- Faculty of Biology

Structure of the modules

Responsible person for these modules: Prof. Dirk Metzler

Please make sure that you are enrolled in the moodle site of the course as important information, e.g. on exam dates, will be communicated over moodle.

Computational Methods in Evolutionary Biology is a 9 ECTS module in master's programs like bioinformatics or statistics. It consists of the lectures and tutorials that are taught on Wednesdays and Fridays. Alle teaching materials provided on this webpage are relevant for this module.
Phylogenetics is 6 ECTS module for students in master's program like e.g. Evolution, Ecology and Systematics and is usually taught in the first half of a winter term. Besides the lecturs and tutorials on Wednesdays and Fridays, also Tutorials on Tuesdays belong to this module.
Computational Methods in Population Genetics is 6 ECTS module for students in master's program like e.g. Evolution, Ecology and Systematics and is usually taught in the second half of a winter term. Besides the lecturs and tutorials on Wednesdays and Fridays, also Tutorials on Tuesdays belong to this module.

Teaching Materials

Data sets of DNA, RNA or protein sequences contain a lot of hidden informations about the history of evolution, about evolutionary processes and about the roles of particular genes in evolutionary adaptation. It is a challenge to develop methods to uncover these informations. Methods that are based on explicit models for evolutionary processes and on the application of statistical principles (like likelihood-maximization or Bayesian inferrence) are most promising. Some of these methods, however, can be very demanding - computationally and intellectually. A thorough understanding of the models and methods is crucial, not only for those who aim to contribute to the further development of such methods but also for those who want to apply these methods to their datasets and have to decide which method to choose, how to set their optional parameters and how to interprete the outcome. In the first half of the semester we will focus on computational methods in phylogenetics In the second half of the semester we will turn to population genetics.

Bayesian and likelihood-based Phylogenetics
We discuss methods from computational statistics and their applications in phylogenetic tree reconstruction. First we compare maximum-likelihood (ML) methods to parsimonious and distance-based methods. Then we turn to Bayesian methods that are based on Markov-Chain Monte-Carlo (MCMC) approaches like the Metropolis-Hastings algorithm and Gibbs sampling. Such methods allow to sample phylogenies (approximately) according to their posterior probability, i.e. conditioned on the given sequence data. Thus, it is also possible to assess the uncertainty of the estimation. Among the special applications that we discuss are phylogeny estimation with time-calibration (e.g. according to the fossil record) and methods for the reconcilement of gene trees and species trees. Statistical methods are always based on probabilistic models for the origin of the data. Therefore, we discuss evolution models for biological sequences (Jukes-Cantor, PAM, F81, HKY, F84, GTR, Gamma-distributed rates,....) and the fundamentals about Markov processes that are necessary to understand these models. Furthermore, we will discuss relaxed molecular-clock models and Brownian-motion models for the evolution of quantitative traits along phylogenetic trees. Another topic are statical sequence-alignment methods that are based on explicit sequence evolution models with insertions and deletions (TKF91, TKF92,...).

Computational methods in population genetics
Given population genetic data, how can we infer evolutionary and ecological features like population substructure, change of population size, recent speciation, natural selection and adaptation? Many computational methods for this purpose have been proposed and most of them are freely available in software packages. In this course we will discuss the theoretical and practical aspects of these methods. The theoretical aspects are the underlying models, statistical principles and computational strategies. In the practical part we will analyze these methods. We will also try out various software packages and explore under which circumstances they are appropriate. Among the models that we discuss are the coalescent process and its variants with structure and demography, the ancestral selection graph, and the ancestral recombination graph. Among the parameter estimation strategies are full-likelihood and full-Bayesian methods, methods based on summary statistics, and Approximate-Bayesian Computation. These methods use computational strategies like importance sampling and variants of MCMC.

Handout Phylogentics (PDF, 4,405 KB)

Handout Computational Methods in Population Genetics (PDF, 2,485 KB)

(For Computational Methods in Evolutionary Biology both handouts are relevant)

The screencast videos on the following pages date back to the covid winter terms 2020/2021 and 2021/2022. Please note that some of the contents have been updated or replaced in the lecures (but not in the videos). Thus, some lecture contents that are relevant for the exams may not be covered in the videos.

Computational Methods in Evolutionary Biology

Structure of the modules

Teaching Materials

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