User:Mithleshjani/sandbox

Gene expression microarrays are a tool used by ecologists, to study the genome-wide transcriptional variation that underlies complex interactions among and between organisms and their environment. Microarrays allow for the simultaneous measurement of thousands of gene products across many samples. These measurements rely on sequence similarity for efficient hybridization of sample mRNA targets to the microarray probes. Genetic polymorphisms located within a microarray's probe affects this hybridization efficiency. These differences in probe level hybridization efficiencies are known as single feature polymorphisms (SFPs). SFPs are both a source of error, they negatively impact the estimate for transcript abundance, and a source of information, they are genetic markers. The Affymetrix® 3' IVT microarray platform uses 11 to 16 short-oligonucleotide probes, covering different regions of a gene, to measure transcript abundance. The redundancy of probes within the same gene, allows for the capability to detect candidate SFPs from the data itself. This dissertation presents three new results pertaining to SFPs in Affymetrix® gene expression microarray data. The first result is a novel, improved algorithm for the accurate detection of SFPs. The second result evaluates the impact of SFPs to false positives in both differential expression analysis and the detection of SFPs themselves and offers a solution to reduce false positives induced by SFPs. Finally, the third result demonstrates how SFPs can be used to provide a new means for evaluating the accuracy of critical microarray preprocessing steps. These results will provide ecologists with new tools and techniques when using Affymetrix® 3' IVT microarrays in ecological microarray studies.