While the primary utility of the resource we have developed is the supplementation of commercial SNP microarrays, it has several other useful applications.
Through the advent of global gene expression profiling methodologies such as microarrays, it has become possible to identify the consequences of these remarkably conserved chromosomal aneuploidies on the cancer transcriptome.
For microarrays, it has taken a decade to understand these biases but for microarrays this has now been achieved and stable analytical solutions have been developed.
For example, with the use of Affymetrix U133A and B microarrays, it has been demonstrated that the microarray data can be reannotated for lncRNA expression analysis [ 10].
With ever increasing numbers of DNA samples being analysed on SNP genotyping microarrays, it has become easier to identify sex chromosome aneuploidies.
In comparison with other methods, like microarrays, it has been shown that RNA-Seq delivers far more accurate measurements (Wang et al., 2009) for differential gene expression analysis.
Unlike microarrays, it has the ability to detect novel forms of RNA such as alternatively spliced and antisense transcripts, without the need for prior knowledge of their existence.
Using mRNA microarrays, it has been demonstrated that the top 100 deregulated mRNA transcripts were able to define patient subgroups, which were associated with the clinical outcome (Yang et al, 2005).
However, using cDNA microarrays, it has been previously shown that the induction of TSP1 in docetaxel-treated head and neck squamous cell carcinoma cell lines increased cytotoxicity (Yoo et al, 2002).
Using human cDNA microarrays, this technique has helped elucidate genomic copy number changes in cancer cells during tumor progression [ 46- 48]; using S. cerevisiae-based microarrays, it has also been employed to discover non-reciprocal chromosomal translocations that occurred in yeasts evolved to tolerate low glucose concentrations [ 31].
