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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 will have advanced knowledge about the main analytical methods in phylogenetics, at various taxonomic levels. In particular, the student will acquire the ability for building and analyzing standard (binary, morphological data), molecular (nucleotide, amino acids) and combined data matrices. Moreover, the student will learn the use of specific algorithm for phylogenetic reconstruction/tree search. Furthermore, he will learn specific abilities for critical analyses of data and interpretation in theoretical and experimental frameworks.

Course contents

Definition of taxonomy, systematics and phylogeny. Phylogenetic systematics (cladistics). Homology and homoplasy; apomorphic, plesiomorphic, synapomorphic and symplesiomorphic characters.

Anatomy of a phylogenetic tree: operational taxonomic unit (OTU); hypothetical taxonomic unit (HTU) - nodes; root. Rooted and unrooted trees; cladograms, phylograms and chronograms. Monophyletic, paraphyletic and polyphyletic groups.

Morphological and molecular phylogenetics: differences, interpretations, advantages & disadvantages. Morphological (standard and presence/absence) and molecular (nucleotide or amino acid) data matrix; mixed matrix.

Phylogenetic reconstructions: algorithmic approach (distance-based; UPGMA, Neighbor-Joining) and tree search methods (character-based). Optimality criteria: Maximum Parsimony and Maximum Likelihood trees. Nodal support: resampling (bootstrap, jackknife) and character-based methods (Bremer support). Bayesian Inference and posterior probability. Tree search using the Markov chain Monte Carlo (MCMC) method.

Choice of molecular markers, homoplasy. The "species tree - gene tree" problem: from the coalescence theory to phylogenetics. Next Generation Sequencing in phylogenetics (phylogenomics).

Methods for studying molecular data. Alignment of protein-coding and non-coding sequences (progressive alignment and iterative approaches); structural alignments. Concatenated datasets; phylogenomics matrices. Observed and expected divergence. Multiple substitutions and substitution models for nucleotides and amino acids. Among-site variation and proportion of invariants.

Strict, relaxed and local molecular clock. Chronograms and tree calibration using fossil records, biogeography and secondary calibrations.

Biases in phylogenetics: nucleotide compositional bias, signal saturation, long branch attraction, incomplete lineage sorting.

Comparative methods in phylogenetics: study of characters evolution.

Computing laboratory work: use of most common software for the analysis of morphological and molecular data matrices.


Suggested textbooks are:
Dan Graur. Molecular and Genome Evolution. 2016, Sinauer Associates.
Naruya Saitou. Introduction to Evolutionary Genomics. 2013, Springer.
Philippe Lemey (ed.) . Phylogenetic Handbook. 2009, Cambridge University Press.
Barry G. Hall. Phylogenetic Trees Made Easy: A How-To Manual. 2011, Sinauer Associates.

Moreover, further study material will be provided by the teacher.

Teaching methods

.ppt presentation; computing didactic laboratory.

Assessment methods

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

Deep knowledge about theories and evolutionary processes, and their study

Deep knowledge about morphological characters and molecular markers for phylogenetic studies at different taxonomic levels

Deep knowledge about data analysis methods in phylogenetics

Ability to carry out a proper interpretation of obtained phylogenetic inferences.

The assessment will take place through a written examination on main course topics and an oral discussion focused on specific topics.

Teaching tools

.ppt presentation; computing didactic laboratory.

Office hours

See the website of Andrea Luchetti