Introducing students to translational research in genomics and the methods used within those studies.

After the completion of this course, students are expected to be able to:

1. Use freely available databases of medical data and data obtained through high-throughput trials, understand the different types and formats of data, and integrate data.
2. Use computational and statistical methods in conducting research into infectious, hereditary and complex diseases and tumour diseases.
3. Use different types of ontologies for the purpose of analysing gene enrrichment.
4. Understand methods for analysing text and using literature databases in translational research.

1. Introduction to Translational Research.
2. Electronic health information. Biobanks and health databases. Ethical and security issues.
3. Databases obtained from high-throughput experiments. Integration of different types of data.
4. Computer genomics of infectious diseases. Diagnosis and characterization of infectious diseases by high-throughput methods. Modelling infectious diseases.
5. Computational genomics of hereditary diseases.
6. Computational genomics of complex diseases. Whole-genome association studies. Analysis of networks and pathways involved in the emergence of complex diseases.
7. Tumour genomics. Large-scale projects aimed at characterizing different types of tumours. Determination of mutations based on data generated by next-generation sequencing.
8. Gene ontologies and disease ontologies. Representation analysis.
9. Computational methods for determining priority genes involved in the onset of disease.
10. Computational Pharmacogenomics. Drug discovery and drug resistance genomics.
11. Text analysis in translational genomics. Literature databases. Natural language processing.
12. Personalized medicine. Patient stratification methods