Understanding the information that yields from massive gene expression studies is a time-consuming task. Automated analysis methods facilitate the use of auxiliary information and can help in the understanding of the underlying biological processes.
In this course you will review the main approaches for the analysis of large-scale studies in the context of lists of candidate genes and associated biological hypothesis. The hands-on exercises will include the identification of biological processes related with the experimental results, and the determination of the predominant functional themes common to previously selected groups of genes. We will also explore tools for the extraction of the connections between genes, small molecules and diseases, and for the automatic exploration of the scientific publications with relevant associations to the experimental results.
To make more interesting the hands-on sessions, we invite you to bring your own data. The bioinformatics tools to be used in the course are all currently available at CNIO.

Teachers

Course Agenda

• 9:00 Introduction
• 9:15 Gene List Functional Enrichment (FatiGO)(Example)
• 10:45 Coffee break
• 11:15  Gene Set Enrichment Analysis (FatiScan and GSEA) (FatiScan files) (GSEA_p53example) (FatiScanGSEA files)
  
• 13:00 Lunch
• 15:00 Ingenuity Pathway Analysis 7.1 (IPA 7.1)
• 16:00 Coffee break
• 16:15 Navigating Pathway Databases and Literature  (PaLS, Gene list) (iHOP)