Microarray Data Analysis using GEPAS and Babelomics

   Course date: May 6th -May 8th 2009


Joaquín Dopazo has a master degree in Chemistry (Universidad de Valencia) and a PhD in Biology (Universidad de Valencia). He is the head of the Department of Bioinformatics at the CIPF (Valencia). In previous appointments he was responsible for Bioinformatics units at the CNIO (Madrid) and at GlaxoWellcome SA (Madrid). He has supervised several large scale projects of software development, as the GEPAS or the Babelomics ( where more than 500 microarray experiments are daily analysed. He has more than one hundred papers published in international peer reviewed journals and has edited a book on genomic data analysis. His main interests include functional and comparative genomics.

Ignacio Medina did his Biochemistry degree at the Valencia University and the Computer Science degree at the Polytechnic University of Valencia. For two years he worked as a researcher in the Medicine Faculty of Valencia University, and for another two years he worked as a researcher in the Artificial Intelligence Department of Polytechnic University of Valencia. Currently he is a bioinformatician in the Bioinformatics Department of the Centro de Investigacion Principe Felipe in Valencia (Spain) where he is involved in the GEPAS, Babelomics ( and Pupasuite ( projects. His research interests include microarray data analysis, predictors, SNPs as well as functional interpretation of genomic data.

Francisco García did his Statistics degree at the Universidad of Valencia. For two years he worked as a statistician in the Health Departament of Government of Catalonia (Spain). Currently he is a statistician in the Bioinformatics Department of the Centro de Investigacion Principe Felipe in Valencia (Spain) where he is involved in the GEPAS project. His research interests include microarray data analysis as well as functional interpretation of genomic data.

Affiliation Centro de Investigacion Principe Felipe, Valencia, ES

Course description:

DNA microarrays constitute, no doubt, a paradigm among post-genomic technologies, which are characterised for producing large amounts of data, whose analysis and interpretation is not trivial. Microarray technologies allows querying living systems in a completely new way, but at the same time present new challenges in the way hypotheses must be tested and results have to be analysed.

Since the first papers published in the latest nineties the number of questions that have been addressed through this technique have both increased and diversified. Initial interest was focused on genes coexpressing across sets of experimental conditions, implying essentially the use of clustering techniques. More recently, however, the interest has switched to find genes differentially expressed among distinct classes of experiments, or correlated to diverse parameters. There is also much interest in robust methods for building predictors of clinical outcomes. Also, CGH-arrays (Albertson and Pinkel, 2003) are recently becoming an alternative for studying the relationship between chromosomal alterations affecting to copy number (which are behind many diseases) and gene expression. In addition, there is also a clear demand for methods that allow automatic transfer of biological information to the results of microarray experiments.

This course covers the state-of-the-art in the above mentioned topics, which are of major relevance in today's gene expression data analysis. Through sessions of theory and practical examples, the students will acquire the experience necessary to address scientific questions to gene expression array datasets and solve them. Special attention will be devoted to important (despite frequently ignored) aspects in microarray data analysis, such as multiple testing or functional annotation. The course is designed to be a mixture of theoretical and practical sessions. The latter will require some familiarity with the use of web-based tools and knowledge of basics notions of statistics. Practical sessions will be carried out using the GEPAS (Herrero et al., 2003, 2004; Vaquerizas et al., 2005) environment, an integrated web tool for microarray data analysis, and the Babelomics suite (Al-Shahrour et al., 2005) for functional annotation of genome-scale experiments.

Course Pre-requisites:

basic knowledge in Molecular Biology and Statistics.

Detailed Program

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:  April 8th 2009