scholarly journals An enhanced method for targeted next generation sequencing copy number variant detection using ExomeDepth

2017 ◽  
Vol 2 ◽  
pp. 49
Author(s):  
Andrew Parrish ◽  
Richard Caswell ◽  
Garan Jones ◽  
Christopher M. Watson ◽  
Laura A. Crinnion ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Gc Content ◽  
Read Depth ◽  
Next Generation ◽  
Clinical Diagnostic Laboratory ◽  
Targeted Next Generation Sequencing ◽  
Clinical Diagnostic ◽  
Depth Analysis ◽  
Generation Sequencing

Copy number variants (CNV) are a major cause of disease, with over 30,000 reported in the DECIPHER database. To use read depth data from targeted Next Generation Sequencing (NGS) panels to identify CNVs with the highest degree of sensitivity, it is necessary to account for biases inherent in the data. GC content and ambiguous mapping due to repetitive sequence elements and pseudogenes are the principal components of technical variability. In addition, the algorithms used favour the detection of multi-exon CNVs, and rely on suitably matched normal dosage samples for comparison. We developed a calling strategy that subdivides target intervals, and uses pools of historical control samples to overcome these limitations in a clinical diagnostic laboratory. We compared our enhanced strategy with an unmodified pipeline using the R software package ExomeDepth, using a cohort of 109 heterozygous CNVs (91 deletions, 18 duplications in 26 genes), including 25 single exon CNVs. The unmodified pipeline detected 104/109 CNVs, giving a sensitivity of 89.62% to 98.49% at the 95% confidence interval. The detection of all 109 CNVs by our enhanced method demonstrates 95% confidence the sensitivity is ≥96.67%, allowing NGS read depth analysis to be used for CNV detection in a clinical diagnostic setting.

scholarly journals A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Guojun Liu ◽  
Junying Zhang
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Read Depth ◽  
Copy Number Variations ◽  
Experimental Results ◽  
Data Sets ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Cnv Detection

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey’s fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

scholarly journals Targeted Next-Generation Sequencing at Copy-Number Breakpoints for Personalized Analysis of Rearranged Ends in Solid Tumors

PLoS ONE ◽  
2014 ◽  
Vol 9 (6) ◽  
pp. e100089 ◽  
Author(s):  
Hyun-Kyoung Kim ◽  
Won Cheol Park ◽  
Kwang Man Lee ◽  
Hai-Li Hwang ◽  
Seong-Yeol Park ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Solid Tumors ◽  
Copy Number ◽  
Next Generation ◽  
Targeted Next Generation Sequencing ◽  
Generation Sequencing

scholarly journals Targeted Next-Generation Sequencing of 117 Routine Clinical Samples Provides Further Insights into the Molecular Landscape of Uveal Melanoma

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1039 ◽  
Author(s):  
Sophie Thornton ◽  
Sarah Coupland ◽  
Lisa Olohan ◽  
Julie Sibbring ◽  
John Kenny ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Uveal Melanoma ◽  
Copy Number ◽  
Clinical Samples ◽  
Sample Type ◽  
Next Generation ◽  
Hybrid Capture ◽  
Targeted Next Generation Sequencing ◽  
Molecular Landscape ◽  
Generation Sequencing

Uveal melanoma (UM) has well-characterised somatic copy number alterations (SCNA) in chromosomes 1, 3, 6 and 8, in addition to mutations in GNAQ, GNA11, CYSLTR2, PLCB4, BAP1, SF3B1 and EIF1AX, most being linked to metastatic-risk. To gain further insight into the molecular landscape of UM, we designed a targeted next-generation sequencing (NGS) panel to detect SCNA and mutations in routine clinical UM samples. We compared hybrid-capture and amplicon-based target enrichment methods and tested a larger cohort of primary UM samples on the best performing panel. UM clinical samples processed either as fresh-frozen, formalin-fixed paraffin embedded (FFPE), small intraocular biopsies or following irradiation were successfully profiled using NGS, with hybrid capture outperforming the PCR-based enrichment methodology. We identified monosomy 3 (M3)-UM that were wild-type for BAP1 but harbored SF3B1 mutations, novel frameshift deletions in SF3B1 and EIF1AX, as well as a PLCB4 mutation outside of the hotspot on exon 20 coinciding with a GNAQ mutation in some UM. We observed samples that harboured mutations in both BAP1 and SF3B1, and SF3B1 and EIF1AX, respectively. Novel mutations were also identified in TTC28, KTN1, CSMD1 and TP53BP1. NGS can simultaneously assess SCNA and mutation data in UM, in a reliable and reproducible way, irrespective of sample type or previous processing. BAP1 and SF3B1 mutations, in addition to 8q copy number, are of added importance when determining UM patient outcome.

