The overall processes of CNV_SS |
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We suggest a new method, CNV_SS, to detect CNVs using scale-space filtering, enabling the detection of the types and the exact locations of CNVs of all sizes even when the coverage level of read data is low (< 5.0x). The scale-space filtering is evaluated by assuming a ¨ç Gaussian distribution of read coverage data and applying to the read coverage data the Gaussian convolution for various scales according to a given scaling parameter. Next, by differentiating twice and finding zero-crossing points, inflection points of scale-space filtered read coverage data are calculated per scale. Then, the types and the exact locations of CNVs are obtained by analyzing the ¨è finger print map, the contours of zero-crossing points for various scales.
The ¨é baselines of each layer are calculated using the mean and the standard deviation of the read coverage data for each layer with decreasing ¥ò. ¨ê Intervals are also searched by using the baselines through the finger print map with decreasing ¥ò. Here, the interval is the region of the input sequence where a CNV gain or loss is detected. More than one interval is not permitted in a region of the sequence. Therefore, once an interval is obtained at a layer the exact position of the detected CNV is decided by localizing the positions where the start and the end points of the interval converge at the lowest layer; no more interval searching at the corresponding region is necessary.
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CNV_SS proceeds in two stages: up and down stages. The up stage includes preprocessing, Gaussian convolution, and finger print mapping. The down stage includes baseline adjustment, interval search, and CNV detection.
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