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Genomics, Proteomics, and Bioinformatics Tutorials
September 2004- August 2005

Tutorial on Microarrays (September 13-17, 2004)

Organizers: Sashwati Roy and Chandan Sen

DNA microarrays are becoming a standard tool for molecular biology research and clinical diagnostics by providing a simple and natural means for surveying the genome in a very systematic and comprehensive manner. Microarrays are miniature arrays of gene fragments immobilized in a dense order on a solid substrate. Because thousands or tens of thousands of gene fragments can be present on a single microarray, data for entire genome can be acquired in a single experiment. The power of DNA microarrays lies in the ability to simultaneously score the hybridization signals, which represent global gene expression patterns of biological processes, and their dynamic variations. The tutorial will introduce all key aspects of microarray technology, including definitions of microarrays, types of microarray, array design, probe selection, array fabrication, biological questions, experiment design, target preparation as well as labeling, visualizing and analysis of microarray images, and basic data analysis. The tutorial is meant for individuals getting introduced to the microarray technology. The subject areas include general principles underlying microarray printing, enzymatic labeling, signal amplification, clustering methods and electronic resources. The tutorial fosters an open and interactive format; attendee questions and comments are encouraged.

Schedule: Monday - Friday, 9:00am - 12:00pm
Speakers: Chandan Sen and Sashwati Roy

Tutorial on Statistical Methods and Software for the Analysis of Microarray Experiments (September 20-24, 2004)

Organizer: Nick Jewell and Sandrine Dudoit

DNA microarray and other high-throughput genomic experiments generate complex, high-dimensional datasets of multiple types. Extracting meaningful and reliable biological information from the analysis of these data presents new statistical and computational challenges. This tutorial will discuss statistical design and inference methods for microarray experiments. Topics to be covered include: pre-processing (image analysis and normalization); multiple testing procedures for the identification of differentially expressed genes; hierarchical and partitioning cluster analysis; prediction; and model selection. We will also consider the joint analysis of microarray data with biological metadata such as Gene Ontology (GO) annotation (www.geneontology.org).

The statistical methods to be discussed apply to a broad range of problems beyond the analysis of microarray data, such as the genetic mapping of complex traits using single nucleotide polymorphisms (SNPs) and the identification of transcription factor binding sites in ChiP-Chip experiments.

Computer lab sessions will allow participants to explore statistical software resources for the analysis of genomic data, with emphasis on R packages developed as part of the Bioconductor Project (www.bioconductor.org).