scholarly journals Integration of Technical, Bioinformatic, and Variant Assessment Approaches in the Validation of a Targeted Next-Generation Sequencing Panel for Myeloid Malignancies

2017 ◽  
Vol 141 (6) ◽  
pp. 759-775 ◽  
Author(s):  
Mariam Thomas ◽  
Mahadeo A. Sukhai ◽  
Tong Zhang ◽  
Roozbeh Dolatshahi ◽  
Djamel Harbi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Rare Variants ◽  
Gc Content ◽  
Hematologic Malignancies ◽  
Performance Characteristics ◽  
Next Generation ◽  
Clinical Diagnostic Laboratory ◽  
Diagnostic Laboratory ◽  
Genomic Regions ◽  
Generation Sequencing

Context.— Detection of variants in hematologic malignancies is increasingly important because of a growing number of variants impacting diagnosis, prognosis, and treatment response, and as potential therapeutic targets. The use of next-generation sequencing technologies to detect variants in hematologic malignancies in a clinical diagnostic laboratory setting allows for efficient identification of routinely tested markers in multiple genes simultaneously, as well as the identification of novel and rare variants in other clinically relevant genes. Objective.— To apply a systematic approach to evaluate and validate a commercially available next-generation sequencing panel (TruSight Myeloid Sequencing Panel, Illumina, San Diego, California) targeting 54 genes. In this manuscript, we focused on the parameters that were used to evaluate assay performance characteristics. Data Sources.— Analytical validation was performed using samples containing known variants that had been identified previously. Cases were selected from different disease types, with variants in a range of genes. Panel performance characteristics were assessed and genomic regions requiring additional analysis or wet-bench approaches identified. Conclusions.— We validated the performance characteristics of a myeloid next-generation sequencing panel for detection of variants. The TruSight Myeloid Sequencing Panel covers more than 95% of target regions with depth greater than 500×. However, because of unique variant types such as large insertions or deletions or genomic regions of high GC content, variants in CEBPA, FLT3, and CALR required supplementation with non–next-generation sequencing assays or with informatics approaches to address deficiencies in performance. The use of multiple bioinformatics approaches (2 variant callers and informatics scripts) allows for maximizing calling of true positives, while identifying limitations in using either method alone.

scholarly journals Validation of OncoPanel: A Targeted Next-Generation Sequencing Assay for the Detection of Somatic Variants in Cancer

2017 ◽  
Vol 141 (6) ◽  
pp. 751-758 ◽  
Author(s):  
Elizabeth P. Garcia ◽  
Alissa Minkovsky ◽  
Yonghui Jia ◽  
Matthew D. Ducar ◽  
Priyanka Shivdasani ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Cancer Biology ◽  
Next Generation ◽  
Analytical Validation ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Insertions And Deletions ◽  
Targeted Next Generation Sequencing ◽  
Generation Sequencing

Context.— The analysis of somatic mutations across multiple genes in cancer specimens may be used to aid clinical decision making. The analytical validation of targeted next-generation sequencing panels is important to assess accuracy and limitations. Objective.— To report the development and validation of OncoPanel, a custom targeted next-generation sequencing assay for cancer. Design.— OncoPanel was designed for the detection of single-nucleotide variants, insertions and deletions, copy number alterations, and structural variants across 282 genes with evidence as drivers of cancer biology. We implemented a validation strategy using formalin-fixed, paraffin-embedded, fresh or frozen samples compared with results obtained by clinically validated orthogonal technologies. Results.— OncoPanel achieved 98% sensitivity and 100% specificity for the detection of single-nucleotide variants, and 84% sensitivity and 100% specificity for the detection of insertions and deletions compared with single-gene assays and mass spectrometry–based genotyping. Copy number detection achieved 86% sensitivity and 98% specificity compared with array comparative genomic hybridization. The sensitivity of structural variant detection was 74% compared with karyotype, fluorescence in situ hybridization, and polymerase chain reaction. Sensitivity was affected by inconsistency in the detection of FLT3 and NPM1 alterations and IGH rearrangements due to design limitations. Limit of detection studies demonstrated 98.4% concordance across triplicate runs for variants with allele fraction greater than 0.1 and at least 50× coverage. Conclusions.— The analytical validation of OncoPanel demonstrates the ability of targeted next-generation sequencing to detect multiple types of genetic alterations across a panel of genes implicated in cancer biology.

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