CNV detection in targeted NGS panel data
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB18961 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The aim of this study was to compare copy-number-variation (CNV) detection methods for targeted NGS panel data in a clinical diagnostic setting. We present targeted NGS panel data from 170 samples that were processed using the TruSight(TM) Cancer (TSC) panel (Illumina, San Diego, CA, USA), which targets 94 genes and 284 SNPs associated with a predisposition towards cancer. The samples are enriched for CNVs in the genes of interest. All CNVs have previously been assessed with MLPA and can therefore be considered as confirmed.
| EGAS00001002481 | EGA
Project description:In order to evaluate the performance of CNV detection in next-generation sequencing platform in varied sample types, we employed chromosomal microarray analysis (CMA) for validation of the samples with NGS-based detection results (NCBI Sequence Read Archive with accession number SRA296708). Besides array Comparative Genomics Hybridization (aCGH, Agilent) , we used a commerical SNP-array (Illumina) including early abortus, induced termination, prenatal samples and postnatal samples. CMA results were compared with NGS-based detection results. 100% consistency was obtained between NGS-based approach and CMA in pathogenic or likely pathogenic CNVs detection.
2015-10-01 | GSE73190 | GEO
Project description:Ovarian cancer is a global problem, is typically diagnosed at a late stage and has no effective screening strategy. Platinum-based chemotherapy or Poly(ADP-ribose) polymerase inhibitors (PARPis) treatment are most frequently applied for ovarian cancer patients who are inoperable and in the advanced stage. The recognition of homologous recombination deficiency (HRD) as a biomarker to predict the effect of Platinum-based or PARPis treatment. WGS and WES can detect tumor HRD status but have several disadvantages which restrict their clinical application. My choice HRD CDx and Foundation Focus CDx are approved by FDA for HRD detection, however, whether they are applicable to the Chinese population or not is unknown. In this study, we created an SNP-based Tg-NGS panel to fill in gaps in Chinese patients’ HRD screening. Our results showed that the panel is cost and time-saving compared with WGS, but equivalent with SNP microarray on CNV and HRD detection. In summary, this newly developed kit is promising in clinical application to guide ovarian cancer and even other cancer types therapy.
2023-02-21 | GSE203509 | GEO
Project description:In order to evaluate the performance of CNV detection in next-generation sequencing platform in varied sample types, we employed chromosomal microarray analysis (CMA) for validation of the samples with NGS-based detection results (NCBI Sequence Read Archive with accession number SRA296708). Besides snp-array, we used a customized array Comparative Genomics Hybridization (aCGH, Agilent) approach for a cohort of clinical samples including early abortus, induced termination, prenatal samples and postnatal samples. CMA results were compared with NGS-based detection results. 100% consistency was obtained between NGS-based approach and CMA in pathogenic or likely pathogenic CNVs detection.
2015-10-01 | GSE72891 | GEO
Project description:Although germline copy-number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. Five tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth, and CODEX2) were tested against four genetic diagnostics datasets (two in-house and two external) for a total of 495 samples with 231 single and multi-exon validated CNVs. The evaluation was performed using the default and sensitivity-optimized parameters. Results showed that most tools were highly sensitive and specific, but the performance was dataset dependant. When evaluating them in our diagnostics scenario, DECoN and panelcn.MOPS detected all CNVs with the exception of one mosaic CNV missed by DECoN. However, DECoN outperformed panelcn.MOPS specificity achieving values greater than 0.90 when using the optimized parameters. In our in-house datasets, DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation. Benchmarking and optimization code is freely available at https://github.com/TranslationalBioinformaticsIGTP/CNVbenchmarkeR .
| S-EPMC7784926 | biostudies-literature
Project description:NanoString nCounter Cancer CN panel assay CNV detection of high-grade serous carcinoma samples
| PRJNA1022412 | ENA
Project description:Detection of CNV in LMP1 mouse lymphomas
| PRJNA561168 | ENA
Project description: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.
| S-EPMC5518446 | biostudies-literature
Project description:We have benchmarked the performance of cancer CNV calling by six most recent software tools on their detection accuracy, sensitivity, and reproducibility. We also explored the consistency of CNV calling across different orthogonal technologies, including optical mapping and microarrays. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we established a high-confidence CNV call set for the reference sample.
2023-08-25 | GSE212051 | GEO
Project description:Interventions: Gold Standard:Histopathological diagnosis;Index test:AmoyDx Essential NGS Panel (Reversible Terminator Sequencing)
Primary outcome(s): EGFR 20 insertion mutation;consistency
Study Design: Factorial
| 2699936 | ecrin-mdr-crc