[BioC] FIRST ANNOUNCEMENT: IV International Course on Microarray Data Analysis

Mon Nov 12 16:02:35 CET 2007

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 Investigación Príncipe 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&bandera=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., Tárraga 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, Götz S, García-Gómez JM, Terol J, Talón 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., Tárraga 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