91401 - Laboratory of Phylogenesis

Learning outcomes

At the end of the course the student will acquire knowledge on the main methods of analysis for the study of phylogenetics, at several taxonomic levels. More in detail, the student will gain the know-how for the building and analyzing standard data matrices (binary, morphological data, etc), molecular data (nucleotides or amino acids) and combined data sets. Furthermore, the student will learn the use of specific algorithm for phylogentic tree building/search. Finally, the student will acquire the ability to critically read and interpret results within the relevant theoretical-experimental framework.

Course contents

Taxonomy, systematics and phylogenetics. How to read a phylogenetic tree: monophyly, paraphyly and polyphyly. Homology and homoplasy: apomorphic, plesiomorphic, sinapomorphic and simplesiomorphic characters.

Phylogenetic trees: operational taxonomic unit (OTU); hypothetical taxonomic unit (HTU); nodes; branches; root (outgroup).

Morphological characters phylogenetics: differenzes, interpretations, pros and cons. Data matrices: morphology (standard or presence/absence) and molecular (nucleotides and/or amino acids).

Basics of molecular evolution: main mechanisms of evolution (mutation, selection, genetic drift). Nucleotide/amino acid substitutions, standing genetic variation, distribution of substitution in the genome: fast- and slow-evolving sequences. Genomes organization; protein coding and non-coding genes; repeated gene families. Omology, orthology and paralogy.
Coalescent theory and the "species tree - gene tree" problem. Molecular markers and their proper choice. Designing a phylogenetic study. NGS sequencing and phylogenomics.

Methods of analysis: Sequence alignment (progressive, iterative and structural algorithm). Observed and expected genetic distance; multiple substitution and substitution models. Phylogenetic reconstructions: distance-based methods (UPGMA, Neighbor-Joining) and character-based methods. Optimality criteria: Maximum Parsimony, Maximum Likelihood. Nodal support: bootstrap, jackknife and Bremer support. Bayesian inference and posterior probabilities. Best tree search using Markov chain Monte Carlo (MCMC) method. Strict, relaxed and local molecular clock; chronograms and calibrations (fossils; paleogeography; secondary calibrations). Validation, sensitivity analysis and phylogentic biases: nucleotide compositional bias, signal saturation, long branch attraction, incomplete lineage sorting. Principle of phylogenomics: big data matrices and analyses. Examples of phylogenetic inferences: case studies. Resources: sequence database and data collection. NCBI databases and Blast searches.

Comparative methods in phylogenetics and character evolution: reconstruction of ancestral characters.

Practical exercises:use of most common software for phylogenetic inference with morphological and molecular data. Students will individually design and carry out a phylogeny project that will be the subject of a talk.

Readings/Bibliography

David A. Baum & Stacey D. Smith. Tree Thinking. An introduction to phylogenetic biology. WH Freeman & Company.
Barry G. Hall. Phylogenetic Trees Made Easy: A How-To Manual. Sinauer Associates.

Teaching methods

Public letures and computer exercises.

Due to the kind of activity and didactical methods, attending the present course requires the prior participation of all students to the following e-learning Modules 1 and 2:

Module 1 [https://elearning-sicurezza.unibo.it/course/view.php?id=23] – Safety General Training

Module 2 [https://elearning-sicurezza.unibo.it/course/view.php?id=43] – Safety Specific Training (part I)

Assessment methods

The exam at the end of the course aims to assess the achievement of the following learning objectives:

• deep knowledge of evolutionary theories and processe, and their study
• deep knowledge of morphological and molecular characters for phylogenetic inference at different taxonomic level
• understanding of the theoretical basis of the methods of phylogenetic and filogenomic inferences;
• correct interpretation of the data and results
  

The final score is defined through a written exam on main course topics and an oral discussion focused on specific topics. Moreover, the phylogeny project presented by students will be part of the evaluation.

 

To participate to exams is necessary to subscribe the list on AlmaEsami, within the indicated deadline. Those unable to subscribe are invited to communicat as soon as possible (and before the official closure of the exam list subscription) the problem to studend segretary. The teacher will eventually decide about the admission. Evaluation will be recorded with 5 days from the exam date, even in absence of the student according to the guidelines published at http://www.unibo.it/Portale/Guida/AlmaEsamiInformazioniDocenti.htm .

Teaching tools

Theoretical lessons, with power point presentations and practical exercises with computers.

All slides, files, tutorials and other material used during the course will be provided to students

Office hours

See the website of Andrea Luchetti