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The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data

Detection of deletions and duplications of whole exons (exon CNVs) is a key requirement of genetic testing. Accurate detection of this variant type has proved very challenging in targeted next-generation sequencing (NGS) data, particularly if only a single exon is involved. Many different NGS exon C...

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Autores principales: Mahamdallie, Shazia, Ruark, Elise, Yost, Shawn, Ramsay, Emma, Uddin, Imran, Wylie, Harriett, Elliott, Anna, Strydom, Ann, Renwick, Anthony, Seal, Sheila, Rahman, Nazneen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473400/
https://www.ncbi.nlm.nih.gov/pubmed/28630945
http://dx.doi.org/10.12688/wellcomeopenres.11689.1
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author Mahamdallie, Shazia
Ruark, Elise
Yost, Shawn
Ramsay, Emma
Uddin, Imran
Wylie, Harriett
Elliott, Anna
Strydom, Ann
Renwick, Anthony
Seal, Sheila
Rahman, Nazneen
author_facet Mahamdallie, Shazia
Ruark, Elise
Yost, Shawn
Ramsay, Emma
Uddin, Imran
Wylie, Harriett
Elliott, Anna
Strydom, Ann
Renwick, Anthony
Seal, Sheila
Rahman, Nazneen
author_sort Mahamdallie, Shazia
collection PubMed
description Detection of deletions and duplications of whole exons (exon CNVs) is a key requirement of genetic testing. Accurate detection of this variant type has proved very challenging in targeted next-generation sequencing (NGS) data, particularly if only a single exon is involved. Many different NGS exon CNV calling methods have been developed over the last five years. Such methods are usually evaluated using simulated and/or in-house data due to a lack of publicly-available datasets with orthogonally generated results. This hinders tool comparisons, transparency and reproducibility. To provide a community resource for assessment of exon CNV calling methods in targeted NGS data, we here present the ICR96 exon CNV validation series. The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM or PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs, the most difficult type of exon CNV to detect. The FASTQ files for the ICR96 exon CNV validation series can be accessed through the European-Genome phenome Archive (EGA) under the accession number EGAS00001002428.
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spelling pubmed-54734002017-06-19 The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data Mahamdallie, Shazia Ruark, Elise Yost, Shawn Ramsay, Emma Uddin, Imran Wylie, Harriett Elliott, Anna Strydom, Ann Renwick, Anthony Seal, Sheila Rahman, Nazneen Wellcome Open Res Data Note Detection of deletions and duplications of whole exons (exon CNVs) is a key requirement of genetic testing. Accurate detection of this variant type has proved very challenging in targeted next-generation sequencing (NGS) data, particularly if only a single exon is involved. Many different NGS exon CNV calling methods have been developed over the last five years. Such methods are usually evaluated using simulated and/or in-house data due to a lack of publicly-available datasets with orthogonally generated results. This hinders tool comparisons, transparency and reproducibility. To provide a community resource for assessment of exon CNV calling methods in targeted NGS data, we here present the ICR96 exon CNV validation series. The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM or PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs, the most difficult type of exon CNV to detect. The FASTQ files for the ICR96 exon CNV validation series can be accessed through the European-Genome phenome Archive (EGA) under the accession number EGAS00001002428. F1000Research 2017-05-26 /pmc/articles/PMC5473400/ /pubmed/28630945 http://dx.doi.org/10.12688/wellcomeopenres.11689.1 Text en Copyright: © 2017 Mahamdallie S 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 Data Note
Mahamdallie, Shazia
Ruark, Elise
Yost, Shawn
Ramsay, Emma
Uddin, Imran
Wylie, Harriett
Elliott, Anna
Strydom, Ann
Renwick, Anthony
Seal, Sheila
Rahman, Nazneen
The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data
title The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data
title_full The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data
title_fullStr The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data
title_full_unstemmed The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data
title_short The ICR96 exon CNV validation series: a resource for orthogonal assessment of exon CNV calling in NGS data
title_sort icr96 exon cnv validation series: a resource for orthogonal assessment of exon cnv calling in ngs data
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473400/
https://www.ncbi.nlm.nih.gov/pubmed/28630945
http://dx.doi.org/10.12688/wellcomeopenres.11689.1
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