RT Journal Article
SR Electronic
T1 ClinCNV: multi-sample germline CNV detection in NGS data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.10.495642
DO 10.1101/2022.06.10.495642
A1 Demidov, German
A1 Sturm, Marc
A1 Ossowski, Stephan
YR 2022
UL http://biorxiv.org/content/early/2022/06/13/2022.06.10.495642.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.