ArticleBMC bioinformatics2013
A computational method for detecting copy number variations using scale-space filtering.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Molecular contribution to embryonic aneuploidy and karyotypic complexity in initial cleavage divisions of mammalian development.Development (Cambridge, England) · 2022Article
- Transcriptome Profiling Associated with Carcass Quality of Loin Muscles in Crossbred Pigs.Animals : an open access journal from MDPI · 2020Article
- Single-cell sequencing of primate preimplantation embryos reveals chromosome elimination via cellular fragmentation and blastomere exclusion.Genome research · 2019Article
- ExCNVSS: A Noise-Robust Method for Copy Number Variation Detection in Whole Exome Sequencing Data.BioMed research international · 2017Article
- Identification of medium-sized copy number alterations in whole-genome sequencing.Cancer informatics · 2014Article
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Authors and funding
4 authors.
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Abstract
backgroundAs 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.
resultsHere, 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.
conclusionsThe 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/.
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