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Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data
BACKGROUND: Structural variants (SVs) play a crucial role in gene regulation, trait association, and disease in humans. SV genotyping has been extensively applied in genomics research and clinical diagnosis. Although a growing number of SV genotyping methods for long reads have been developed, a com...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9034514/ https://www.ncbi.nlm.nih.gov/pubmed/35461238 http://dx.doi.org/10.1186/s12864-022-08548-y |
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author | Duan, Xiaoke Pan, Mingpei Fan, Shaohua |
author_facet | Duan, Xiaoke Pan, Mingpei Fan, Shaohua |
author_sort | Duan, Xiaoke |
collection | PubMed |
description | BACKGROUND: Structural variants (SVs) play a crucial role in gene regulation, trait association, and disease in humans. SV genotyping has been extensively applied in genomics research and clinical diagnosis. Although a growing number of SV genotyping methods for long reads have been developed, a comprehensive performance assessment of these methods has yet to be done. RESULTS: Based on one simulated and three real SV datasets, we performed an in-depth evaluation of five SV genotyping methods, including cuteSV, LRcaller, Sniffles, SVJedi, and VaPoR. The results show that for insertions and deletions, cuteSV and LRcaller have similar F1 scores (cuteSV, insertions: 0.69–0.90, deletions: 0.77–0.90 and LRcaller, insertions: 0.67–0.87, deletions: 0.74–0.91) and are superior to other methods. For duplications, inversions, and translocations, LRcaller yields the most accurate genotyping results (0.84, 0.68, and 0.47, respectively). When genotyping SVs located in tandem repeat region or with imprecise breakpoints, cuteSV (insertions and deletions) and LRcaller (duplications, inversions, and translocations) are better than other methods. In addition, we observed a decrease in F1 scores when the SV size increased. Finally, our analyses suggest that the F1 scores of these methods reach the point of diminishing returns at 20× depth of coverage. CONCLUSIONS: We present an in-depth benchmark study of long-read SV genotyping methods. Our results highlight the advantages and disadvantages of each genotyping method, which provide practical guidance for optimal application selection and prospective directions for tool improvement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08548-y. |
format | Online Article Text |
id | pubmed-9034514 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-90345142022-04-24 Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data Duan, Xiaoke Pan, Mingpei Fan, Shaohua BMC Genomics Research BACKGROUND: Structural variants (SVs) play a crucial role in gene regulation, trait association, and disease in humans. SV genotyping has been extensively applied in genomics research and clinical diagnosis. Although a growing number of SV genotyping methods for long reads have been developed, a comprehensive performance assessment of these methods has yet to be done. RESULTS: Based on one simulated and three real SV datasets, we performed an in-depth evaluation of five SV genotyping methods, including cuteSV, LRcaller, Sniffles, SVJedi, and VaPoR. The results show that for insertions and deletions, cuteSV and LRcaller have similar F1 scores (cuteSV, insertions: 0.69–0.90, deletions: 0.77–0.90 and LRcaller, insertions: 0.67–0.87, deletions: 0.74–0.91) and are superior to other methods. For duplications, inversions, and translocations, LRcaller yields the most accurate genotyping results (0.84, 0.68, and 0.47, respectively). When genotyping SVs located in tandem repeat region or with imprecise breakpoints, cuteSV (insertions and deletions) and LRcaller (duplications, inversions, and translocations) are better than other methods. In addition, we observed a decrease in F1 scores when the SV size increased. Finally, our analyses suggest that the F1 scores of these methods reach the point of diminishing returns at 20× depth of coverage. CONCLUSIONS: We present an in-depth benchmark study of long-read SV genotyping methods. Our results highlight the advantages and disadvantages of each genotyping method, which provide practical guidance for optimal application selection and prospective directions for tool improvement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-022-08548-y. BioMed Central 2022-04-23 /pmc/articles/PMC9034514/ /pubmed/35461238 http://dx.doi.org/10.1186/s12864-022-08548-y 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, visithttp://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 Duan, Xiaoke Pan, Mingpei Fan, Shaohua Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
title | Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
title_full | Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
title_fullStr | Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
title_full_unstemmed | Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
title_short | Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
title_sort | comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9034514/ https://www.ncbi.nlm.nih.gov/pubmed/35461238 http://dx.doi.org/10.1186/s12864-022-08548-y |
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