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Visualization tools for human structural variations identified by whole-genome sequencing

Visualizing structural variations (SVs) is a critical step for finding associations between SVs and human traits or diseases. Given that there are many sequencing platforms used for SV identification and given that how best to visualize SVs together with other data, such as read alignments and annot...

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Detalles Bibliográficos
Autores principales: Yokoyama, Toshiyuki T., Kasahara, Masahiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075883/
https://www.ncbi.nlm.nih.gov/pubmed/31666648
http://dx.doi.org/10.1038/s10038-019-0687-0
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author Yokoyama, Toshiyuki T.
Kasahara, Masahiro
author_facet Yokoyama, Toshiyuki T.
Kasahara, Masahiro
author_sort Yokoyama, Toshiyuki T.
collection PubMed
description Visualizing structural variations (SVs) is a critical step for finding associations between SVs and human traits or diseases. Given that there are many sequencing platforms used for SV identification and given that how best to visualize SVs together with other data, such as read alignments and annotations, depends on research goals, there are dozens of SV visualization tools designed for different research goals and sequencing platforms. Here, we provide a comprehensive survey of over 30 SV visualization tools to help users choose which tools to use. This review targets users who wish to visualize a set of SVs identified from the massively parallel sequencing reads of an individual human genome. We first categorize the ways in which SV visualization tools display SVs into ten major categories, which we denote as view modules. View modules allow readers to understand the features of each SV visualization tool quickly. Next, we introduce the features of individual SV visualization tools from several aspects, including whether SV views are integrated with annotations, whether long-read alignment is displayed, whether underlying data structures are graph-based, the type of SVs shown, whether auditing is possible, whether bird’s eye view is available, sequencing platforms, and the number of samples. We hope that this review will serve as a guide for readers on the currently available SV visualization tools and lead to the development of new SV visualization tools in the near future.
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spelling pubmed-80758832021-05-06 Visualization tools for human structural variations identified by whole-genome sequencing Yokoyama, Toshiyuki T. Kasahara, Masahiro J Hum Genet Review Article Visualizing structural variations (SVs) is a critical step for finding associations between SVs and human traits or diseases. Given that there are many sequencing platforms used for SV identification and given that how best to visualize SVs together with other data, such as read alignments and annotations, depends on research goals, there are dozens of SV visualization tools designed for different research goals and sequencing platforms. Here, we provide a comprehensive survey of over 30 SV visualization tools to help users choose which tools to use. This review targets users who wish to visualize a set of SVs identified from the massively parallel sequencing reads of an individual human genome. We first categorize the ways in which SV visualization tools display SVs into ten major categories, which we denote as view modules. View modules allow readers to understand the features of each SV visualization tool quickly. Next, we introduce the features of individual SV visualization tools from several aspects, including whether SV views are integrated with annotations, whether long-read alignment is displayed, whether underlying data structures are graph-based, the type of SVs shown, whether auditing is possible, whether bird’s eye view is available, sequencing platforms, and the number of samples. We hope that this review will serve as a guide for readers on the currently available SV visualization tools and lead to the development of new SV visualization tools in the near future. Springer Singapore 2019-10-30 2020 /pmc/articles/PMC8075883/ /pubmed/31666648 http://dx.doi.org/10.1038/s10038-019-0687-0 Text en © The Author(s) 2019 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Review Article
Yokoyama, Toshiyuki T.
Kasahara, Masahiro
Visualization tools for human structural variations identified by whole-genome sequencing
title Visualization tools for human structural variations identified by whole-genome sequencing
title_full Visualization tools for human structural variations identified by whole-genome sequencing
title_fullStr Visualization tools for human structural variations identified by whole-genome sequencing
title_full_unstemmed Visualization tools for human structural variations identified by whole-genome sequencing
title_short Visualization tools for human structural variations identified by whole-genome sequencing
title_sort visualization tools for human structural variations identified by whole-genome sequencing
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075883/
https://www.ncbi.nlm.nih.gov/pubmed/31666648
http://dx.doi.org/10.1038/s10038-019-0687-0
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