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Open Access Highly Accessed Research article

A computational method for detecting copy number variations using scale-space filtering

Jongkeun Lee12, Unjoo Lee3, Baeksop Kim2* and Jeehee Yoon2*

Author Affiliations

1 Cancer Genomics Branch and Research Institute and Hospital, National Cancer Center, Goyang, Korea

2 Department of Computer Engineering, Hallym University, Chuncheon, Korea

3 Department of Electronic Engineering, Hallym University, Chuncheon, Korea

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BMC Bioinformatics 2013, 14:57  doi:10.1186/1471-2105-14-57

Published: 18 February 2013

Abstract

Background

As next-generation sequencing technology made rapid and cost-effective sequencing available, the importance of computational approaches in finding and analyzing copy number variations (CNVs) has been amplified. Furthermore, most genome projects need to accurately analyze sequences with fairly low-coverage read data. It is urgently needed to develop a method to detect the exact types and locations of CNVs from low coverage read data.

Results

Here, we propose a new CNV detection method, CNV_SS, which uses scale-space filtering. The scale-space filtering is evaluated by 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 finger print map, the contours of zero-crossing points for various scales.

Conclusions

The performance of CNV_SS showed that FNR and FPR stay in the range of 1.27% to 2.43% and 1.14% to 2.44%, respectively, even at a relatively low coverage (0.5x ≤C ≤2x). CNV_SS gave also much more effective results than the conventional methods in the evaluation of FNR, at 3.82% at least and 76.97% at most even when the coverage level of read data is low. CNV_SS source code is freely available from http://dblab.hallym.ac.kr/CNV SS/ webcite.