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      Large-scale gene-expression studies and the challenge of multiple sclerosis

      review-article
      1 , , 1
      Genome Biology
      BioMed Central

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          Abstract

          Recent transcription-profiling studies have found distinct gene-expression patterns in multiple sclerosis; such large-scale studies at different stages of the disease are contributing to understanding multiple sclerosis and developing effective therapy.

          Abstract

          In multiple sclerosis, a complex neurodegenerative disorder, a combination of genetic and environmental factors results in inflammation and myelin damage. Recent transcription-profiling studies have found distinct gene-expression patterns in diseased tissue; such large-scale studies at different stages of the disease are contributing to understanding multiple sclerosis and developing effective therapy.

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          Most cited references15

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

            A high-capacity system was developed to monitor the expression of many genes in parallel. Microarrays prepared by high-speed robotic printing of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. Because of the small format and high density of the arrays, hybridization volumes of 2 microliters could be used that enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA. Differential expression measurements of 45 Arabidopsis genes were made by means of simultaneous, two-color fluorescence hybridization.
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              Serial analysis of gene expression.

              The characteristics of an organism are determined by the genes expressed within it. A method was developed, called serial analysis of gene expression (SAGE), that allows the quantitative and simultaneous analysis of a large number of transcripts. To demonstrate this strategy, short diagnostic sequence tags were isolated from pancreas, concatenated, and cloned. Manual sequencing of 1000 tags revealed a gene expression pattern characteristic of pancreatic function. New pancreatic transcripts corresponding to novel tags were identified. SAGE should provide a broadly applicable means for the quantitative cataloging and comparison of expressed genes in a variety of normal, developmental, and disease states.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2002
                16 September 2002
                : 3
                : 10
                : reviews1027.1-reviews1027.5
                Affiliations
                [1 ]Department of Neurology, University of California San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143-0435, USA
                Article
                gb-2002-3-10-reviews1027
                10.1186/gb-2002-3-10-reviews1027
                244911
                12372148
                214527a4-e9dc-4605-ad93-a316bdd67f11
                Copyright © 2002 BioMed Central Ltd
                History
                Categories
                Minireview

                Genetics
                Genetics

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