Program

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: Programming in Bioinformatics (6 ECTS)     Description This modules applies the core tools and basic techniques for development of bioinformatics.     Contents: 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 Bioconductor   Teaching guide

Module 2: Core Bioinformatics (12 ECTS)     Description 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.    Contents: Bioinformatics formats and databases Statistics and stochastic processes for sequence analysis Sequence alignments Gene and control region finding Genomics Molecular evolution and Phylogeny Structural Bioinformatics:                  Structure, Interactions, PDB, Folds, SCOP, PFAM                  PyMol                  Homology modeling, energy minimization, MD                  Docking Proteomics Interactomics System biology Workflow management APIs and Web services Web developing  Software Engineering   Teaching guide

Module 3: Genomics (12 ECTS)     Description 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.   Contents: 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 Epigenomic Systems genetics Omics data integration Applied genomics   Teaching guide

Module 4: Structure and Function of Proteins and Drug Design (12 ECTS)     Description 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.    Contents:   Chemoinformatics 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)   Description 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.   Contents:  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 Teaching Guide

Module 6: Professional Practice (15 ECTS)     Description The Professional Practice (15 ECTS) is a compulsory module where students perform 365 hours of practice in research centers, companies or institutions, public or private. In this module, students have the opportunity to consolidate their training by integrating all the knowledge learned, applying and improving the professional skills and the abilities acquired throughout the course.   Official Teaching Guide Collaborating Center and Entities

Module  7: Master Thesis (15 ECTS)     Description The Master’s Thesis (MT) is an autonomous and individually research work that gives the student the opportunity to focus on some of the topics dealt with in the academic modules of the Master, as well as to integrate all together the capacities and competences achieved along the Master’s degree. The MT must show that the student has acquired the capacity to carry out sound and rigorous academic research. It should present an original argument that is carefully documented from primary and secondary sources. The student must do the work for oneself with the guide of an advisor. Master's Thesis Guide

 

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MSc in Bioinformatics
Master in Bioinformatics
Faculty Biosciences, University Autonoma Barcelona (UAB)
http://MScBioinformatics.uab.cat