1st YEAR

SUBJECT 1: GENOME BIOINFORMATICS

Course

ECTS credits

Term

Description

PGB.Principles of Genome Bioinformatics

5

1st

Foundations of protein and DNA sequence analysis and next generation sequencing techniques. Core topics: sequence alignment, sequence similarity search in databases, genome annotation, sequence motifs, high throughput sequencing, and quantification of gene expression levels.

Genomes and Systems

5

1st

Genome evolution in the context of NGS data. The Human Genome Project and its new annotations and its implications for biology and medicine. Mutational mechanisms of genomes. Medical genetics and treatment of genetic diseases.  

IEO.Extraction of Information Technologies "OMICS"

5

3rd

Introduction to the analysis of data obtained from high-throughput molecular profiling assays, such as DNA-seq or RNA-seq, using R and Bioconductor. 

The topics covered span a full data analysis cycle, including data acquisition, quality assessment, inference of molecular changes and functional analysis.

SUBJECT 2: MOLECULAR STRUCTURE AND FUNCTION

Course

ECTS credits

Term

Description

SBI. Structural Bioinformatics

5

2nd

The main goal of the course is to learn the basic concepts on the structure of macromolecules, more specifically of proteins. Also the principles of crystallography are given in order to gain a deeper understanding on the 3-dimensional data. The main features relating sequence and protein fold will be used on the prediction of secondary and tertiary structure of proteins and on its evaluation. Finally, the relationship between sequence/structure and function of proteins will be analyzed.

CAD. Computer-Assisted Drug Discovery

5

3rd

In modern drug discovery and development, computational methods play a central role. They are applied in the search of novel targets as well as at different stages of hit and lead finding and optimization.

In this course we aim to provide the student with an overview of the most important computational techniques, to put them in a context and illustrated by means of examples and practical sessions.

MSI. Molecular Simulations

5

3rd

Concepts of molecular simulation and its application to computer-assisted drug discovery and design. Introduction to statistical mechanics. Concepts of potential energy surface and free energy. 

SUBJECT 3: BIOMEDICAL INFORMATICS

Course

ECTS credits

Term

Description

BCO. Elements of Biocomputing

5

1st

The course is aimed at introducing the use of information technologies and computational approaches in biomedical research. The course is centered in the identification and understanding of the problems that can be tackled by computational approaches in a biomedical research project. 

The intended training includes the acquisition of concepts and skills to find data and information over the public biomedical databases, the execution of basic analysis on high-throughput biomedical data, the functional annotation of mutations, and basic concepts in system biology (e.g. mapping of genes and proteins to interaction networks and network analysis). A practical course, with practical exercises using publicly accessible resources about real data.

GPA. Applied Genomics: Genome-Phenome Analysis for Human Health

5

2nd 

This subject focuses on the conceptual underpinnings and the bioinformatics tools that are used to learn about the genetic architecture of human interindividual phenotypic differences. The course is about linking genotypes and phenotypes. An emphasis is made on novel methods used to ascertain the causal genetic variants associated with complex diseases, which are currently being applied to study many diseases of great relevance for public health. Recent studies are used as illustrative examples.

This advanced subject aims to provide researchers & practitioners involved in genetic disease studies with (1) a comprehensive overview of concepts and experimental design in the study of human traits, with particular emphasis on quantitative traits and disease; and (2) up-to-date training in the use of the latest statistical methods and software for analysis of both, rare & complex diseases. The course provides a comprehensive overview of the statistical methods currently used to map disease susceptibility variants and genes in humans. Data collected from families will be considered, but the emphasis will be on data collected from populations. This includes both small-scale disease-specific studies and large-scale collaborative projects including those that can be used for analysis of multiple complex traits such as UK Biobank. The course will include an overview of the tools, standards and large-scale initiatives such as Europe’s ELIXIR  or the Global Alliance for Genomics and Health.

CSB. Computational Systems Biology

5

3rd

This course is centered in the understanding of the translation of a biological problem into a mathematical frame to construct models of metabolic or signaling networks that permit to answer relevant biological and biomedical questions. Modelling of networks of cellular processes will permit to understand its regulation and to design new therapeutic strategies in multifactorial complex diseases such as cancer.

SUBJECT 4: ELEMENTS OF PROGRAMMING

Course

ECTS credits

Term

Description

ALG. Introduction to Algorithmics

5

1st

This course will illustrate the most basic tools and programming concepts using as the main language shell scripting. Particular effort will be spent on how to use efficiently use GNU/Linux as programming environment. Basic algorithmic concepts will be also introduced, as well as how to handle data structures like trees and graphs. 

