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AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs

BACKGROUND: The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification...

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Autores principales: Fan, Chunna, Wang, Zhonghua, Sun, Yan, Sun, Jun, Liu, Xi, Kang, Licheng, Xu, Yingshuo, Yang, Manqiu, Dai, Wentao, Song, Lijie, Wei, Xiaoming, Xiang, Jiale, Huang, Hui, Zhou, Meizhen, Zeng, Fanwei, Huang, Lin, Xu, Zhengfeng, Peng, Zhiyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496072/
https://www.ncbi.nlm.nih.gov/pubmed/34615484
http://dx.doi.org/10.1186/s12864-021-08011-4
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author Fan, Chunna
Wang, Zhonghua
Sun, Yan
Sun, Jun
Liu, Xi
Kang, Licheng
Xu, Yingshuo
Yang, Manqiu
Dai, Wentao
Song, Lijie
Wei, Xiaoming
Xiang, Jiale
Huang, Hui
Zhou, Meizhen
Zeng, Fanwei
Huang, Lin
Xu, Zhengfeng
Peng, Zhiyu
author_facet Fan, Chunna
Wang, Zhonghua
Sun, Yan
Sun, Jun
Liu, Xi
Kang, Licheng
Xu, Yingshuo
Yang, Manqiu
Dai, Wentao
Song, Lijie
Wei, Xiaoming
Xiang, Jiale
Huang, Hui
Zhou, Meizhen
Zeng, Fanwei
Huang, Lin
Xu, Zhengfeng
Peng, Zhiyu
author_sort Fan, Chunna
collection PubMed
description BACKGROUND: The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification calculator based on scoring metrics, it can only track and tally points that have been assigned based on observed evidence. Here, we developed AutoCNV (a semiautomatic automated CNV interpretation system) based on the standards, which can automatically generate predictions on 18 and 16 criteria for copy number loss and gain, respectively. RESULTS: We assessed the performance of AutoCNV using 72 CNVs evaluated by external independent reviewers and 20 illustrative case examples. Using AutoCNV, it showed that 100 % (72/72) and 95 % (19/20) of CNVs were consistent with the reviewers’ and ClinGen-verified classifications, respectively. AutoCNV only required an average of less than 5 milliseconds to obtain the result for one CNV with automated scoring. We also applied AutoCNV for the interpretation of CNVs from the ClinVar database and the dbVar database. We also developed a web-based version of AutoCNV (wAutoCNV). CONCLUSIONS: AutoCNV may serve to assist users in conducting in-depth CNV interpretation, to accelerate and facilitate the interpretation process of CNVs and to improve the consistency and reliability of CNV interpretation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-08011-4.
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spelling pubmed-84960722021-10-07 AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs Fan, Chunna Wang, Zhonghua Sun, Yan Sun, Jun Liu, Xi Kang, Licheng Xu, Yingshuo Yang, Manqiu Dai, Wentao Song, Lijie Wei, Xiaoming Xiang, Jiale Huang, Hui Zhou, Meizhen Zeng, Fanwei Huang, Lin Xu, Zhengfeng Peng, Zhiyu BMC Genomics Research BACKGROUND: The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification calculator based on scoring metrics, it can only track and tally points that have been assigned based on observed evidence. Here, we developed AutoCNV (a semiautomatic automated CNV interpretation system) based on the standards, which can automatically generate predictions on 18 and 16 criteria for copy number loss and gain, respectively. RESULTS: We assessed the performance of AutoCNV using 72 CNVs evaluated by external independent reviewers and 20 illustrative case examples. Using AutoCNV, it showed that 100 % (72/72) and 95 % (19/20) of CNVs were consistent with the reviewers’ and ClinGen-verified classifications, respectively. AutoCNV only required an average of less than 5 milliseconds to obtain the result for one CNV with automated scoring. We also applied AutoCNV for the interpretation of CNVs from the ClinVar database and the dbVar database. We also developed a web-based version of AutoCNV (wAutoCNV). CONCLUSIONS: AutoCNV may serve to assist users in conducting in-depth CNV interpretation, to accelerate and facilitate the interpretation process of CNVs and to improve the consistency and reliability of CNV interpretation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-021-08011-4. BioMed Central 2021-10-06 /pmc/articles/PMC8496072/ /pubmed/34615484 http://dx.doi.org/10.1186/s12864-021-08011-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fan, Chunna
Wang, Zhonghua
Sun, Yan
Sun, Jun
Liu, Xi
Kang, Licheng
Xu, Yingshuo
Yang, Manqiu
Dai, Wentao
Song, Lijie
Wei, Xiaoming
Xiang, Jiale
Huang, Hui
Zhou, Meizhen
Zeng, Fanwei
Huang, Lin
Xu, Zhengfeng
Peng, Zhiyu
AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs
title AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs
title_full AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs
title_fullStr AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs
title_full_unstemmed AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs
title_short AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs
title_sort autocnv: a semiautomatic cnv interpretation system based on the 2019 acmg/clingen technical standards for cnvs
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496072/
https://www.ncbi.nlm.nih.gov/pubmed/34615484
http://dx.doi.org/10.1186/s12864-021-08011-4
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