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This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being Climate Action Oceans Life on land

Academic Year 2019/2020

Learning outcomes

At the end of the course the student, starting from the knowledge of the mechanisms of molecular evolution, will be trained in the study of phylogenetic inference and will be able to reconstruct phylogenetic trees based on several molecular markers, applying the State-of-the-Art bioinformatic tools in the field. In particular the student will acquire the basics of phylogenomics and will analyze case studies in phylogenetics and phylogenomics.

Course contents

Taxonomy, systematics and phylogenetics. How to read a phylogenetic tree: monophyly, paraphyly and polyphyly. 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.
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.

Practical exercises: Ganbank search and dataset download/construction. Sequence alignment, alignment editing and phylogenetic noise removal. Data partition and the choice of the best substitution model. Use of Neighbor-Joining, Maximum Likelihood and Bayesian inference. Chronograms reconstruction and calibration using bayesian methods. Re-analysis and interpretation of reference data.


Naruya Saitou. Introduction to Evolutionary Genomics. 2013, Springer.
Philippe Lemey (ed.) . Phylogentic Handbook. 2009, Cambridge University Press.

Students will also study on the material provided by the teacher.

Teaching methods

Public letures and computer exercises.

Assessment methods

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

  • ability to align and analyze DNA sequences for phylogenetic analyses;
  • understanding of the theoretical basis of the methods of phylogenetic and filogenomic inferences;
  • ability to use the most common softwares for phylogenetic inference.
The final score is defined through a written exam on main course topics and an oral discussion focused on specific topics.

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