panelcn.MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics

Abstract

Targeted next‐generation‐sequencing (NGS) panels have largely replaced Sanger sequencing in clinical diagnostics. They allow for the detection of copy‐number variations (CNVs) in addition to single‐nucleotide variants and small insertions/deletions. However, existing computational CNV detection methods have shortcomings regarding accuracy, quality control (QC), incidental findings, and user‐friendliness. We developed panelcn.MOPS, a novel pipeline for detecting CNVs in targeted NGS panel data. Using data from 180 samples, we compared panelcn.MOPS with five state‐of‐the‐art methods. With panelcn.MOPS leading the field, most methods achieved comparably high accuracy. panelcn.MOPS reliably detected CNVs ranging in size from part of a region of interest (ROI), to whole genes, which may comprise all ROIs investigated in a given sample. The latter is enabled by analyzing reads from all ROIs of the panel, but presenting results exclusively for user‐selected genes, thus avoiding incidental findings. Additionally, panelcn.MOPS offers QC criteria not only for samples, but also for individual ROIs within a sample, which increases the confidence in called CNVs. panelcn.MOPS is freely available both as R package and standalone software with graphical user interface that is easy to use for clinical geneticists without any programming experience. panelcn.MOPS combines high sensitivity and specificity with user‐friendliness rendering it highly suitable for routine clinical diagnostics.

Further delineation of putative ACTB loss‐of‐function variants: A 4‐patient series

“…To the best of our knowledge, this represents the first 7p22.1 microdeletion that is confined to ACTB. The boundaries of this intrachromosomal 7p22.1 deletion, which was detected in WES data with the panelcn.MOPS software (Povysil et al, 2017), were narrowed down by quantitative polymerase chain reaction (qPCR) to ultimately facilitate breakpoint sequencing in patients 1 and 2. for glycine is predicted to bring the alanine residue in hydrogenbonding distance to ADP as modeled on pdb structure 6FM2 (Kotila et al, 2018;Figure 1f,g). The effect of this missense variant was predicted to be deleterious by three of four different in silico prediction programs, Provean (Choi, Sims, Murphy, Miller, & Chan, 2012), MutationTaster (Schwarz, Rodelsperger, Schuelke, & Seelow, 2010), and CADD (Rentzsch, Witten, Cooper, Shendure, & Kircher, 2019) and to be benign by Polyphen2 (Adzhubei et al, 2010; Table 1).…”

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

“…The dataset ICR96 exon CNV validation series (Mahamdallie et al, 2017) was downloaded from the European Genome-phenome Archive (EGA) (EGAD00001003335) and contained 96 samples captured with TruSight Cancer Panel v2 (100 genes) and sequenced in a single HiSeq lane with 101-base pairedend reads. panelcnDataset (Povysil et al, 2017) was also downloaded from EGA (EGAS00001002481) and had 170 samples captured using TruSight Cancer Panel (94 genes, Illumina) and sequenced on a MiSeq instrument with 151-base paired-end reads. Only 161 out of the 170 samples were used in this work and 9 were removed because they presented alterations out of the scope of this benchmark: 5 presented CNVs smaller than an exon (IBK9, IBK23, IBK67, IBK153, IBK166) and 4 contained ALUs insertions instead of CNVs (IBK141, IBK142, IBK143, IBK151).…”

“…Five tools were tested in the benchmark: CoNVaDING v1.2.0 (Johansson et al, 2016), DECoN v1.0.1 (Fowler et al, 2016), panelcn.MOPS v1.0.0 (Povysil et al, 2017), ExomeDepth v1.1.10 (Plagnol et al, 2012) and CODEX2 v1.2.0 (Jiang et al, 2018).…”

“…After a literature search process, we selected five CNV calling tools to be evaluated ( Table 2), all of them based on depth-of-coverage analysis. Three tools have been reported to perform well on NGS panel data at single-exon resolution: CoNVaDING v1.2.0 (Johansson et al, 2016), DECoN v1.0.1 (Fowler et al, 2016) and panelcn.MOPS v1.0.0 (Povysil et al, 2017). ExomeDepth v1.1.10 (Plagnol et al, 2012) was included due to its high performance in benchmarks on WES data (de Ligt et al, 2013;Sadedin et al, 2018) and because the developers reported good performance with panel data (https://github.com/vplagnol/ExomeDepth).…”

“…Other benchmarks of CNV calling tools on targeted NGS panel data have been published. However, they were performed by the authors of the tools and executed against a single dataset (Johansson et al, 2016;Fowler et al, 2016;Povysil et al, 2017;Kim et al, 2017;Chiang et al, 2019), or used mainly simulated data with a small number of validated CNVs (Roca et al, 2019). The aim of this work is to perform an independent benchmark of multiple CNV calling tools, optimizing and evaluating them against multiple datasets generated in diagnostics settings, to identify the most suitable tools to be used for genetic diagnostics (Figure 1).…”