ClinCNV: multi-sample germline CNV detection in NGS data

Abstract Germline copy number variants (CNVs) are a common source of genomic variation involved in many genetic disorders, and their detection is crucial for clinical molecular diagnostics. Genomic microarrays, quantitative polymerase chain reaction (qPCR), and multiplex ligation-dependent probe amplification (MLPA) have been widely used for CNV detection in clinics for many years. Similarly, next-generation sequencing (NGS) applications such as whole-genome sequencing (WGS) and whole-exome sequencing (WES) are well-established, highly accurate techniques for the detection of single nucleotide variants (SNVs) and small insertions and deletions (indels). However, CNV detection using NGS remains challenging due to short read lengths, smaller than CNVs sizes. CNV detection using read coverage depths summarized in genomic regions is affected by various biases that arise during the library preparation and sequencing. We have developed a novel strategy for detecting CNVs, implemented in the tool ClinCNV (freely available on https://github.com/imgag/ClinCNV). ClinCNV does multi-sample normalization and CNV calling, using an original algorithm taking the best from the circular binary segmentation method and Hidden Markov model-based approaches. Here, we describe the methods and discuss the results obtained by applying ClinCNV to thousands of clinical WES, WGS, and shallow-WGS samples in various clinical and research settings.