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The MSc in Bioinformatics is a full time one year course, corresponding to 60 ECTS. The master program consists of seven modules (depicted in the diagram below). During the first semester, from October to February, students take all theoretical classes (both compulsory modules and one elective module) and on the second semester, from March to July, they perform their Professional Practices and their Master's Thesis (module 6 and 7).

  Module 1 Module 2 Module 3 Module 4 Module 5 Module 6 Module 7 Scheme of Modules

 Module 1: Programming in Bioinformatics (6 ECTS)



This modules applies the core tools and basic techniques for development of bioinformatics.  


Computational Thinking applied to Bioinformatics
Linux Environments
Bioinformatics Databases: MySQL, database creation and management. Biological databases
Programming languages (Perl and Python)
Data Analysis with R

  Teaching guide


 Module 2: Core Bioinformatics (12 ECTS)



This module displays the development of most common tools and bioinformatics resources used in Omics research. The aim is to provide our students the necessary background to enable them to apply bioinformatics in the multiples fields of the Bioinformatics research. 


Bioinformatics formats and databases
Statistics and stochastic processes for sequence analysis
Sequence alignments
Gene and control region finding
Molecular evolution and Phylogeny
Structural Bioinformatics:
                 Structure, Interactions, PDB, Folds, SCOP, PFAM
                 Homology modeling, energy minimization, MD

System biology
Workflow management
APIs and Web services
Web developing 
Software Engineering

  Teaching guide


 Module 3: Genomics (12 ECTS)



The technological capacity to generate massive genomic data grows at a relentless pace without parallel growth of the bioinformatics expertise to deal with human, animal, microorganism and plant genomes. The purpose of this module is to provide the knowledge and technical skills which are required to successfully meet the current challenges of genomic analysis.


Genome sequencing projects
Next generation sequencing (NGS)
NGS data analysis
Genome assembly
Genome annotation
Functional Analysis
Genome browsers and databases
Genome variation
Association and GWA studies
Expression analysis
Systems genetics
Omics data integration
Applied genomics

  Teaching guide


 Module 4: Structure and Function of Proteins and Drug Design (12 ECTS)


The number of three-dimensional structures of proteins deposited in the Protein Data Bank (PDB) has grown exponentially in the last years because of the improvement in X-ray and NMR technologies. This module introduces the theoretical and practical knowledge of the computational techniques used to analyze, to characterize and to visualize protein structures and their interactions with other proteins, peptides or ligands. 



Computational chemistry / Molecular modeling
Database design
Structure-Activity relationships
Pharmacophore modeling
Similarity searches

  Structural bioinformatics

Homology modeling and fold recognition
Ab initio modeling
Molecular dynamics
Protein-protein docking
Protein-ligand docking and Virtual High-Throughput Screening
Key elements of protein families (GPCR and Kinases)

  Teaching guide


 Module 5: High Performance Computing and Big Data Analytics (12 ECTS)


The immense outpouring of molecular data precipitated by Omics technologies require efficient statistical analysis methods combined with the use of modern computational power.

This module aims to provide students with the necessary knowledge and skills (1) to implement performance engineering approaches into modern computing platforms and (2) to perform statistical analyses of Big Data. By following a problem-oriented approach, students will get insight about efficient computational algorithms, methods and platforms and the statistical methods to be applied to challenging bioinformatics problems dealing with Big Data.


 Modern Computer Architecture

  • General-Purpose and specialized processor architecture
  • Memory hierarchy
  • Cluster systems
  • Cloud infrastructures
  • System Middleware and Programming Frameworks

Advanced Programming Models

  • Shared-memory and distributed parallel programming (OpenMP, MPI…)
  • Workflow composition tools (Galaxy, Python…) 
  • Principles of performance engineering (tools and methods)
  • Performance engineering applied to common bioinformatics algorithms and tools (genome indexing, read alignment…).

Big Data Analytics

  • Big Data analytics platforms and tools (Hadoop MapReduce, Apache Spark…)
  • Theory and tools of advanced statistics in Big Data analytics (dimensionality reduction, variable selection and Spark) 
  • Machine learning theory and algorithms. Applications in Bioinformatics
  • Predictive modelling: data mining, model evaluation and validation
  • Data classification: naïve Bayes and decision trees learning
  • Association rule learning
  • Clustering analysis: k-means algorithm
  • Graph Theory for Big Data


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MSc in Bioinformatics
Master in Bioinformatics
Faculty Biosciences, University Autonoma Barcelona (UAB)

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MSc in Bioinformatics 

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