 (March 25 + April 8): Mini project 2

Week 10 (April 15): Model Selection

Online lectures
Course material
Computer exercise

Week 11 (April 22): Bayesian Phylogenetics, Part 2

Course material
Computer exercise

Week 12 + 13 (April 20 + May 6): Mini project 3: Final exam

Details will follow

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