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About the project

Welcome to the CNV_SS website.

The software of Copy Number Variation using Sacle Space (herein, CNV_SS), to detect CNVs using scale-space filtering, is enabling the detection of the types and the exact locations of CNVs of all sizes. CNV_SS has been developed on the database laboratory, Hallym university in South Korea. In order to run this software, the users must install the software MATLAB. CNV_SS is provided to CNV study community freely on download page of this website.


Contact: Jeehee Yoon (jhyoon@hallym.ac.kr) and Baeksop Kim (bskim@hallym.ac.kr)  (If you have any requirements and advices, freely let us know).


The overall processes of CNV_SS
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.
 

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.


Database Laboratory, Department of Computer Engineering, Hallym University, 1 Hallymdaehak-gil, Chuncheon, Gangwon-do 200-702, Korea