Dataset Information

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications.

ABSTRACT: CoverageMaster (CoM) is a copy number variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome [whole exome sequencing (WES)] and genome [whole genome sequencing (WGS)] data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model. CoM processes WES and WGS data at nucleotide scale resolution and accurately detects and visualizes full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such as array comparative genomic hybridization and multiplex ligation-dependent probe amplification in the near future.

SUBMITTER: Rapti M

PROVIDER: S-EPMC8921749 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications.

Rapti Melivoia M Zouaghi Yassine Y Meylan Jenny J Ranza Emmanuelle E Antonarakis Stylianos E SE Santoni Federico A FA

Briefings in bioinformatics 20220301 2

CoverageMaster (CoM) is a copy number variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome [whole exome sequencing (WES)] and genome [whole genome sequencing (WGS)] data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model. CoM processes WES and WGS data at nucleotide scale resolution and accurately detects and visualizes full size ...[more]

PMID: 35224620

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Project description:IntroductionTesting for homologous recombination deficiency (HRD) as a biomarker in relation to poly (ADP-ribose) polymerase inhibitor (PARPi) treatment in ovarian cancer is done by sequencing of the BRCA1/2 genes and/or by assessing a genomic instability signature. Here we present data obtained with two different methods for genomic instability testing: the Oncomine™ Comprehensive Assay Plus (OCA Plus) NGS panel and the OncoScan CNV assay.MethodsThe retrospective analytical study included 80 ovarian cancer samples of patients previously referred to clinical Myriad testing (reference cohort), and 50 ovarian cancer samples from patients collected as part of the Pelvic Mass study. OCA Plus NGS libraries were sequenced with the Ion S5™XL Sequencer and analyzed with the Ion Reporter™ Software v5.20 for calculation of the genomic instability metric (GIM). In addition, all samples were tested with the OncoScan CNV FFPE Assay and analyzed with a previously published R-algorithm for generation of an in-house genomic instability score (in-house GIS).ResultsThe OCA Plus assay had a concordance to the reference of 89% on samples with a tumor fraction ≥ 30% (auto-calculated or via molecular estimation). A total of 15 samples in the reference cohort had a calculated tumor fraction < 30% in the OCA Plus assay. In these, the concordance to reference was only 60%. For the OncoScan CNV in-house GIS a local cutoff point of ≥ 50 was calculated. This gave a concordance to the reference of 85%, with 91% of the samples in the reference cohort passing quality control (QC) on tumor fraction. Both assays had a high sensitivity for the detection of genomic instability in samples with pathogenic or likely pathogenic BRCA1/2 mutations, with 12/13 being GIM positive (OCA Plus assay) and 13/13 being in-house GIS positive (OncoScan CNV assay).ConclusionsThe OCA Plus assay and the OncoScan CNV assay show a high but not complete concordance to reference standard homologous recombination deficiency (HRD) detection. The main reason for QC failure or non-concordance in our study was a low tumor fraction estimated in the assay, despite the selection of material by a pathologist with an inclusion criterion of > 30% tumor. QC steps should include careful tumor content evaluation, and results on samples with < 30% tumor should not be reported.

Dataset Information

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications.

Publications

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications.

Similar Datasets

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