Computational Molecular Evolution

开始时间: 01/13/2014 持续时间: 6 weeks

所在平台: Coursera

课程类别: 生物与生命科学

大学或机构: Technical University of Denmark (DTU)(丹麦技术大学)

授课老师: Anders Gorm Pedersen



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This course is about molecular evolution - the evolution of DNA, RNA, and protein molecules. The focus is on computational methods for inferring phylogenetic trees from sequence data, and the course will cover the fundamental theory and algorithms, while also giving the student hands-on experience with some widely used software tools. Since evolutionary theory is the conceptual foundation of biology (in the words of Theodosius Dobzhansky: "Nothing in biology makes sense except in the light of evolution"), what you learn on this course will be relevant for any project you will ever do inside the life sciences. A phylogenetic tree will almost always help you think more clearly about your biological problem. 

A special emphasis is put on methods that employ explicit models of the evolutionary process (maximum likelihood and Bayesian approaches), and we will explore the role of statistical modeling in molecular evolution, and in science more generally. A mathematical (statistical) model of a biological system can be considered to be a stringently phrased hypothesis about that system, and this way of thinking about models will often be helpful. In addition to model-based methods, you will also learn about other approaches, such as those based on parsimony and genetic distance (e.g., neighbor joining). 

Often, the evolutionary tree  is the result we are interested in - knowing how a set of sequences (or organisms) are related can provide us with important information about the biological problem we are  investigating. For instance, knowing which organisms are most closely related to a newly identified, uncharacterized, pathogenic bacterium will allow you to infer many aspects of its lifestyle, thereby giving you important clues about how to fight it. In other cases, however, inferring the structure of the tree is not the goal: for instance, our main focus may instead be the detection of positions in a protein undergoing positive selection (indicating adaptation) or negative selection (indicating conserved functional importance). However, even in these cases, the underlying phylogenetic tree will be an important part of our hypothesis about (model of) how the proteins have been evolving, and will help in getting the correct answer. 

Although the study of molecular evolution does require a certain level of mathematical understanding, this course has been designed to be accessible also for students with limited computational background (e.g., students of biology).

Topics covered:

  • Brief introduction to evolutionary theory and population genetics.
  • Mechanisms of molecular evolution.
  • Models of DNA and protein substitution.
  • Reconstruction of phylogenetic trees using parsimony, distance based methods, maximum likelihood, and Bayesian techniques.
  • Advanced models of nucleotide substitution (gamma-distributed mutation rates, molecular clock models, codon models and analysis of selective pressure).
  • Statistical analysis of biological hypotheses (likelihood ratio tests, non-parametric and parametric bootstrapping, Bayesian statistics).


Week 1:   Introduction to evolutionary theory and population genetics: models of growth, selection and mutation
Week 2:   Neutral mutations, genetic drift and tree reconstruction by parsimony
Week 3:   Consensus trees and distance matrix methods
Week 4:   Mini-project I
Week 5:   Models of DNA and protein evolution
Week 6:   Likelihood methods
Week 7:   Bayesian inference of phylogeny
Week 8:   Mini-project II
Week 9:   Testing hypotheses in a phylogenetic context
Week 10:  Bootstrap, jackknife, and permutation tests


Deep Learning Specialization on Coursera


In this course you will learn about how and why DNA and protein sequences evolve. You will learn the theory behind methods for building and analyzing phylogenetic trees, and get hands-on experience with some widely used software packages.


丹麦技术大学 DTU



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