Bioinformatics in genetics

Recommended Prerequisites

Knowledge of biology, biochemistry and statistics in the pre graduate cycle of courses in the scope of Biomedical Sciences .

Teaching Methods

Lecturing, discussing and resolving practical problems.

Learning Outcomes

Recognize the organization and difficulties associated to large genetic data sets;

Analyse tools for genomes alignment

Analyse tools for variant calls and data base anotation

Understand the need for data reduction in large genetic data sets;

Acquire the fundamentals of data reduction techniques;

Use software tools to build classification statistical models applied to the relation genotype/phenotype.

Work Placement(s)

No

Syllabus

Very large data sets.

Genomes alignment

Tools for variant calls and data base anotation

Data reduction methods. Principal component analysis. Clustering techniques.

Genomic wide association studies resorting to classification algorithms.

Head Lecturer(s)

Francisco José Santiago Fernandes Amado Caramelo

Assessment Methods

Assessment
The evaluation consists of a multiple-choice questions and/or others types : 100.0%

Bibliography

Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 1edition, May 2014

James, G.; Witten, D.; Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: With Applications in R Springer Publishing Company, Incorporated, 2014

Design, Analysis, and Interpretation of Genome-Wide Association Scans, Daniel O. Stram, Springer

Genome-Wide Association Studies and Genomic Prediction, Gondro, Cedric, van der Werf, Julius, Hayes, Ben (Eds.), Springer.