Bioinformatics and Statistical Genetics

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Specialization complementary (Data Science)
This subject has not requirements, but it has got previous capacities
Bioinformatics and Statistical Genetics


Person in charge

  • Gabriel Valiente Feruglio ( )

Weekly hours

Guided learning
Autonomous learning


Generic Technical Competences


  • 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.

Transversal Competences


  • 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.


  1. Introduce the student to the algorithmic, computational, and statistical problems that arise in the analysis of biological data.
    Related competences: CB6, CB7, CB9, CTR6, CEC1, CEC2, CEC3, CG3,
  2. Reinforce the knowledge of discrete structures, algorithmic techniques, and statistical techniques that the student may have from previous courses.
    Related competences: CB6, CB7, CB9, CTR6, CEC1, CEC2, CEC3, CG3,


  1. Introduction to bioinformatics
    Computational biology and bioinformatics. Algorithms in bioinformatics. Strings, sequences, trees, and graphs. Algorithms on strings and sequences. Representation of trees and graphs. Algorithms on trees and graphs.
  2. Phylogenetic reconstruction I
    Character-based phylogenetic reconstruction. Compatibility. Perfect phylogenies. Distance-based phylogenetic reconstruction. Additive trees. Ultrametric trees.
  3. Agreement of phylogenetic trees
    Partition distance. Triplets distance. Quartets distance. Transposition distance. Edit distance and alignment of phylogenetic trees.
  4. Phylogenetic reconstruction II
    Phylogenetic networks. Galled trees. Tree-child networks. Tree-sibling networks. Time consistency of phylogenetic networks.
  5. Agreement of phylogenetic networks
    Path multiplicity distance. Tripartition distance. Nodal distance. Triplets distance. Edit distance and alignment of phylogenetic networks.
  6. Phylogenetic reconstruction III
    Mutation trees. Clonal trees. Clonal deconvolution.
  7. Phylogenetic and taxonomic reconstruction
    Phylogenies and taxonomies. Classification of metagenomic samples. Agreement of classifications.
  8. 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.
  9. Hardy-Weinberg equilibrium
    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.
  10. Linkage disequilibrium
    Definition of linkage disequilibrium (LD). Measures for LD. Estimation of LD by maximum likelihood. Haplotypes. The HapMap project. Graphics for LD. The LD heatmap.
  11. Phase estimation
    Phase ambiguity for double heterozygotes. Phase estimation with the EM algorithm. Estimation of haplotype frequencies. R-package haplo.stats.
  12. Population substructure
    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.
  13. 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.
  14. Family relationships and allele sharing
    Identity by state (IBS) and Identity by descent (IBD). Kinship coefficients. Allele sharing. Detection of family relationships. Graphical representations.


Activity Evaluation act

Teaching methodology

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.

Evaluation methodology

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.




Web links

Previous capacities

Basic knowledge of algorithms and data structures.
Basic knowledge of statistics.
Basic knowledge of the Python programming language.
Basic knowledge of the R programming language.