Bioinformatics and Statistical Genetics

Credits
6
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
EIO;CS
Bioinformatics and Statistical Genetics

Teachers

Person in charge

Weekly hours

Theory
1
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
105

Objectives

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

Contents

  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.

Activities

Activity Evaluation act



Final exam



Theory
3h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
30h

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.

Bibliography

Basic

Complementary

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.