APA.Advanced Programming, Algorithms and Data Structures

This course will cover advanced concepts in algorithm design, data structure and programming. The course will focus on building the capacity to improve the computational and space efficiency of algorithms and the ability to translate these algorithms to efficient implementations.

Key concepts that are going to be introduced in the course are (i) [Algorithms] complexity theory, divide-and-conquer scheme, dynamic programming, greedy search, approximation algorithms; (ii) [Data structures] stacks, heaps, suffix trees and graphs (iii) [Programming] call by value vs reference, static vs dynamic typing, recursion, object-oriented programming, polymorphism and generic programming.

The course will involve several programming assignments and a final project to apply these advanced concepts in a practical and real-world setting.

PER. Introduction to PERL

5

1st

This course will introduce the basic elements of programming, using PERL as a vehicular language. PERL is a programming language commonly used in bioinformatics applications and, in general, for text processing, filtering, etc. Programming capabilities will be reinforced during this course.

DBW. Databases and Web Design

5

2nd

Development of web based applications and services. Building of data models for bioinformatics, implementation in SQL or noSQL databases, and usage through web applications.

PYT. Introduction to Python

5

2nd

The main goal of the course is to learn the basic concepts on programming with Python, and to learn how Python features can be used to deal with biological research data. The course is intended to provide students with the tools to create, understand and maintain complex software in a productive manner. Starting from basic programming procedural constructs, the students will be introduced to object oriented techniques to produce code which is clear, understandable and well documented.

SUBJECT 5: BASIC TOOLS IN BIOCOMPUTING

Course

ECTS credits

Term

Description

IBM. Introduction to Biomedicine

5

1st

This course will introduce the students to the basic principles of genetics, cell biology, molecular biology and main physiological processes. 

For the students with little background in biology, the course will cover the structural components of the cells, basic principles of cellular function: transport, metabolism, signaling, the main molecules that mediate these processes, and how tissues are organized and can lead to diseases.

BDA. Biomedical Data Analysis

5

1st

This course will introduce the students in how to use standard statistical methods to analyze Biomedical data, and focuses on practical implementation of different types of tools for statistical inference. 

After a general introduction on probability theory and statistical inference, an emphasis will be made on the most-common methods used to analyze multivariate data. Particular cases will be used as illustrative examples. The course will be focused on Frequentist Statistics, but basic overviews of Bayesian and Maximum Likelihood methods will be provided.

AST. Advanced Statistics

Advanced Statistics subject is intended to revise important concepts of statistics applied to biomedical and research Data. Students are expected to have already done at least 2 Statistical subjects during their degree or master and been proficient in R. During the subject we will deal with modelling, Bayesian Statistics and General Linear Models with emphasis on practical application in the biomedical field.

DMI. Data Mining and Data Integration in Biomedicine

Data Mining uses algorithms and computational paradigms that allow finding hidden patterns and regularities in the data, and performing prediction and forecasting. This course will cover a selection of commonly employed data mining techniques and will also cover the steps of data pre-processing. Given the importance of data integration to understand the complexity of biological systems, this course will also include examples of data integration. These topics will be illustrated using real case examples, such as mining scientific publication data, electronic health records data, and drug response data.

MAT. Elements of Mathematics

5

1st

The aim of the course is to provide the students with the basic tools needed for the correct progress in more advanced courses requiring algebra and calculus. 

The course focuses in making available a basic ground for the built of a wide while deep enough vision of mathematics in the bioinformatics disciplines.

2nd YEAR

SUBJECT 6 : ECONOMICAL AND SOCIAL ASPECTS OF RESEARCH

Course

ECTS credits

Term

Description

SCA. Science in Action

5

1st or 3rd

Principles of responsible conduct in research and components of good scientific practice. Knowledge of the structure and dynamics of the science-technology-innovation system, and indicators and evaluation methods. Introduction to the methodology by critical thinking; best practices will be studied based on analysis of cases. Discussions on current issues in political science and bioethics will also take place.

PRO. Design and Management of a Research Project

15

2nd and 3rd

This course aims at providing the students with some basic knowledge and skills related to creativity, innovation and entrepreneurship, and their relationship with research project management. 

The course focuses in entrepreneurial skills, data protection and intellectual property, basic financial concepts, how to create a business plan and communicate it, and how to build a personal brand. 

SUBJECT 7: RESEARCH IN BIOINFORMATICS

Course

ECTS credits

Term

Description

RBI. Research in Bioinformatics

1st/2nd/3rd

Development of a bioinformatics research project in an industrial, academic or clinical environment.

MP: Master Completion Project

Submission of a written paper based on the results obtained in the stage done in the Research in Bioinformatics subject, which will follow a peer-review process, and a public defense of the project and its results.