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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7683568 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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