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

2.50
Hdl Handle:
http://hdl.handle.net/11287/620418
Title:
An enhanced method for targeted next generation sequencing copy number variant detection using ExomeDepth
Authors:
Parrish, A.; Caswell, R.; Jones, G.; Watson, C. M.; Crinnion, L. A.; Ellard, Sian ( 0000-0002-7620-5526 )
Abstract:
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.
Citation:
An enhanced method for targeted next generation sequencing copy number variant detection using ExomeDepth 2017, 2:49 Wellcome Open Research
Publisher:
Wellcome
Journal:
Wellcome Open Research
Issue Date:
14-Jul-2017
URI:
http://hdl.handle.net/11287/620418
DOI:
10.12688/wellcomeopenres.11548.1
Additional Links:
https://wellcomeopenresearch.org/articles/2-49/v1
Note:
This article is freely available online. Click on the Additional Link above to access the full-text via the publisher's site.
Type:
Journal Article
Language:
en
ISSN:
2398-502X
Appears in Collections:
Molecular Genetics; 2017 RD&E publications

Full metadata record

DC FieldValue Language
dc.contributor.authorParrish, A.en
dc.contributor.authorCaswell, R.en
dc.contributor.authorJones, G.en
dc.contributor.authorWatson, C. M.en
dc.contributor.authorCrinnion, L. A.en
dc.contributor.authorEllard, Sianen
dc.date.accessioned2017-10-04T12:20:46Z-
dc.date.available2017-10-04T12:20:46Z-
dc.date.issued2017-07-14-
dc.identifier.citationAn enhanced method for targeted next generation sequencing copy number variant detection using ExomeDepth 2017, 2:49 Wellcome Open Researchen
dc.identifier.issn2398-502X-
dc.identifier.doi10.12688/wellcomeopenres.11548.1-
dc.identifier.urihttp://hdl.handle.net/11287/620418-
dc.description.abstractCopy 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.en
dc.language.isoenen
dc.publisherWellcomeen
dc.relation.urlhttps://wellcomeopenresearch.org/articles/2-49/v1en
dc.rightsArchived with thanks to Wellcome Open Researchen
dc.subjectWessex Classification Subject Headings::Oncology. Pathology.::Geneticsen
dc.titleAn enhanced method for targeted next generation sequencing copy number variant detection using ExomeDepthen
dc.typeJournal Articleen
dc.identifier.journalWellcome Open Researchen
dc.description.noteThis article is freely available online. Click on the Additional Link above to access the full-text via the publisher's site.en
dc.type.versionPublisheden
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