The Use and Analysis of Microarray Data

  • Published 2002


NATURE REVIEWS | DRUG DISCOVERY VOLUME 1 | DECEMBER 2002 | 951 At the time of writing this article, the genomes of more than 800 organisms have been sequenced, and well over 3.5 million genetic sequences have been deposited in international repositories. However, the biological functions of most of these genes remain unknown, or have been predicted only through homology to genes with functions that are better known. One way to determine the functions of these genes is through repeated measurements of their RNA transcripts; for example, knowing that a particular gene is expressed only in cardiac muscle and only under particular conditions implicitly gives us functional knowledge about that gene. Functional genomics is the study of gene function through parallel expression measurements of a genome. The most common tools used to carry out these measurements include complementary DNA microarrays, oligonucleotide microarrays or serial analysis of gene expression (SAGE). This article will focus on microarrays, which are artificially constructed grids of DNA, such that each element of the grid probes for a specific RNA sequence — that is, each holds a DNA sequence that is a reverse complement to the target RNA sequence. Although there are many protocols and types of system available, the basic technique involves extraction of RNA from biological samples in either normal or interventional states. The RNA (or, in some protocols, isolated messenger RNA) is then copied, while incorporating either fluorescent nucleotides or a tag that is later stained with fluorescence. The labelled RNA is then hybridized to a microarray for a period of time, after which the excess is washed off and the microarray is scanned under laser light. This process is schematized in FIG. 1. With oligonucleotide microarrays, for which all probes have been designed to be theoretically similar with regard to hybridization temperature and binding affinity, each microarray measures a single sample and provides an absolute measurement level for each RNA molecule, although this absolute measurement might not correlate exactly with concentration in terms of micrograms per unit volume. With cDNA microarrays, for which each probe has its own hybridization characteristic, each microarray measures two samples, and provides a relative measurement level for each RNA molecule. Regardless of the technique, the end result is 4,000–50,000 measurements of gene expression per biological sample.As a complete experiment might involve anywhere up to hundreds of microarrays, the resultant RNA-expression data sets can vary greatly in size. As the cost of microarrays continues to drop, it is clear that microarrays are becoming more integral to the drug discovery process. In addition to the obvious use of functional genomics in basic research and target discovery, such as finding genes expressed in significantly different patterns across samples, there are many other specific uses in this domain. These include: biomarker determination, to find genes that correlate with and presage disease progression, but are easier to measure and follow in clinical trials; pharmacology, to THE USE AND ANALYSIS OF MICROARRAY DATA

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@inproceedings{2002TheUA, title={The Use and Analysis of Microarray Data}, author={}, year={2002} }