Bioinformatics in Biomedicine


1. Understand the fundamental concepts behind experimental design, data handling and interpretation of results.

2. Obtain practical knowledge in the application of R-based bioinformatics tools.

3. Understand the importance of bioinformatics in the biomedical field.

General characterization





Responsible teacher

Ana Rita Fialho Grosso, João Manuel Gonçalves Couceiro Feio de Almeida


Weekly - 4

Total - 91

Teaching language



Basic knowledge of Molecular Genetics and Statistics.


Gentleman, R. (2008). R Programming for Bioinformatics. Chapman & Hall/CRC press, 328 pp.
Zar, J.H. (2007). Biostatistical Analysis (5th Ed.). Prentice-Hall, 960 pp.

Teaching method

A limited number of lectures is scheduled. Student autonomous studying is encouraged, as well as scientific debate and critical reasoning. Exercises with hands-on will be carried out in practical sessions using personal computers.

Evaluation method

The evaluation consists in one individual test and a two group projects, contributing respectively for 40% (test) and 30%+30% (group projects) of the final grade. The execution of each evaluation element is mandatory to obtain approval via continuous assessment, with a minimum grade of 9 on each.

The test is individual, without consulting and performed during 1 hour.

The group project consists in the data analysis, interpretation and integration of results in order to answer a biological question, with further preparation of a scientific manuscript.

The exam precludes both theory and practice. As such, grades from groupt projects will only be considered for continuous assessment.

Subject matter


1. Experimental design

2. Descriptive statistics and exploratory analyses

3. Statistical inference 1: comparing means and medians

4. Statistical inference 3: analysis of variance

5. Statistical inference 3: data analysis

6. Analysis of large-scale data

7.  Association between two of more variables

8.  Multivariate analysis

9. Identification of transcriptome alterations

10. Functional Annotation and Analysis

11. Survival Analysis


Programs where the course is taught: