Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 4, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac221
Keywords
Variant calling; Structural Variant; Bioinformatics
Funding
- Federal Ministry of Education and Research (BMBF) from the German Federal Ministry for Education and Research [031 L0180]
- National Science Foundation [1705197, 1910885, 2041984, 2135954]
- NIH [R01MH122569]
- Quantitative and Computational Biology Institute
- Department of Computational Medicine at University of California Los Angeles (UCLA)
- National Science Foundation (NSF) [1705197]
- National Institute of Health (NIH) [R25MH109172]
- Direct For Computer & Info Scie & Enginr [1910885] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Office of Advanced Cyberinfrastructure (OAC) [2135954] Funding Source: National Science Foundation
- Div Of Biological Infrastructure
- Direct For Biological Sciences [2041984] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems [1910885] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1705197] Funding Source: National Science Foundation
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This article evaluates the performance of SV detection tools on mouse and human WGS data, using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set. In contrast to previous studies, their gold standard dataset includes a complete set of SVs to report precision and sensitivity rates of the SV detection methods. The study finds wide variation in performance among SV detection tools.
Advances in whole-genome sequencing (WGS) promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from WGS data presents a substantial number of challenges and a plethora of SV detection methods have been developed. Currently, evidence that investigators can use to select appropriate SV detection tools is lacking. In this article, we have evaluated the performance of SV detection tools on mouse and human WGS data using a comprehensive polymerase chain reaction-confirmed gold standard set of SVs and the genome-in-a-bottle variant set, respectively. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of the SV detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance as the SV detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low- and ultralow-pass sequencing data as well as for different deletion length categories.
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