Bioinformatics and Experimental design and data analysis, Specialization Course

Experimental design and data analysis:-Uncertainty analysis,-Hypothesis testing,-Simple and Multiple linear regression,-Experimental design with two-level factorial experiments,-Analysis of variance (ANOVA),-Nonparametric methods,-IBM SPSS for statistical analysis.

Bioinformatics: introduction to the field of bioinformatics, covering genome sequencing methods, genome assembly, sequence alignment, evolution of genes sequences, protein structures, annotation of protein functions, and relevant tools and databases. Development of python programming skills using the Jupyter environment.

Experimental design and data analysis: The student has knowledge of the basic statistical models and methods used in science and technology. The student can interpret results of hypothesis testing, regression analysis, experimental design, analysis of variance and nonparametric methods. The student can use software for statistical calculations and analyzes.

Bioinformatics: The student understands genome sequencing methods and their limitations. The student can apply computational tools for genome assembly and annotation. The student can explain the principles and applications of sequence alignment. The student can extract information from bioinformatics databases and write python scripts to process and analyze multiple types of data. The student can combine data from different bioinformatics databases to analyze the function of genes and proteins.

Experimental design and data analysis: Lectures (24 h), exercises (8 h) and self-study (64 h).

Bioinformatics: Weekly lectures (8 x 3h) divided into theory (1h) and computer exercises (2h). Group project and home study (self-paced).