CoNVaDING: Single Exon Variation Detection in Targeted NGS Data. Issue 5 (24th February 2016) - Johansson, Lennart F.; van Dijk, Freerk; de Boer, Eddy N.; van Dijk‐Bos, Krista K.; Jongbloed, Jan D.H.; van der Hout, Annemieke H.; Westers, Helga; Sinke, Richard J.; Swertz, Morris A.; Sijmons, Rolf H.; Sikkema‐Raddatz, Birgit
Abstract : We have developed a tool for detecting single exon copy number variations (CNVs) in targeted next‐generation sequencing data: CoNVaDING (Copy Number Variation Detection In Next‐generation sequencing Gene panels). CoNVaDING includes a stringent quality control metric, that excludes or flags low quality exons. Since this quality control shows exactly which exons can be reliably analysed and which exons are in need of an alternative analysis method, CoNVaDING is also useful for CNV detection in clinical diagnostics. ABSTRACT: We have developed a tool for detecting single exon copy‐number variations (CNVs) in targeted next‐generation sequencing data: CoNVaDING (Copy Number Variation Detection In Next‐generation sequencing Gene panels). CoNVaDING includes a stringent quality control (QC) metric, that excludes or flags low‐quality exons. Since this QC shows exactly which exons can be reliably analyzed and which exons are in need of an alternative analysis method, CoNVaDING is not only useful for CNV detection in a research setting, but also in clinical diagnostics. During the validation phase, CoNVaDING detected all known CNVs in high‐quality targets in 320 samples analyzed, giving 100% sensitivity and 99.998% specificity for 308, 574 exons. CoNVaDING outperforms existing tools by exhibiting a higher sensitivity and specificity and by precisely identifying low‐quality samples and regions.