Somatic copy number (CN) alterations are major drivers of tumorigenesis and growth. Although next-generation sequencing (NGS) technologies enable a deep genomic analysis of cancers, the analysis of the data remains subject to biases and multiple sources of error, including varying local read coverage. The currently existing algorithms for NGS-based detection of CN abberations do not incorporate information on the local coverage quality. We have developed a new algorithm, copy number estimation with controlled support (CoNCoS) that increases the accuracy of CN estimation in paired tumor/normal exome sequencing data sets by assessing and optimizing the support for a site-specific CN estimate. We show by simulations and in a benchmarking study against single nucleotide polymorphism (SNP) microarray data that our approach outperforms the commonly used methods CNAnorm and VarScan2. Our algorithm is suitable to increase the accuracy of somatic CN analysis by a support-optimized estimation approach.
Keywords: Cancer; copy number abberations; next-generation sequencing; optimization.