This course will explore state-of-the-art knowledge and experiences to better understand complex biological systems. The main objective is to provide participants with a solid foundation on the current bioinformatics approaches and tools to better address specific biological questions using OMIC data.
We will introduce knowledge databases for functional annotation of model and non-model organisms. Then, we will discuss methods for over-representation analysis and gene-set enrichment analysis (e.g. biological pathways) and their application.
Participants will also become familiarized with the main concepts of multivariate methods for the exploration, variable selection and data integration of 2 or more data sets. Dimensionality reduction methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), multiple factor analysis (MFA) and independent component analysis (ICA) will be compared.
We will discuss the key aspects of network theory applied to biological networks and use them to visualize, explore and summarize relevant relationships among OMIC datasets.