Computational methods in Evolution

With this course we aim to provide you with the theoretical knowledge and practical skills to carry out molecular evolutionary analyses on sequence data. In this course you will learn how and why DNA and protein sequences evolve between and within species. Also, we will focus on analyzing within species sequences (e.g. human genome datasets) and infer the history of the species as well as understand how and where natural selection operates. On one hand, the course is focused on the computational methods for inferring phylogenetic trees from sequence data, giving an introduction to the fundamental theory and algorithms. This course will entail data retrieval and assembly, alignment techniques, phylogeny reconstruction, hypothesis testing, and population genetic approaches. On the other hand, the course is dealing with the properties of a sample of sequences and polymorphisms from a single species, thus introducing the concept of coalescent trees.

Although the study of molecular phylogenetics and evolution do 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

• Introduction to evolutionary theory and population genetics.
• Interpretation of molecular phylogenetic trees
• Dataset assembly and sequence alignment
• Models of substitution and advanced models of nucleotide substitution (gamma-distributed mutation rates, codon models and analysis of selective pressure).
• Reconstruction of phylogenetic trees using parsimony, distance based methods, maximum likelihood, and Bayesian techniques.
• Statistical analysis of biological hypotheses (likelihood ratio tests, Akaike Information Criterion, Bayesian statistics).
• Hypothesis testing in phylogenetics
• Estimating divergence times
• Coalescent model and inference from population data
• Inference of demographic history using the coalescent
• Detecting natural selection from polymorphic data
• Detecting selection from polymorphic data and divergence