Towards accurate and reliable resolution of structural variants for clinical diagnosis

Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the...

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Autores principales: Liu, Zhichao, Roberts, Ruth, Mercer, Timothy R., Xu, Joshua, Sedlazeck, Fritz J., Tong, Weida
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892125/
https://www.ncbi.nlm.nih.gov/pubmed/35241127
http://dx.doi.org/10.1186/s13059-022-02636-8
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author Liu, Zhichao
Roberts, Ruth
Mercer, Timothy R.
Xu, Joshua
Sedlazeck, Fritz J.
Tong, Weida
author_facet Liu, Zhichao
Roberts, Ruth
Mercer, Timothy R.
Xu, Joshua
Sedlazeck, Fritz J.
Tong, Weida
author_sort Liu, Zhichao
collection PubMed
description Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02636-8.
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spelling pubmed-88921252022-03-04 Towards accurate and reliable resolution of structural variants for clinical diagnosis Liu, Zhichao Roberts, Ruth Mercer, Timothy R. Xu, Joshua Sedlazeck, Fritz J. Tong, Weida Genome Biol Review Structural variants (SVs) are a major source of human genetic diversity and have been associated with different diseases and phenotypes. The detection of SVs is difficult, and a diverse range of detection methods and data analysis protocols has been developed. This difficulty and diversity make the detection of SVs for clinical applications challenging and requires a framework to ensure accuracy and reproducibility. Here, we discuss current developments in the diagnosis of SVs and propose a roadmap for the accurate and reproducible detection of SVs that includes case studies provided from the FDA-led SEquencing Quality Control Phase II (SEQC-II) and other consortium efforts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02636-8. BioMed Central 2022-03-03 /pmc/articles/PMC8892125/ /pubmed/35241127 http://dx.doi.org/10.1186/s13059-022-02636-8 Text en © The Author(s) 2022 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 Review
Liu, Zhichao
Roberts, Ruth
Mercer, Timothy R.
Xu, Joshua
Sedlazeck, Fritz J.
Tong, Weida
Towards accurate and reliable resolution of structural variants for clinical diagnosis
title Towards accurate and reliable resolution of structural variants for clinical diagnosis
title_full Towards accurate and reliable resolution of structural variants for clinical diagnosis
title_fullStr Towards accurate and reliable resolution of structural variants for clinical diagnosis
title_full_unstemmed Towards accurate and reliable resolution of structural variants for clinical diagnosis
title_short Towards accurate and reliable resolution of structural variants for clinical diagnosis
title_sort towards accurate and reliable resolution of structural variants for clinical diagnosis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8892125/
https://www.ncbi.nlm.nih.gov/pubmed/35241127
http://dx.doi.org/10.1186/s13059-022-02636-8
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