Project description:To minimize the distortion of genetic signal by system noise, we have explored the latter in an archive of hybridizations in which no genetic signal is expected. This archive is obtained by comparative genomic hybridization (CGH) of a reference sample in one channel to the same sample in the other channel, which we have termed ‘self-self’ data. We show that these self-self hybridizations trap a variety of system noise inherent in sample-reference (test) data. Through singular value decomposition (SVD) of self-self data, we are able to determine the principal components of system noise. Assuming simple linear models of noise generation, we present evidence that the linear correction of test data with self-self data—which we call system normalization—reduces local and long-range correlations as well as improves signal-to-noise metrics, yet does not introduce detectable spurious signal.
Project description:Using microarrays targeting the complete ORFeome of Saccharomyces cerevisiae S288c, this study investigates the genetic variability present in wild type yeast from different ecological niches by comparative genome hybridization on array (aCGH). Genomic DNA of Saccharomyces strains isolated from clinical samples and strains from wine fermentations, either commercially available or isolated form spontaneous wine fermentations, was compared to genomic DNA of the sequenced laboratory strain S288c, using a common reference experimental design, with two dye-swap replicate hybridizations for each strain and six S288c self-self hybridizations for background noise estimation, in a total of 38 hybridizations.
