Guía Docente 2021-22


Id.: 33370
Subject type: OBLIGATORIA
Year: 3 Teaching period: Primer Cuatrimestre
Credits: 6 Total hours: 150
Classroom activities: 62 Individual study: 88
Main teaching language: Inglés Secondary teaching language: Castellano
Lecturer: Email:


The main goal of this subject is providing the tools to analyze molecular sequences in order to establish evolutionary relationships between each other by the creation of trees.

Historical background will be provided and how the discovers in biotechnology have provided new theories in the phylogenetics field. The process of tree building by detecting the difference between a given set of biological sequence will be analyzed. Substitution methods of evolution will be explored by using mathematical concepts.

Tree building algorithms based on distances or probabilistics approaches will be analyzed to determine which performs the best outcome. Already developed tools, as well as, R packages of functions and in-house python algorithms will be used for phylogenetics tree building.

Validation of trees will be also determined and the computational limitations due to genomic complexity will be analyzed.


General programme competences G03 Cooperate to achieve common results through teamwork in a context of integration, collaboration and empowerment of critical discussion.
G04 Reason critically based on information, data and lines of action and their application on relevant issues of a social, scientific or ethical nature.
G05 Communicate professional topics in Spanish and / or English both orally and in writing.
G07 Choose between different complex models of knowledge to solve problems.
G08 Recognise the role of the scientific method in the generation of knowledge and its applicability to a professional environment.
G10 Apply creativity, independence of thought, self-criticism and autonomy in the professional practice.
Specific programme competences E12 Apply the principles and techniques of protein computational modelling to predict their biological function, their activity or new therapeutic targets (Structural Bioinformatics, Computational Toxicology).
E13 Apply omics technologies for the extraction of statistically significant information and for the creation of relational databases of biodata that can be updated and publicly accessible to the scientific community.
E15 Infer the evolutionary history of genes and proteins through the creation and interpretation of phylogenetic trees.
E16 Plan linkage and association studies for medical and environmental purposes.
E17 Induce complex relationships between samples by applying statistical and classification techniques.
E18 Apply statistical and computational methods to solve problems in the fields of molecular biology, genomics, medical research and population genetics.
E19 Explain the main biochemical reactions by applying the principles of chemical kinetics and thermodynamics.
E20 Relate the overall functioning of the organism with the basic mechanisms at the cellular and molecular level.
E21 Apply computational and data processing techniques for the integration of physical, chemical and biological concepts and data for the description and/ or prediction of the activity of a substance in a given context.


Basic statistics, probabilistic, genomics and alignement theory are needed to understand the underlying knowledge of phylogenetics. Programming skills in R and Python are mandatory to understand the provided code.



The order of the units might vary depeding on the learning pace of the students. The content of the
units can be modify to adapt it to all the students.
It is highly recommended to the student to study, revise and practise the learnt algorithms before
starting with the next.
Unexpected events might change the subject, as resources disponibility, academic calendar or group
capacity, for these reasons cannot be considerated definitely closed.

Subject contents:

1 - Introduction to Phylogenetics
    1.1 - Introduction and terminology
    1.2 - Speciation. Tree topologies.
    1.3 - Theories
    1.4 - Applications
2 - Phylogenetics Analysis
    2.1 - Types of trees
    2.2 - Molecular markers
    2.3 - Multiple sequence alignment
    2.4 - Model evolution of nucleotides
    2.5 - Model evolution of amino acids
    2.6 - Model selection
    2.7 - Tree building method
    2.8 - Assessing tree reliability
    2.9 - Available softwares
3 - Building algorithms based on distances. Neighbour joining.
4 - Building algorithms based on distances. Unweighted pair group method with arithmetic mean.
5 - Building algorithms based on distances. Maximum parsimony.
6 - Building algorithms based on probabilistic approaches. Maximum likelihood.
7 - Building algorithms based on probabilistic approaches. Bayesian inference.
8 - Assessing tree reliability.
9 - Genome complexity and phylogenetics analysis.

Subject planning could be modified due unforeseen circumstances (group performance, availability of resources, changes to academic calendar etc.) and should not, therefore, be considered to be definitive.


Teaching and learning methodologies and activities applied:

This subject is based on active participation, for which different teaching and learning methodologies will be applied: master classes, problem-based learning and case-study learning.

Student work load:

Teaching mode Teaching methods Estimated hours
Classroom activities
Master classes 20
Other theory activities 2
Practical exercises 20
Practical work, exercises, problem-solving etc. 20
Individual study
Tutorials 6
Individual study 40
Individual coursework preparation 10
Project work 15
Research work 10
Recommended reading 7
Total hours: 150


Calculation of final mark:

Written tests: 20 %
Individual coursework: 20 %
Final exam: 50 %
Case-study: 10 %
TOTAL 100 %

*Las observaciones específicas sobre el sistema de evaluación serán comunicadas por escrito a los alumnos al inicio de la materia.


Basic bibliography:

PEVSNER, Jonathan. Bioinformatics and functiona genomics. Oxford: John Wiley and Sons, 2015.
DURBIN, Richard. Biological sequence analysis. Cambridge: Cambridge University Press, 1998
BLEIDORN, Christoph. Phylogenomics An Introduction. Gewerberstrasse: Springer. 2017

Recommended bibliography:

ROCHA Miguel. Bioinformatics Algorithms Design and Implementation in Python. London: Academic Press 2018
XIONG, Jin. Essential Bioinformatics. Cambridge: Cambridge University Press, 2006

Recommended websites:


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