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ClassifyCNV: a tool for clinical annotation of copy-number variants

Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evalu...

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Autores principales: Gurbich, Tatiana A., Ilinsky, Valery Vladimirovich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683568/
https://www.ncbi.nlm.nih.gov/pubmed/33230148
http://dx.doi.org/10.1038/s41598-020-76425-3
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author Gurbich, Tatiana A.
Ilinsky, Valery Vladimirovich
author_facet Gurbich, Tatiana A.
Ilinsky, Valery Vladimirovich
author_sort Gurbich, Tatiana A.
collection PubMed
description Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV’s performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https://github.com/Genotek/ClassifyCNV.
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spelling pubmed-76835682020-11-24 ClassifyCNV: a tool for clinical annotation of copy-number variants Gurbich, Tatiana A. Ilinsky, Valery Vladimirovich Sci Rep Article Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV’s performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https://github.com/Genotek/ClassifyCNV. Nature Publishing Group UK 2020-11-23 /pmc/articles/PMC7683568/ /pubmed/33230148 http://dx.doi.org/10.1038/s41598-020-76425-3 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Gurbich, Tatiana A.
Ilinsky, Valery Vladimirovich
ClassifyCNV: a tool for clinical annotation of copy-number variants
title ClassifyCNV: a tool for clinical annotation of copy-number variants
title_full ClassifyCNV: a tool for clinical annotation of copy-number variants
title_fullStr ClassifyCNV: a tool for clinical annotation of copy-number variants
title_full_unstemmed ClassifyCNV: a tool for clinical annotation of copy-number variants
title_short ClassifyCNV: a tool for clinical annotation of copy-number variants
title_sort classifycnv: a tool for clinical annotation of copy-number variants
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683568/
https://www.ncbi.nlm.nih.gov/pubmed/33230148
http://dx.doi.org/10.1038/s41598-020-76425-3
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