Ryan M. Fryer a , Jeffrey Randall a , Takumi Yoshida a , Li-Li Hsiao a , Joshua Blumenstock a , Katharine E. Jensen a , Tudor Dimofte a , Roderick V. Jensen b , Steven R. Gullans a
05 April 2002
Gene expression, HuGE index, Microarray, Bioinformatics, Standard gene set
Over the past 15 years, global analysis of mRNA expression has emerged as a powerful strategy for biological discovery. Using the power of parallel processing, robotics, and computer-based informatics, a number of high-throughput methods have been devised. These include DNA microarrays, serial analysis of gene expression, quantitative RT-PCR, differential-display RT-PCR, and massively parallel signature sequencing. Each of these methods has inherent advantages and disadvantages, often related to expense, technical difficulty, specificity, and reliability. Further, the ability to generate large data sets of gene expression has led to new challenges in bioinformatics. Nonetheless, this technological revolution is transforming disease classification, gene discovery, and our understanding of regulatory gene networks.
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