Phylogenetic methods

Duration

20h Th, 15h Pr

Number of credits

Lecturer

Denis Baurain

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

This course aims to provide the bases of phylogenetic methodology required for the understanding of the course of taxonomy and phylogeny [BIOL0807-4].

• Reminder on (molecular) phylogenetics
• Sequence alignment - global alignment (NW), local alignment (SW, BLAST), multiple alignment (ClustalW) and profiles
• Parsimony - phylogenetic trees, tree length (Fitch's algorithm) and search heuristics
• Distance methods - distance matrices, W(U)PGMA, NJ and models of sequence evolution
• Probabilistic methods - likelihood (principle, algorithms, models, advantages and model selection), statistical support and congruence, Bayesian inference (principle and algorithms), relaxed molecular clocks
• Phylogenomics - principle, stochastic and systematic errors, supertrees, supermatrices, CAT model, applications

Learning outcomes of the learning unit

• Theory: At the end of this course, students will be able to explain the main concepts, methods and algorithms used in phylogenetics. They will have an intuitive understanding of probabilistic methods and will be able to justify their preferential use over other approaches. This requires, above all, to have UNDERSTOOD the course material. In this sense, this course is probably different from some other subjects of the curriculum in biology, in which mere memorization may be enough to pass the examination.
• Exercises: For some algorithms (specified in class), an APPLICATION using pen and paper to solve a toy example may be requested.
• Applications: The practical analysis of a dataset in a phylogenetic context will have been covered during computer practicals. Students will thus be able to concretely reproduce it on their own datasets.

Prerequisite knowledge and skills

The course assumes no prerequisites in computer science, but relies on a basic knowledge of mathematics and molecular genetics. In principle, the necessary level in both subjects is reached at the end of the 3rd year of BA in biology.

Planned learning activities and teaching methods

• Theoretical lectures, demonstrations and supervised exercises: 8 x 2h30.
• Computer practicals (Seaview and R): 4 x 4h.

Mode of delivery (face-to-face ; distance-learning)

This is a face-to-face course. Attending the lectures is strongly encouraged as they are designed so as to facilitate understanding and assimilation of the course material.

Recommended or required readings

Computer practicals will make use of the following book but students are not expected to buy it: Paradis E. (2012) Analysis of Phylogenetics and Evolution with R, 2nd edition, Springer, 386 pages http://www.springer.com/978-1-4614-1742-2 http://ape-package.ird.fr/APER.html

Assessment methods and criteria

Evaluation has two parts:

• 75% : written examination (January session) including three theory questions (free-form explanation of a theoretical concept) and one exercise (algorithm to be applied);
• 25% : computer practical examination (at the end of the course) where one has to analyze a new dataset.

Work placement(s)

Organizational remarks

Taking notes on a laptop or tablet is allowed. However, students are expected not to surf or chat in the classroom.

Contacts

Prof. Denis Baurain Institut de Botanique B22 (P70) denis.baurain@ulg.ac.be Assistant: Dr. Damien Sirjacobs Institut de Botanique B22 (P70) 04/366.38.54 D.Sirjacobs@ulg.ac.be