Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.
Structural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV de...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314982 |
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| author | De-Min Duan Chinyi Cheng Yu-Shu Huang An-Ko Chung Pin-Xuan Chen Yu-An Chen Jacob Shujui Hsu Pei-Lung Chen |
| author_facet | De-Min Duan Chinyi Cheng Yu-Shu Huang An-Ko Chung Pin-Xuan Chen Yu-An Chen Jacob Shujui Hsu Pei-Lung Chen |
| author_sort | De-Min Duan |
| collection | DOAJ |
| description | Structural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV detection, short-read sequencing remains essential due to practical and cost considerations, as well as the need to analyze existing short-read datasets. Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. In this study, we evaluated the efficacy of six advanced SV detection algorithms, including the commercial software DRAGEN, using the GIAB v0.6 Tier 1 benchmark and HGSVC2 cell lines. We employed both individual and combination strategies, with systematic assessments of recall, precision, and F1 scores. Our results demonstrate that the union combination approach enhanced detection capabilities, surpassing single algorithms in identifying deletions and insertions, and delivered comparable recall and F1 scores to the commercial software DRAGEN. Interestingly, expanding the number of algorithms from three to five in the combination did not enhance performance, highlighting the efficiency of a well-chosen ensemble over a larger algorithmic pool. |
| format | Article |
| id | doaj-art-1fcea99f2ee0464db8927772169cd99a |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1fcea99f2ee0464db8927772169cd99a2025-02-12T05:30:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031498210.1371/journal.pone.0314982Comparisons of performances of structural variants detection algorithms in solitary or combination strategy.De-Min DuanChinyi ChengYu-Shu HuangAn-Ko ChungPin-Xuan ChenYu-An ChenJacob Shujui HsuPei-Lung ChenStructural variants (SVs) have been associated with changes in gene expression, which may contribute to alterations in phenotypes and disease development. However, the precise identification and characterization of SVs remain challenging. While long-read sequencing offers superior accuracy for SV detection, short-read sequencing remains essential due to practical and cost considerations, as well as the need to analyze existing short-read datasets. Numerous algorithms for short-read SV detection exist, but none are universally optimal, each having limitations for specific SV sizes and types. In this study, we evaluated the efficacy of six advanced SV detection algorithms, including the commercial software DRAGEN, using the GIAB v0.6 Tier 1 benchmark and HGSVC2 cell lines. We employed both individual and combination strategies, with systematic assessments of recall, precision, and F1 scores. Our results demonstrate that the union combination approach enhanced detection capabilities, surpassing single algorithms in identifying deletions and insertions, and delivered comparable recall and F1 scores to the commercial software DRAGEN. Interestingly, expanding the number of algorithms from three to five in the combination did not enhance performance, highlighting the efficiency of a well-chosen ensemble over a larger algorithmic pool.https://doi.org/10.1371/journal.pone.0314982 |
| spellingShingle | De-Min Duan Chinyi Cheng Yu-Shu Huang An-Ko Chung Pin-Xuan Chen Yu-An Chen Jacob Shujui Hsu Pei-Lung Chen Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. PLoS ONE |
| title | Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. |
| title_full | Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. |
| title_fullStr | Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. |
| title_full_unstemmed | Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. |
| title_short | Comparisons of performances of structural variants detection algorithms in solitary or combination strategy. |
| title_sort | comparisons of performances of structural variants detection algorithms in solitary or combination strategy |
| url | https://doi.org/10.1371/journal.pone.0314982 |
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