ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets
Accurate benchmarking of structural variant (SV) detection is essential for advancing the development and application of human whole-genome sequencing (WGS). A fundamental challenge in benchmarking SV detection results is determining whether two SVs represent the same event. Differences in the varia...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025002612 |
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| author | Peizheng Mu Xiangyan Feng Lanxin Tong Jie Huang Chaoqun Zhu Fei Wang Wei Quan Yuanjun Ma Yucui Dong Xiao Zhu |
| author_facet | Peizheng Mu Xiangyan Feng Lanxin Tong Jie Huang Chaoqun Zhu Fei Wang Wei Quan Yuanjun Ma Yucui Dong Xiao Zhu |
| author_sort | Peizheng Mu |
| collection | DOAJ |
| description | Accurate benchmarking of structural variant (SV) detection is essential for advancing the development and application of human whole-genome sequencing (WGS). A fundamental challenge in benchmarking SV detection results is determining whether two SVs represent the same event. Differences in the variation-awareness and strategic implementation of aligners inherently constrain SV detection algorithms that rely on alignment-based approaches. Traditional benchmarking, which primarily focuses on comparing and matching individual variants, makes it difficult to capture the relationships between multiple adjacent variants. We introduced ASVBM, an improved benchmarking framework that introduces the notion of latent positives and leverages a joint analysis and validation strategy based on local variants. This performance improvement arose from the discovery that multiple smaller variants are nearly equivalent to a larger variant. We comprehensively evaluated the performance of six state-of-the-art variant calling pipelines using real WGS datasets. According to multiple matching criteria, ASVBM employs a joint analysis strategy to uncover potential equivalences between the callset and the benchmark set, thereby reducing false mismatches caused by differences in variant representation. ASVBM is available at https://github.com/zhuxiao/asvbm. |
| format | Article |
| id | doaj-art-a241992555d24d3fbe71fc48d51f87ce |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-a241992555d24d3fbe71fc48d51f87ce2025-08-20T02:43:50ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01272851286210.1016/j.csbj.2025.06.045ASVBM: Structural variant benchmarking with local joint analysis for multiple callsetsPeizheng Mu0Xiangyan Feng1Lanxin Tong2Jie Huang3Chaoqun Zhu4Fei Wang5Wei Quan6Yuanjun Ma7Yucui Dong8Xiao Zhu9School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, ChinaDepartment of Hematology, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong 264009, ChinaGuangzhou Dublin International College of Life Sciences and Technology (GDIC), South China Agricultural University, Guangzhou, Guangdong 510642, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, ChinaSchool of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, ChinaDepartment of Immunology, Binzhou Medical University, Yantai, Shandong 264003, China; Corresponding authors.School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China; Corresponding authors.Accurate benchmarking of structural variant (SV) detection is essential for advancing the development and application of human whole-genome sequencing (WGS). A fundamental challenge in benchmarking SV detection results is determining whether two SVs represent the same event. Differences in the variation-awareness and strategic implementation of aligners inherently constrain SV detection algorithms that rely on alignment-based approaches. Traditional benchmarking, which primarily focuses on comparing and matching individual variants, makes it difficult to capture the relationships between multiple adjacent variants. We introduced ASVBM, an improved benchmarking framework that introduces the notion of latent positives and leverages a joint analysis and validation strategy based on local variants. This performance improvement arose from the discovery that multiple smaller variants are nearly equivalent to a larger variant. We comprehensively evaluated the performance of six state-of-the-art variant calling pipelines using real WGS datasets. According to multiple matching criteria, ASVBM employs a joint analysis strategy to uncover potential equivalences between the callset and the benchmark set, thereby reducing false mismatches caused by differences in variant representation. ASVBM is available at https://github.com/zhuxiao/asvbm.http://www.sciencedirect.com/science/article/pii/S2001037025002612SV benchmarkingSV matchingJoint analysisStructural variant |
| spellingShingle | Peizheng Mu Xiangyan Feng Lanxin Tong Jie Huang Chaoqun Zhu Fei Wang Wei Quan Yuanjun Ma Yucui Dong Xiao Zhu ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets Computational and Structural Biotechnology Journal SV benchmarking SV matching Joint analysis Structural variant |
| title | ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets |
| title_full | ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets |
| title_fullStr | ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets |
| title_full_unstemmed | ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets |
| title_short | ASVBM: Structural variant benchmarking with local joint analysis for multiple callsets |
| title_sort | asvbm structural variant benchmarking with local joint analysis for multiple callsets |
| topic | SV benchmarking SV matching Joint analysis Structural variant |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025002612 |
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