A benchmark of structural variation detection by long reads through a realistic simulated model

Accurate simulations of structural variation distributions and sequencing data are crucial for the development and benchmarking of new tools. We develop Sim-it, a straightforward tool for the simulation of both structural variation and long-read data. These simulations from Sim-it reveal the strengt...

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Autores principales: Dierckxsens, Nicolas, Li, Tong, Vermeesch, Joris R., Xie, Zhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672642/
https://www.ncbi.nlm.nih.gov/pubmed/34911553
http://dx.doi.org/10.1186/s13059-021-02551-4
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author Dierckxsens, Nicolas
Li, Tong
Vermeesch, Joris R.
Xie, Zhi
author_facet Dierckxsens, Nicolas
Li, Tong
Vermeesch, Joris R.
Xie, Zhi
author_sort Dierckxsens, Nicolas
collection PubMed
description Accurate simulations of structural variation distributions and sequencing data are crucial for the development and benchmarking of new tools. We develop Sim-it, a straightforward tool for the simulation of both structural variation and long-read data. These simulations from Sim-it reveal the strengths and weaknesses for current available structural variation callers and long-read sequencing platforms. With these findings, we develop a new method (combiSV) that can combine the results from structural variation callers into a superior call set with increased recall and precision, which is also observed for the latest structural variation benchmark set developed by the GIAB Consortium. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02551-4).
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spelling pubmed-86726422021-12-17 A benchmark of structural variation detection by long reads through a realistic simulated model Dierckxsens, Nicolas Li, Tong Vermeesch, Joris R. Xie, Zhi Genome Biol Method Accurate simulations of structural variation distributions and sequencing data are crucial for the development and benchmarking of new tools. We develop Sim-it, a straightforward tool for the simulation of both structural variation and long-read data. These simulations from Sim-it reveal the strengths and weaknesses for current available structural variation callers and long-read sequencing platforms. With these findings, we develop a new method (combiSV) that can combine the results from structural variation callers into a superior call set with increased recall and precision, which is also observed for the latest structural variation benchmark set developed by the GIAB Consortium. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-021-02551-4). BioMed Central 2021-12-15 /pmc/articles/PMC8672642/ /pubmed/34911553 http://dx.doi.org/10.1186/s13059-021-02551-4 Text en © The Author(s) 2021 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 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 Method
Dierckxsens, Nicolas
Li, Tong
Vermeesch, Joris R.
Xie, Zhi
A benchmark of structural variation detection by long reads through a realistic simulated model
title A benchmark of structural variation detection by long reads through a realistic simulated model
title_full A benchmark of structural variation detection by long reads through a realistic simulated model
title_fullStr A benchmark of structural variation detection by long reads through a realistic simulated model
title_full_unstemmed A benchmark of structural variation detection by long reads through a realistic simulated model
title_short A benchmark of structural variation detection by long reads through a realistic simulated model
title_sort benchmark of structural variation detection by long reads through a realistic simulated model
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672642/
https://www.ncbi.nlm.nih.gov/pubmed/34911553
http://dx.doi.org/10.1186/s13059-021-02551-4
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