Free-access copy-number variant detection tools for targeted next-generation sequencing data

Abstract Copy number variants (CNVs) are intermediate-scale structural variants containing copy number changes involving DNA fragments of between 1 kb and 5 Mb. Although known to account for a significant proportion of the genetic burden in human disease, the role of CNVs (especially small CNVs) is often underestimated, as they are undetectable by traditional Sanger sequencing. Since the development of next-generation sequencing (NGS) technologies, several research groups have compared depth of coverage (DoC) patterns between samples, an approach that may facilitate effective CNV detection. Most CNV detection tools based on DoC comparisons are designed to work with whole-genome sequencing (WGS) or whole-exome sequencing (WES) data. However, few methods developed to date are designed for custom/commercial targeted NGS (tg-NGS) panels, the assays most commonly used for diagnostic purposes. Moreover, the development and evaluation of these tools is hindered by (i) the scarcity of thoroughly annotated data containing CNVs and (ii) a dearth of simulation tools for WES and tg-NGS that mimic the errors and biases encountered in these data. Here, we review DoC-based CNV detection methods described in the current literature, assess their performance with simulated tg-NGS data, and discuss their strengths and weaknesses when integrated into the daily laboratory workflow. Our findings suggest that the best methods for CNV detection in tg-NGS panels are DECoN, ExomeDepth, and ExomeCNV. Regardless of the method used, there is a need to make these programs more user-friendly to enable their use by diagnostic laboratory staff who lack bioinformatics training.