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Although transcriptional profiles in such target disease tissues/cells are ideal for such analyses, the complexity of procuring such tissue biopsies/cells and the low amount of RNA from these specimens for standard microarray assays has made whole blood, a practical and an attractive surrogate tissue in clinical research [3], [4], [5], [6], [7].
Indeed, the quality of data generated by microarray assays has been questioned [ 1, 2].
The ChIP-chip analyses also showed that only one (STM2938) of the 13 newly-identified, potential σ-dependent operons from the DNA microarray assays has a σ DNA binding site associated with it (Table 3), suggesting that the other 12 operons may be indirectly regulated by σ.
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Recently, microarray technology has increasingly been widely applied in glycobiology.
Supporting this view, the number of applications of DNA microarray technology has since expanded exponentially.
This is appropriate for discovery based investigations where microarray technology has been successfully utilized.
Quality control of a microarray experiment has become an important issue for both research and regulation.
As microarray technology has matured, data analysis methods have advanced as well.
Microarray technology has been extensively used to profile gene expression for more than a decade.
Microarray design has been described previously [39].
Until quite recently, microarray technology has dominated gene expression profiling.
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