Beyond the Identification of Transcribed
Sequences:
Functional and Expression Analysis
11th Annual Workshop
November 9-12, 2001
Washington D.C.
Alvis Brazma
European Bioinformatics Institute
Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
Phone: 1223-494658; Fax: 1223-494468
Email: brazma@ebi.ac.uk
The handling and analysis of the huge amounts of microarray data are becoming the major bottlenecks in the utilization of the microarray technology. Storing and annotating these data is not a trivial problem due to many reasons. The raw microarray data are images, which have to be transformed into gene expression matrices -- tables where rows represent genes, columns represent various samples such as different tissues, and values at each position characterize the expression level of the particular gene in the particular sample. These matrices have to be integrated with other genomic data and analyzed further, if any knowledge about the underlying biological processes is to be extracted (see [1]). European Bioinformatics Institute initiated an international effort to establish standards for microarray data representation, annotation and exchange [2]. An XML based data exchange format - MicroArray Gene Expression Markup Language (MAGE-ML) it being developed in collaboration with Microarray Gene Expression Database (MGED) Group (see www.mged.org). EBI is establishing a public repository for microarray data ArrayExpress, which will accept data in MAGE-0ML format. Online tools for gene expression data analysis are available (www.ebi.ac.uk/microarray).
We study dependencies between the gene expression profiles in a dataset from genome wide yeast mutation studies, regarding the profiles as random variables. We build gene expression dependency networks from these data and study their properties. We look for 'important' genes, i.e., genes with high out-degree in the dependency graph, and genes with complex regulation, i.e., genes with high in-degree in the graph.
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