Dear Colleague, Would you be so kind of giving diffusion to this course on microarray data analysis that we are organizing at the CIPF, in Valencia (Spain)? Thanks ********************************************************************** ********* IV International Course on Microarray Data Analysis Valencia, Spain. 10th- 14th March, 2008 http://bioinfo.cipf.es/docus/courses/coursesCIPF/MDA2008.html ********************************************************************** ********** Joaquin Dopazo, Fatima Al-Shahrour, David Montaner and Ana Conesa Department of Bioinformatics and Functional Genomics Node (INB) Centro de Investigaci?n Pr?ncipe Felipe (CIPF) 46013, Valencia, Spain http://bioinfo.cipf.es http://www.gepas.org http://www.babelomics.org http://bioinfo.cipf.es/blast2go/ ********************************************************************** *********** 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 our results ought 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 co-expressing 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 and to interpret them at the light of the biological knowledge. Recently, new methods of analysis have been proposed that directly address hypothesis on modules of genes functionally related that have demonstrated to be superior to the classical one-gene-at-a-time approaches (Mootha et al., 2003; Al-Shahrour et al., 2005, 2007) 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 (although not always took into account) aspects in microarray data analysis, such as multiple testing or functional profiling. In addition, some theoretical lessons on basic statistics will be included as part of the programme. Finally, for the bravest and those who want to go in more depth into analysis possibilities, the last day a short course on Bioconductor (Gentleman et al., 2004) will be taught. 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; Montaner et al., 2006) environment, an integrated web tool for microarray data analysis, and the Babelomics suite (Al-Shahrour et al., 2005b, 2006, 2007) for functional profiling of genome-scale experiments. and the Blast2GO suite (Conesa et al., 2005), a set of tools for the high-throughput functional annotation and analysis of uncharacterized sequences. The course will be held the week before fallas, one of the most popular and impressing folkloric festivals in Spain which ends the 19th March when all the fallas are burnt in an apotheosis of fireworks. So you can use this opportunity to enjoy one of the most exceptional holiday festival in the world. See more in: http://www.fallas.com/contenido.asp?seccion=museo&tema=historia&bander a=en See information on the Bioinformatics Department courses in: http://bioinfo.cipf.es/docus/courses/courses.html *Programme* *Day 1 * -------- 9.30 ? 11.30. Introduction Structure of the course. Why microarrays? Pre- and post-genomics hypothesis testing: a note of caution. Design of experiments. Data preprocessing and normalization. Unsupervised analysis (clustering). Supervised analysis (gene selection, predictors). Functional profiling. 12.00?13.30. Normalization (theory and practical exercises) Getting rid of unwanted variability from sources other than the experimental conditions assayed. Methods for Affymetrix, two-colour and one-colour microarrays 13.30-14.30 Lunch 14.30-16.00 Gene selection (theory) Methods for selecting genes differentially expressed among two or more experimental conditions, correlated to a continuous variable or correlated to survival. How to deal with the multiple-testing problem. 16.30-18.00 Gene selection (practical exercises) *Day 2* ------- 9.30-10.30 Basic statistical methods Some theory on basic statistical methods. 11.00-13.30 Predictors (theory and practical exercises) Gene selection in the context of class prediction. How to deal with the selection bias problem. Different methods for class prediction. Estimating the error of classification. Interpretation of confusion matrices. 13.30-14.30 Lunch 14.30-16.00 Clustering (theory) Different clustering methods: hierarchical clustering, SOM, SOTA and k-means. Pros and cons. Measures of cluster quality. Cluster visualisation. 16.30-18.00 Clustering (practical exercises) *Day 3* -------- 9.30-10.30 Basic statistical methods Some theory on basic statistical methods. 11.00-13.30 Functional profiling of experiments Understanding the biological roles played by the genes in the experiments. Using different types of information for the functional profiling of microarray experiments: gene ontology, InterPro motifs, transcription factor binding sites, gene expression in other experiments, text-mining, etc. New trends in the analysis of microarray data: testing pathway-based or function-based hypothesis. 13.30-14.30 Lunch 10.30-12.30 Functional profiling. The Babelomics suite Different methods for functional profiling of experiments from the Babelomics suite: FatiGO/FatiGO+, Marmite (using text-mining) or TMT (pre-tabulated gene expression results). Methods for finding blocs of functionally-related genes differentially expressed (GSEA, FatiScan). *Day 4* -------- 9.30-10.30 Basic statistical methods Some theory on basic statistical methods. 11.00-12.00 Array-CGH Estimation of copy number in chromosomal aberrations. Joint study of copy number, gene expression and functional profiling. 12.00-13.30 Introduction to the programmable GEPAS interface Using the visual programming interface of GEPAS to build up pipelines of analysis. 13.30-14.30 Lunch 14.30-17.30 Exercises Do a complete practical exercise using the tools you learned. 17.30-18.00 Concluding remarks and final questions *Day 5* -------- 9.30-13.30 A primer on automatic annotation of unknown sequences. 13.30-14.30 Lunch 14.30-18.00 Blast2GO ---------------------------------------------------------------------- -------------------------------------------------------- */References/* ? Albertson, D.G. and Pinkel, D. Genomic microarrays in human genetic disease and cancer. Hum Mol Genet, 2003 12 Spec No 2, R145-52 ? Al-Shahrour, F., Diaz-Uriarte, R. & Dopazo, J. Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics. 2005;21: 2988-2993 ? Al-Shahrour F, Minguez P, Vaquerizas JM, Conde L, Dopazo J: BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Res 2005b, 33:W460-464 ? Al-Shahrour F., Minguez P., T?rraga J., Montaner D., Alloza E., Vaquerizas J.M., Conde L., Blaschke C., Vera J. and Dopazo J. BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments Nucl Acids Res., 2006, 34: W472-W476 ? Al-Shahrour F, Arbiza L, Dopazo H, Huerta J, Minguez P, Montaner D, Dopazo J. From genes to functional classes in the study of biological systems. 2007 BMC Bioinformatics 8:114 ? Conesa A, G?tz S, Garc?a-G?mez JM, Terol J, Tal?n M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. 2005 Bioinformatics, 21(18), 3674-3676. ? Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol, 2004, 5, R80 ? Herrero J, Al-Shahrour F, Diaz-Uriarte R, Mateos A, Vaquerizas JM, Santoyo J, Dopazo J: GEPAS: A web-based resource for microarray gene expression data analysis. Nucleic Acids Res 2003, 31:3461-3467. ? Herrero J, Vaquerizas JM, Al-Shahrour F, Conde L, Mateos A, Diaz-Uriarte JS, Dopazo J: New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Res 2004, 32:W485-491 ? Montaner D., T?rraga J., Huerta-Cepas J., Burguet J., Vaquerizas J.M., Conde L., Minguez P., Vera J., Mukherjee S., Valls J., Pujana M., Alloza E., Herrero J., Al-Shahrour F., Dopazo J. Next station in microarray data analysis: GEPAS Nucl Acids Res., 2006, 34: W486-W491 ? Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E et al: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003, 34(3):267-273. ? Vaquerizas JM, Conde L, Yankilevich P, Cabezon A, Minguez P, Diaz-Uriarte R, Al-Shahrour F, Herrero J, Dopazo J: GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Res 2005, 33:W616-620