Abstract:Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studi… Show more
“…We found that similar tool combinations (especially cuteSV, followed closely by Sniffles2 and dysgu after minimap2 alignment) had superior performance across all the simulated datasets. The findings are in line with a recent study reporting that cuteSV performed better than other tested SV tools such as Sniffles1, SVIM, and pbsv for precision and recall at both SV calling and genotyping in human datasets (Bolognini and Magi, 2021). Increasing coverage improved recall and F1-scores for all tested SVs calling combinations, confirming that the probability of detecting quality SVs increases with more sequencing coverage (Jiang et al, 2021).…”
Section: Resultssupporting
confidence: 91%
“…Many of the SV detection tools are benchmarked primarily on human/animal datasets, (Bolognini and Magi, 2021; Coster et al, 2019; Dierckxsens et al, 2021; Jiang et al, 2020; Jiang et al, 2021; Zhou et al, 2019), however the complexity and different SV profiles of crop plant genomes might bring unique challenges. Therefore, to guide the design of large-scale long-read re-sequencing studies, this study performed comprehensive benchmarking of popular SV calling tools with a focus on tool performance at lower sequencing coverage.…”
Section: Resultsmentioning
confidence: 99%
“…Overall, the results found minimap2 to be the best performing aligner for SV calling applications, which also had the fastest run time and the most mapped bases. Recent benchmarking studies on human data also recommended minimap2 among tested aligners such as GraphMap, LAST, and NGMLR (Bolognini and Magi, 2021; Coster et al, 2019; Zhou et al, 2019).…”
Structural variations (SVs) are larger polymorphisms (>50 bp in length), which consist of insertions, deletions, inversions, duplications, and translocations. They can have a strong impact on agronomical traits and play an important role in environmental adaptation. The development of long-read sequencing technologies, including Oxford Nanopore, allows for comprehensive SV discovery and characterization even in complex polyploid crop genomes. However, many of the SV discovery pipeline benchmarks do not include complex plant genome datasets. In this study, we benchmarked popular long-read alignment-based SV detection tools for crop plant genomes. We used real and simulated Oxford Nanopore reads for two crops, allotetraploid Brassica napus (oilseed rape) and diploid Solanum lycopersicum (tomato), and evaluated several read aligners and SV callers across 5x, 10x, and 20x coverages typically used in re-sequencing studies. Our benchmarks provide a useful guide for designing Oxford Nanopore re-sequencing projects and SV discovery pipelines for crop plants.
“…We found that similar tool combinations (especially cuteSV, followed closely by Sniffles2 and dysgu after minimap2 alignment) had superior performance across all the simulated datasets. The findings are in line with a recent study reporting that cuteSV performed better than other tested SV tools such as Sniffles1, SVIM, and pbsv for precision and recall at both SV calling and genotyping in human datasets (Bolognini and Magi, 2021). Increasing coverage improved recall and F1-scores for all tested SVs calling combinations, confirming that the probability of detecting quality SVs increases with more sequencing coverage (Jiang et al, 2021).…”
Section: Resultssupporting
confidence: 91%
“…Many of the SV detection tools are benchmarked primarily on human/animal datasets, (Bolognini and Magi, 2021; Coster et al, 2019; Dierckxsens et al, 2021; Jiang et al, 2020; Jiang et al, 2021; Zhou et al, 2019), however the complexity and different SV profiles of crop plant genomes might bring unique challenges. Therefore, to guide the design of large-scale long-read re-sequencing studies, this study performed comprehensive benchmarking of popular SV calling tools with a focus on tool performance at lower sequencing coverage.…”
Section: Resultsmentioning
confidence: 99%
“…Overall, the results found minimap2 to be the best performing aligner for SV calling applications, which also had the fastest run time and the most mapped bases. Recent benchmarking studies on human data also recommended minimap2 among tested aligners such as GraphMap, LAST, and NGMLR (Bolognini and Magi, 2021; Coster et al, 2019; Zhou et al, 2019).…”
Structural variations (SVs) are larger polymorphisms (>50 bp in length), which consist of insertions, deletions, inversions, duplications, and translocations. They can have a strong impact on agronomical traits and play an important role in environmental adaptation. The development of long-read sequencing technologies, including Oxford Nanopore, allows for comprehensive SV discovery and characterization even in complex polyploid crop genomes. However, many of the SV discovery pipeline benchmarks do not include complex plant genome datasets. In this study, we benchmarked popular long-read alignment-based SV detection tools for crop plant genomes. We used real and simulated Oxford Nanopore reads for two crops, allotetraploid Brassica napus (oilseed rape) and diploid Solanum lycopersicum (tomato), and evaluated several read aligners and SV callers across 5x, 10x, and 20x coverages typically used in re-sequencing studies. Our benchmarks provide a useful guide for designing Oxford Nanopore re-sequencing projects and SV discovery pipelines for crop plants.
“…2b , CuteSV reproducibly showed higher recall and precision scores than Sniffles in the detection of DELs. This result is concordant with previous benchmark studies 41 , 43 , and we therefore decided to utilize the CuteSV algorithm. Utilizing this algorithm, we next compared the precision and recall scores of activated T cells and LCLs.…”
Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale.
“…7 b–i). Our results that a slight increment in performance after depth of coverage > 20× were also found in a prior study of SV calling methods using nanopore sequencing data [ 44 ]. Fig.…”
BackgroundStructural 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.ResultsBased 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.ConclusionsWe 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.
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