Detection of copy-number variations from NGS data using read depth information: a diagnostic performance evaluation

Abstract

The detection of copy-number variations (CNVs) from NGS data is underexploited as chip-based or targeted techniques are still commonly used. We assessed the performances of a workflow centered on CANOES, a bioinformatics tool based on read depth information. We applied our workflow to gene panel (GP) and whole-exome sequencing (WES) data, and compared CNV calls to quantitative multiplex PCR of short fluorescent fragments (QMSPF) or array comparative genomic hybridization (aCGH) results. From GP data of 3776 samples, we reached an overall positive predictive value (PPV) of 87.8%. This dataset included a complete comprehensive QMPSF comparison of four genes (60 exons) on which we obtained 100% sensitivity and specificity. From WES data, we first compared 137 samples with aCGH and filtered comparable events (exonic CNVs encompassing enough aCGH probes) and obtained an 87.25% sensitivity. The overall PPV was 86.4% following the targeted confirmation of candidate CNVs from 1056 additional WES. In addition, our CANOES-centered workflow on WES data allowed the detection of CNVs with a resolution of single exons, allowing the detection of CNVs that were missed by aCGH. Overall, switching to an NGS-only approach should be cost-effective as it allows a reduction in overall costs together with likely stable diagnostic yields. Our bioinformatics pipeline is available at: https://gitlab.bioinfo-diag.fr/nc4gpm/canoes-centered-workflow .

Fig. 1. Principles of depth of coverage (DOC) comparison.

Schematic distribution of reads among three different samples over five sequenced exons. a The absence of any CNV. b Duplication of two exons (2 and 3). c Deletion of exon 4. In order to call those CNVs, software tools have to establish a reference. Some tools compare paired data from the same patient, e.g., tumor tissue against germline, while others build their reference from a pool of samples and then compare a given sample to this reference, as the CANOES tool used in our workflow.

Fig. 2. CANOES-centered workflow.

File (square) with their format in parenthesis, and process (rounded) constituting the workflow. From the original capture kit definition, we merge closed target from the same exon, then do in parallel the DOC and the GC content estimation. We gather DOC individual files depending on the project, sequencing batch, unrelated samples, and remove non-informative regions. The last steps consist in CNV calling using CANOES and annotation with annotSV.

Fig. 3. Example of a CNV detected by aCGH but missed by the CANOES-centered workflow.

A CNV (highlight region) detected by aCGH encompassing multiple CGH probes (1M probes array, in gray) but only one target from the SureSelect V5 capture kit. Of note, this deletion would have been missed by using a 180k probes array CGH (in black). View extract from UCSC genome Browser (https://genome-euro.ucsc.edu/cgi-bin/hgTracks).

Fig. 4. Example of CNVs detected by the CANOES-centered workflow from WES data but missed by aCGH.

a The highlighted region represents the CNV called by the CANOES-centered workflow, encompassing one exon of RHCE. b View of the same region from DNA-Analytics (aCGH data 1M) in the same patient. This deletion was not called following aCGH data analysis as the number of deviated probes did not reach the threshold for calling. However, as three probes (in white) were deviated, this allows the confirmation of the deletion of the region. View extract from UCSC genome Browser (https://genome-euro.ucsc.edu/cgi-bin/hgTracks).