Benchmark of tools for CNV detection from NGS panel data in a genetic diagnostics context

Abstract

MotivationAlthough germline copy number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data with CNVs up to single-exon resolution and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. ResultsFive tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth and CODEX2) were tested against four genetic diagnostics datasets (495 samples, 231 CNVs), using the default and sensitivity-optimized parameters. Most tools were highly sensitive and specific, but the performance was dataset-dependant. In our in-house datasets, DECoN and panelcn.MOPS with optimized parameters showed enough sensitivity to be used as screening methods in genetic diagnostics. AvailabilityBenchmarking-optimization code is freely available at https://github.com/TranslationalBioinformaticsIGTP/CNVbenchmarkeR.