Sarah Sandmann1, Marius Wöste1, Aniek O de Graaf2, Birgit Burkhardt3, Joop H Jansen2, Martin Dugas1. 1. Institute of Medical Informatics, University of Münster, Albert-Schweitzer-Campus 1, Building A11, Münster 48149, Germany. 2. Laboratory Hematology, RadboudUMC, Geert Grooteplein Zuid 10, Nijmegen 6525 GA, Netherlands. 3. Paediatric Hematology & Oncology, University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, Münster 48149, Germany.
Abstract
BACKGROUND: Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, detection of CNVs with whole-exome sequencing (WES) experiments is challenging. Usually, additional experiments have to be performed. FINDINGS: We developed a novel algorithm for somatic CNV calling in matched WES data called "CopyDetective". Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. Analyzing 4 WES data sets (n = 100) we observed superior performance of CopyDetective compared with ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq. CONCLUSIONS: Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds-as realized by CopyDetective-can and should be performed prior to actual variant calling.
BACKGROUND: Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, detection of CNVs with whole-exome sequencing (WES) experiments is challenging. Usually, additional experiments have to be performed. FINDINGS: We developed a novel algorithm for somatic CNV calling in matched WES data called "CopyDetective". Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. Analyzing 4 WES data sets (n = 100) we observed superior performance of CopyDetective compared with ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq. CONCLUSIONS: Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds-as realized by CopyDetective-can and should be performed prior to actual variant calling.
Authors: Jarupon Fah Sathirapongsasuti; Hane Lee; Basil A J Horst; Georg Brunner; Alistair J Cochran; Scott Binder; John Quackenbush; Stanley F Nelson Journal: Bioinformatics Date: 2011-08-09 Impact factor: 6.937
Authors: Peter Van Loo; Silje H Nordgard; Ole Christian Lingjærde; Hege G Russnes; Inga H Rye; Wei Sun; Victor J Weigman; Peter Marynen; Anders Zetterberg; Bjørn Naume; Charles M Perou; Anne-Lise Børresen-Dale; Vessela N Kristensen Journal: Proc Natl Acad Sci U S A Date: 2010-09-13 Impact factor: 11.205
Authors: Sarah Sandmann; Aniek O de Graaf; Mohsen Karimi; Bert A van der Reijden; Eva Hellström-Lindberg; Joop H Jansen; Martin Dugas Journal: Sci Rep Date: 2017-02-24 Impact factor: 4.379