Briefly, complexity-reduced representations consisting of small (200-1200 bp) fragments were amplified by adapter-mediated PCR of genomic DNA. DNA samples (2 ug) were labeled either with Cy5-dCTP or Cy3-dCTP using Amersham-Pharmacia MegaPrime labeling kit (Amersham Biosciences, Piscataway, NJ), and competitively hybridized to each other on the same slide.
Briefly, complexity-reduced representations consisting of small (200-1200 bp) fragments were amplified by adapter-mediated PCR of genomic DNA. DNA samples (2 ug) were labeled either with Cy5-dCTP or Cy3-dCTP using Amersham-Pharmacia MegaPrime labeling kit (Amersham Biosciences, Piscataway, NJ), and competitively hybridized to each other on the same slide.
Hybridization protocol
Hybridizations consisted of 35 uL of hybridization solution (37% formamide, 4x SSC, 0.1%SDS, and labeled DNA). Samples were denatured in an MJ Research Tetrad (Bio-Rad, Hercules, CA) at 95 degrees C for 5 min, and then pre-annealed at 37 degrees C for no more than 30 min. The solution was then applied to the microarray and hybridized under a coverslip in an oven at 42 degrees C for 14 to 16 h. Thereafter, slides were washed 1 min in 0.2% SDS/0.2x SSC, 30 sec in 0.2x SSC, and 30 sec in 0.05x SSC. Slides were dried by centrifugation and scanned immediately.
Scan protocol
Scanned on an Axon GenePix 4000B scanner using a pixel size of 5 um. Microarrays were scanned and gridded using GenePix Pro 4.0 software (MDS Analytical Technologies, Toronto, Canada) and data were imported into S-Plus 2000 analysis software (Insightful, Seattle, WA).
Description
This sample was hybridized to a single array (two color-reversals not performed).
Data processing
The data were normalized using a lowess curve-fitting algorithm, followed by a local normalization (previously described in Hicks et al.). After placement in genome order, the mean of log ratios was computed. Segmentation was performed on the above-described data. Segments are defined as non-overlapping, genomic regions where copy number has changed. Our segmentation method is based on the minimization of the square-sum of differences between log-ratios and means (squared deviation) over segments larger than 6 probes in size. Initially, the segmenter searches for breakpoints that might be boundaries of segments. The first known breakpoint on a given chromosome is its first probe. For a given breakpoint, a 100-probe window to its right is selected. The sum of squared deviations of the flanking probes is calculated for each probe within this window. A probe whose squared deviation value produces a local minimum with respect to its neighbors, and is below a threshold of 95% of the square deviation within a window, is accepted as a new, known breakpoint. Whenever a probe is found below the threshold in the newly defined region, the segmenter recursively breaks said region into two pieces until it cannot find any further breakpoints therein. If no breakpoints are found, the 100- probe window is shifted by half its size and this procedure continues until a chromosome end is reached. Initial segments are constructed using found breakpoints. Each segment and its neighbors are validated for significance by the Kolmogorov-Smirnov (K-S) algorithm. If the p-value of compared segments is less than 10-5, then said segment is accepted as real. If not, the segments are merged. The segmenter also reports statistics such as mean, standard deviation, and median for each segment.
