Similarly, advances in next-generation sequencing methods have revolutionized the application of metatranscriptome analyses, which until recently were limited to microarray experiments or analysis of mRNA-derived cDNA clone libraries.
Constructing a Living Microarray with known promoters could also profile complex processes, for example in studying the transcriptional changes that occur during the cell cycle [ 32- 34], where temporal profiling of transcription in mammalian cells has been limited to microarray analysis using pooled RNA from synchronized cultures [ 35- 37].
Examples might include, but would not be limited to, obtaining microarray data from individual in vivo bioassays or in vitro cell or tissue-based assays or from batteries of assays, using conventional or high-throughput approaches.
While these high-throughput methods are powerful, there are practical limitations: DNA microarrays are limited to detecting genes for which allelic variants have already been characterized and may miss emerging mutations; the PAML-based approach requires multiple genomes of phenotypic variants of the same species, which are not always available.
However, microarray gene expression profiling is limited to ESTs or genes present on the microarray chip and may not provide a complete representation of differentially expressed genes.
Second, the resolution of a microarray is limited to the gene level for most arrays and the exon level for specially designed exon arrays.
Finally, MACAT is limited to the analysis of Affymetrix microarray, further underlining the need for a tool open to all platforms as well as to cross-platform analysis.
These results are limited to the Agilent 44 k microarray platform and should be carefully interpreted when transferring to other settings.
However, despite the promise of microarrays as versatile biodetection platforms, most applications of microarray have been limited to genome-scale gene expression analysis, such as a preliminary screening method to investigate relative expression level of target genes or gene ontology analysis based on the accumulated amount of various genome data and their functional annotation information [ 8].
