From aconesa from cipf.es Thu Jan 17 09:51:55 2008 From: aconesa from cipf.es (Ana Conesa) Date: Thu Jan 17 09:59:53 2008 Subject: [Arrays] SECOND ANNOUNCEMENT: IV International Course on Microarray Data Analysis Message-ID: <478F6B8B.5030002@cipf.es> Dear Colleague, We are pleased to announce the IV International Course on Microarray Data Analysis to be held at the CIPF (Valencia) in March 2008. The course provides theoretical and practical lectures on the use of user-friendly web tools for the analysis and interpretation of microarray experiments. See below for detailed information. Please forward this message to other colleagues who might be interested. Our apologies is this mail reaches you several times. Best regards, 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://www.blast2go.org ******************************************************************************* IV International Course on Microarray Data Analysis Centro de Investigaciones Príncipe Felipe Valencia, Spain. 10th- 14th March, 2008 http://bioinfo.cipf.es/docus/courses/coursesCIPF/MDA2008.html ******************************************************************************** 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 -- ------------------------------------------ Ana Conesa, PhD Bioinformatics Department Centro de Investigación Príncipe Felipe Avda. Autopista Saler, 16 46013 Valencia Spain http://bioinfo.cipf.es/aconesa ========================================== From editor from gene-quantification.info Mon Jan 21 02:30:51 2008 From: editor from gene-quantification.info (Editor www.Gene-Quantification.info) Date: Mon Jan 21 12:07:39 2008 Subject: [Arrays] The Reference in qPCR - Academic & Industrial Information Platform Message-ID: <3bc53ba7-c774-4c5b-93f5-2d76f579af4e@p69g2000hsa.googlegroups.com> The Reference in qPCR - Academic & Industrial Information Platform Dear researcher, dear Gene Quantification page reader, The Gene Quantification page - www.Gene-Quantification.info - describes and summarises all technical aspects involved in quantitative gene expression analysis using real-time qPCR & qRT-PCR. It presents a lot of applications, chemistries, methods, algorithms, cyclers, kits, dyes, analysis methods, meetings, workshops, and services involved. Commercial and academic institutions can present their qPCR tools right here => http://qpcrplatform.gene-quantification.info/ -------------------------------------------------------------------------------- The quantification strategy in real-time RT-PCR is the principal marker in gene quantification. Generally two strategies can be performed in real-time RT-PCR. The levels of expressed genes may be measured by absolute quantification or relative quantitative real-time RT-PCR. Absolute quantification relates the PCR signal to input copy number using a calibration curve, while relative quantification measures the relative change in mRNA expression levels. The reliability of an absolute real-time RT-PCR assay depends on the condition of 'identical' amplification efficiencies for both the native target and the calibration curve in RT reaction and in following kinetic PCR. Relative quantification is easier to perform than absolute quantification because a calibration curve is not necessary. It is based on the expression levels of a target gene versus a housekeeping gene (reference or control gene) and in theory is adequate for most purposes to investigate physiological changes in gene expression levels. The units used to express relative quantities are irrelevant, and the relative quantities can be compared across multiple real-time RT-PCR experiments. => http://strategy.gene-quantification.info/ -------------------------------------------------------------------------------- With this page and all the presented tools we will help you with to find the right information about qPCR and related topics in Molecular Biology in the literature and in the World Wide Web: New Papers / Protocols / Methods / Databases / Alets / Feeds / Books / Forums / E-mail / Directory => http://infoportal.gene-quantification.info/ -------------------------------------------------------------------------------- Our newsletter informs about the latest news in quantitative real-time PCR (qPCR and qRT-PCR), which are compiled and summarised on the Gene Quantification homepage. The focus of the December newsletter issue was: => Copy Number Variation => new webinars online => new application workshops in 2008 ........... http://qpcrnews.gene-quantification.info/ -------------------------------------------------------------------------------- The gene copy number (also "copy number variants" or CNVs) is the number of copies of a particular gene in the genotype of an individual. Recent evidence shows that the gene copy number can be elevated in cancer cells. => Why are CNVs important? Differences in the DNA sequence of our genomes contribute to our uniqueness. These changes influence most traits including susceptibility to disease. It was thought that single nucleotide changes (called SNPs) in DNA were the most prevalent and important form of genetic variation. The current studies reveal that CNVs comprise at least three times the total nucleotide content of SNPs. Since CNVs often encompass genes, they may have important roles both in human disease and drug response. Understanding the mechanisms of CNV formation may also help us better understand human genome evolution. http://cnv.gene-quantification.info/ -------------------------------------------------------------------------------- Data Analysis and BioInformatics in real-time qPCR Bioinformatics is a multidisciplinary approach to discribe, model and understand biological processes on basis of information on genes, proteins and metabolism. It uses computers, data bases and algorythms to link information and translate it back into biology, physiology or pathophysiology. BioInformatics => Database Management Systems, Data Mining, Sample Tracking, Information Management, Data Acquisition, Data Analysis, Statistics, Pattern Recognition & Classification, Simulation & Modeling Bioinformatics initially centered on sequence and genome analysis but now the extensive use of microarrays, mass spectrometry, qPCR and qRT- PCR, has stimulated bioinformatic work in data acquisition, signal processing, and data mining. Also, simulation and modeling are becoming increasingly important areas of focus in bioinformatics which finally will lead to a new level of understanding the networks in the metabolism: Genomics, Transcriptomics, Splicomics, Proteomics, Metabolomics, etc. http://bioinformatics.gene-quantification.info/ -------------------------------------------------------------------------------- TATAA Biocenter Germany - qPCR Application workshops At the TATAA Biocenter Germany we offer qPCR application workshops, a 3-day qPCR Core Module and a 2-day qPCR Biostatistics Module. All courses are held regularly in G?teborg, Sweden, in English and in Freising-Weihenstephan, Germany, in German and English, and in Prague, Czech Republic in English and Czech. http://tataa.gene-quantification.info/ Course Occasions 2007 and 2008: 3-day qPCR Core Module (Mon. - Wed.) and 2-day BioStatistics Module (Thu. - Fri.) => 3rd - 7th March 2008 (in Freising, Germany, English language) => 5 - 9th May 2008 (in Freising, Germany, Kurs wird in DEUTSCH gehalten, German language) => 7 - 11th July 2008 (in Freising, Germany, English language) => Please register here => http://www.tataa.com/Courses/Courses.html -------------------------------------------------------------------------------- Forward Please send the mail to further scientists and friends who are interested in qPCR ! Best regards, Michael W. Pfaffl responsible Editor of the Gene Quantification Pages http://www.Gene-Quantification.info The qPCR NEWS and the Gene Quantification Pages are educational sites with the only purpose of facilitating access to qPCR related information on the internet. The qPCR NEWS and the Gene Quantification Pages are edited by Michael W. Pfaffl and powered by BioScience Events. Copyright (c) 2005 - 2008 All rights reserved. Any unauthorized use, reproduction, or transfer of this message or its contents, in any medium, is strictly prohibited. Disclaimer & Copyrights are displayed on the homepage www.gene-quantification.com To subscribe or change your e-mail address in qPCR NEWS, and if you would like to receive future issues FREE of charge, please send an e- mail with the subject SUBSCRIBE to mailto:newsletter@gene- quantification.info?subject=SUBSCRIBE