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Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN

Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS pane...

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Autores principales: Fowler, Anna, Mahamdallie, Shazia, Ruark, Elise, Seal, Sheila, Ramsay, Emma, Clarke, Matthew, Uddin, Imran, Wylie, Harriet, Strydom, Ann, Lunter, Gerton, Rahman, Nazneen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409526/
https://www.ncbi.nlm.nih.gov/pubmed/28459104
http://dx.doi.org/10.12688/wellcomeopenres.10069.1
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author Fowler, Anna
Mahamdallie, Shazia
Ruark, Elise
Seal, Sheila
Ramsay, Emma
Clarke, Matthew
Uddin, Imran
Wylie, Harriet
Strydom, Ann
Lunter, Gerton
Rahman, Nazneen
author_facet Fowler, Anna
Mahamdallie, Shazia
Ruark, Elise
Seal, Sheila
Ramsay, Emma
Clarke, Matthew
Uddin, Imran
Wylie, Harriet
Strydom, Ann
Lunter, Gerton
Rahman, Nazneen
author_sort Fowler, Anna
collection PubMed
description Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs.  Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA).  Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%.  Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon.
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spelling pubmed-54095262017-04-28 Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN Fowler, Anna Mahamdallie, Shazia Ruark, Elise Seal, Sheila Ramsay, Emma Clarke, Matthew Uddin, Imran Wylie, Harriet Strydom, Ann Lunter, Gerton Rahman, Nazneen Wellcome Open Res Method Article Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, ‘exon CNVs’) in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs.  Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA).  Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%.  Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate. DECoN is freely available at www.icr.ac.uk/decon. F1000Research 2016-11-25 /pmc/articles/PMC5409526/ /pubmed/28459104 http://dx.doi.org/10.12688/wellcomeopenres.10069.1 Text en Copyright: © 2016 Fowler A et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Fowler, Anna
Mahamdallie, Shazia
Ruark, Elise
Seal, Sheila
Ramsay, Emma
Clarke, Matthew
Uddin, Imran
Wylie, Harriet
Strydom, Ann
Lunter, Gerton
Rahman, Nazneen
Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
title Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
title_full Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
title_fullStr Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
title_full_unstemmed Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
title_short Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN
title_sort accurate clinical detection of exon copy number variants in a targeted ngs panel using decon
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409526/
https://www.ncbi.nlm.nih.gov/pubmed/28459104
http://dx.doi.org/10.12688/wellcomeopenres.10069.1
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