Supporting data for "CopyDetective: Detection Threshold Aware CNV Calling in WES Data"

Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, the detection of CNVs with whole-exome sequencing experiments is challenging. Usually, additional experiments have to be performed.
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 four WES data sets (n=100) we observe superior performance of CopyDetective compared to ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq. Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds (as it is realized by CopyDetective), can and should be performed prior to actual variant calling.