Genomic mapping of single-stranded DNA in hydroxyurea-challenged yeasts identifies origins of replication

We report a genome-wide analysis of single-stranded DNA formation during DNA replication in wild type and checkpoint-deficient rad53 yeast cells in the presence of hydroxyurea. In wild type cells, ssDNA first appears at a subset of replication origins and later “migrates” bi-directionally, suggesting that ssDNA formation is associated with continuously moving replication forks. In rad53 cells, ssDNA appears at virtually every known origin, but remains there over time, suggesting that replication forks stall. Telomeric regions appear to be especially sensitive to the loss of Rad53 checkpoint function. We also mapped replication origins in Schizosaccharomyces pombe using our method.

In our microarray experiments, we sought a quantitative measurement of the ratio of single stranded DNA between a time point N (denoted TP N ) and a reference time point (denoted TP 0 , for time point zero). After collection of DNA for TP N and TP 0 and isolation of genomic DNA for time points N and 0, we differentially labeled DNA complementary to the single stranded DNA, using Cy-3 and Cy-5 dyes, respectively. The labeled samples were hybridized to the microarray. Using GenePix 4.0 software (Axon), we converted the Cy-5 and Cy-3 fluorescence intensities from TIFF files into numerical intensity data for each labeled sample and extracted the background subtracted median feature pixel intensity numbers for each spot on the array. Averaging over duplicated features, we obtained TP N Cy-3 signals (denoted a i *) and TP 0 Cy-5 signals (denoted b i *), where i indexes the set of all yeast ORFs. We arrived at Equation (1): observed ratio of ssDNA at ORF i = a i */b i *, i indexing the ORFs.
This calculation led to the chromosomal observed single stranded ratio profile by plotting the data points (cj, a j */b j * ), where j runs over the ORFs for a given chromosome. Although informative, this profile suffers from the fact it is not 2 quantitative: the TIFF files produced by core facilities arbitrarily adjust the gain in both Cy channels to achieve a roughly equal balance of total signal.
To get around this problem, the data were normalized and made quantitative by applying the scheme laid out in Collingwood et al. (manuscript in preparation). The normalization uses an external measurement of ssDNA content in S vs. G1 (slot blot) to correct one of the Cy channels, so as to restore to the microarray data the S/G1 ratio seen in the external calibration. The key result asserts the existence of a computable constant g, such that Equation (2): actual ratio of ssDNA at ORF i = g a i */b i *. We were able to experimentally compute this ratio of percentages, thus allowing us to compute the normalization constant g. We then obtained the actual single stranded ratio profile by plotting the data points (c j , g a j */b j * ), where j runs over the ORFs for a given chromosome.

Smoothing
We transformed the raw data of ssDNA ratio by using the Fourier convolution smoothing technique previously introduced 5 to obtain a smoothed profile. The smoothed profiles offered the advantage of prominently identifying local extrema in the data. In our application of smoothing, a window of 4 kb was specified and a moving average with this window size was constructed. We used this moving average as a target and selected the closest Fourier smoothing among a large family of smoothings. See the supporting online text to Raghuraman et al. 5 for full details of this procedure.

Extrema detection
Given any discrete dataset of points (x i ,y i ), we detected local extrema as follows: First, for each data point, we calculated the numbers If S i L > 0 and S i R < 0, then we flagged the point (x i ,y i ) as a local maximum, whereas if S i L < 0 and S i R > 0, then we flagged the point (x i ,y i ) as a local minimum. 4

Identification of significant ssDNA peaks
In order to calculate standard deviation in the background level of ssDNA labeling in all timed samples, we first identified and flagged those data points with values above the median value in each data set, thus removing prominent peaks from our estimation of background variation. We then removed those data points that were flagged in any timed sample data from all timed sample data in all further calculations. Averaging the three median values (Own Median), we obtained the Average Median and normalized the remaining data points by multiplying each value with the constant M (M=Average Median/Own Median). Standard deviation was calculated as the square root of variance where i indexes the data points and the subscripts 1, 2, and 3 index time points 1, 2, and 3 hr).
We present a sample calculation (numbers have been rounded here to two decimal places for clarity): Calculate median for rad53 at 1, 2 and 3 hour We next identified the local maxima and minima in each timed sample and calculated the difference between every local maximum and its two flanking minima (note that for some of the telomeric points, not every local maximum is flanked by two local minima). Those local maxima with values that are above 3 standard deviations from both its flanking local minima were considered significant ssDNA peaks. Those telomeric local maxima that only have a single flanking local minimum but are above 3 standard deviation of the said local minimum are also considered significant ssDNA peaks.   circles. Those previously mapped origins that have also been identified as significant ssDNA peaks in cds1 cells are labeled above the graphs. The two origins that eluded our analysis are boxed.