Bioinformatics and Statistical Genetics

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Credits
6
Types
  • MIRI: Specialization complementary (Data Science)
  • MDS: Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS;EIO
Bioinformatics and Statistical Genetics

Teachers

Person in charge

  • Gabriel Valiente Feruglio ( )

Others

  • Marta Janira Castellano Palomino ( )

Weekly hours

Theory
1
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
7

Competences

Transversal Competences

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Third language

  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

Basic

  • 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 knowledge 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.
  • CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.

Generic Technical Competences

Generic

  • CG4 - Design and implement data science projects in specific domains and in an innovative way

Technical Competences

Especifics

  • CE1 - Develop efficient algorithms based on the knowledge and understanding of the computational complexity theory and considering the main data structures within the scope of data science
  • CE2 - Apply the fundamentals of data management and processing to a data science problem
  • CE5 - Model, design, and implement complex data systems, including data visualization
  • CE6 - Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
  • CE9 - Apply appropriate methods for the analysis of non-traditional data formats, such as processes and graphs, within the scope of data science

Objectives

  1. Introduce the student to the algorithmic, computational, and statistical problems that arise in the analysis of biological data.
    Related competences: CB10, CB6, CB7, CT4, CT5, CE5, CE6, CE9, CG4,
  2. Reinforce the knowledge of discrete structures, algorithmic techniques, and statistical techniques that the student may have from previous courses.
    Related competences: CT5, CE1, CE2, CE9,

Contents

  1. Introduction to bioinformatics
    Combinatorial introduction to molecular biology.
  2. ILP and SAT in bioinformatics
    Brief Introduction to ILP. Solving an integer linear program. AMPL. Brief introduction to SAT. Solving a SAT formulation. PySAT.
  3. Longest common substring and subsequence
    Longest common substring. ILP and SAT models. Longest common subsequence. RNA folding. ILP and SAT models.
  4. Shortest common superstring and supersequence
    Shortest common superstring. Genome assembly. ILP and SAT models. Shortest common supersequence. ILP and SAT models.
  5. Sequence alignment and multiple sequence alignment
    Sequence alignment. Edit distance. ILP and SAT models. Multiple sequence alignment. ILP and SAT models.
  6. Other string selection problems
    Closest string. ILP and SAT models. Closest substring. ILP and SAT models.
  7. 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.
  8. 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.
  9. 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.
  10. Phase estimation
    Phase ambiguity for double heterozygotes. Phase estimation with the EM algorithm. Estimation of haplotype frequencies. R-package haplo.stats.
  11. 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.
  12. 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.
  13. 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 Bioinformatics


Objectives: 1 2
Week: 9 (Outside class hours)
Theory
0h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
15h

Final exam Statistical Genetics


Objectives: 1 2
Week: 18 (Outside class hours)
Theory
0h
Problems
0h
Laboratory
3h
Guided learning
0h
Autonomous learning
15h

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

For the first half (Bioinformatics), students are evaluated in a mid-term exam, in which they model and solve new string problems in Bioinformatics using ILP and SAT. In the second half (Statistical Genetics), 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:

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.