Description of contents. At the end of the course, students should be able to:
a. Understand and use massive technologies (omics) in biomedicine.
b. Design algorithms for filtering and analyzing large amounts of data generated by these technologies, focusing on the problem to be analyzed.
a. Introduction to complex diseases, whose studies and diagnosis are the subject of omic technologies. Personalized medicine.
b. Application of next generation sequencing (NGS) tools and microarrays for the diagnosis of genetically heterogeneous pathologies.
- NGS: exome sequencing vs gene panels.
- NGS: RNA-Seq, small RNA-Seq. Characterization of pathological molecular signatures.
- Bioinformatics: pipelines and Big-Data Analysis in complex pathologies.
- SNPs arrays: applications in pharmacogenomics and hereditary diseases.
- CGH microarrays in genetic diagnosis
- Methylation arrays in cancer diagnosis
c. Advance proteomic applications (functional, structural and expression) in Biomedicine for the development of drugs, the identification of tentative pharmacological targets, diagnosis, the development of vaccines and the search for biomarkers and molecular signatures involved in signal transduction and pathologies.
d. Metabolomics applications for the identification of ¿metabolic fingerprints¿, in order to differentiate normal and pathological situations (cancer, neurological and metabolic diseases, etc.). Identification of metabolites in response to therapeutic or nutritional interventions. Metabolomics in the development of new drugs, organ transplant and identification of population risk factors.
e. Use of these technologies: practical examples.