Computational Methods in Evolutionary Biology, WS 2016/2017

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Language: English

Instructor: Prof. Dr. Dirk Metzler

Lecture: Each Wednesday and Friday from 8:45 to 10:15 in room C00.013
Exercises: Each Wednesday from 10:15 to 11:00 and each Friday from 10:15 to 12:00 in the computer room C00.005
Additional exercises and lecture for block courses Phylogenetics I/II and Comp. Pop. Gen. I/II: Tuesday 9:00 to 12:00 and Wednesday from 11:00 to 12:00.

Make-up exam for computational population genetics II: 24. March 2017 from 2:00 p.m. to 3:00 p.m. in seminar room C00.013. Note: to take this make-up exam it is necessary to register before 20. March by e-mail to Prof. Dr. Dirk Metzler.
Make-up exam for computational methods in evolutionary biology (for bioinformaticians): 24. March 2017 from 2:00 p.m. to 4:00 p.m. in seminar room C00.013. Note: to take this make-up exam it is necessary to register before 20. March by e-mail to Prof. Dr. Dirk Metzler.
Please note that you can bring an A4 formula sheet and a pocket calculator. Then A4 formula sheet must only contain your original handwriting! It can be two-sided. This holds for both exams.

Target group / Zielgruppe: Master's and PhD students in EES/MEME, Bioinformatics, Biostatistics, Biology, Mathematics, Statistics,...
Students in block-structured programms like EES, MEME and the Master's program in Biology can participate block-wise:
Phylogenetics I (Block I, Oct 17 - Nov 4)
Phylogenetics II (Block II, Nov 7 - Nov 25)
Computational Methods in Population Genetics I (Block III, Nov 28 - Dec 23)
Computational Methods in Population Genetics II (Block IV, Jan 9 - Feb 10)


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,...). Software: PHYLIP, Seq-Gen, R with the ape package, RAxML, MrBayes, BEAST, Bali-Phy, ....
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. Software: LAMARC, GENETREE, Hudson's MS, IM/IMa, MIMAR, STRUCTURE, etc...

The following handouts contain only a summary of the contents of the slides shown in the lecture. More detailed explanations are given on the whiteboard during the lectures. The handouts will be updated during the semester.
Handout on Phylogenetics: PhyloHandout.pdf, handout on Computational Population Genetics: CMPG_handout.pdf
Exercises on Phylogenetics
phylo04.pdf , PAM_rate_matrix.txt, pfold_rate_matrix.txt
phylo05.pdf, pruning.R
phylo06.pdf, QuantTraitsA.csv, QuantTraitsB.csv, QuantTraitsC.csv, QuantTraits_Tree.txt
Phylogenetics example files
primates.nex, primates.phylip, primates.R,
Exercises on Computational Population Genetics
sheet06.pdf, cheater.txt,
Comp PopGen software example files
Tajimas_D.R, abc.R
SortSequences.R (Example R file to convert ms/seq-gen output to Migrate input file, which can be read by Lamarac input file converter)
In the practical part of the cours(es) we will use Linux. If you are new to Linux/Unix, you may be interested in some online tutorials such as

Announcement for bioinformaticians in official LMU course overview

web page last updated: Dirk Metzler, 13. March 2017