CG3 - Capacity for mathematical modeling, calculation and experimental designing in technology and companies engineering centers, particularly in research and innovation in all areas of Computer Science.
CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.
CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
Technical Competences of each Specialization
CEC1 - Ability to apply scientific methodologies in the study and analysis of phenomena and systems in any field of Information Technology as well as in the conception, design and implementation of innovative and original computing solutions.
CEC2 - Capacity for mathematical modelling, calculation and experimental design in engineering technology centres and business, particularly in research and innovation in all areas of Computer Science.
CEC3 - Ability to apply innovative solutions and make progress in the knowledge that exploit the new paradigms of Informatics, particularly in distributed environments.
Introduce the student to the algorithmic, computational, and statistical problems that arise in the analysis of biological data.
Reinforce the knowledge of discrete structures, algorithmic techniques, and statistical techniques that the student may have from previous courses.
Introduction to Bioinformatics
Introduction to the R language. Computational linear algebra. Numerical optimization. Bioinformatics with R.
Linear programming and integer linear programming
Solving easy and hard feasibility and optimization problems in Bioinformatics. Linear programming and integer linear programming in R.
The longest common substring problem
The longest common substring problem: Finding consensus among DNA sequences. Integer programming formulations.
The shortest common superstring problem
The shortest common superstring problem: Assembling short DNA sequence reads. Integer programming formulations.
The closest and the farthest string problems
The closest and the farthest string problems: Finding patterns that occur, or do not occur, in each string in a given set of DNA sequences. Integer programming formulations.
The closest and the farthest substring problems
The closest and the farthest substring problems: Finding short strings that are, or are not, enriched in each string in a given set of DNA sequences. Integer programming formulations.
Other string selection problems
Other string selection problems: The Close to Most and the Far from Most Strings problems. Integer programming formulations.
Introduction to statistical genetics
Basic genetic terminology. Population-based and family-based studies. Traits, markers and polymorphisms. Single nucleotide polymorphisms and microsatellites. R-package genetics.
Hardy-Weinberg law. Hardy-Weinberg assumptions. Multiple alleles. Statistical tests for Hardy-Weinberg equilibrium: chi-square, exact and likelihood-ratio tests. Graphical representations. Disequilibrium coefficients: the inbreeding coefficient, Weir's D. R-package HardyWeinberg.
Definition of linkage disequilibrium (LD). Measures for LD. Estimation of LD by maximum likelihood. Haplotypes. The HapMap project. Graphics for LD. The LD heatmap.
Phase ambiguity for double heterozygotes. Phase estimation with the EM algorithm. Estimation of haplotype frequencies. R-package haplo.stats.
Definition of population substructure. Population substructure and Hardy-Weinberg equilibrium. Population substructure and LD. Statistical methods for detecting substructure. Multidimensional scaling. Metric and non-metric multidimensional scaling. Euclidean distance matrices. Stress. Graphical representations.
Genetic association analysis
Disease-marker association studies. Genetic models: dominant, co-dominant and recessive models. Testing models with chi-square tests. The alleles test and the Cochran-Armitage trend test. Genome-wide assocation tests.
Family relationships and allele sharing
Identity by state (IBS) and Identity by descent (IBD). Kinship coefficients. Allele sharing. Detection of family relationships. Graphical representations.
All classes consist of a theoretical session (a lecture in which the professor introduces new concepts or techniques and detailed examples illustrating them) followed by a practical session (in which the students work on the examples and exercises proposed in the lecture). On the average, two hours a week are dedicated to theory and one hour a week to practice, and the professor allocates them according to the subject matter. Students are required to take an active part in class and to submit the exercises at the end of each class.
Students are evaluated during class, and in a final exam. Every student is required to submit one exercise each week, graded from 0 to 10, and the final grade consists of 50% for the exercises and 50% for the final exam, also graded from 0 to 10.