Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data

Abstract

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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“…Determining the specific change that has occurred has great value, as variants are later classified to determine their clinical significance. Similar to variant calling, SV genotyping is much more complex than the genotyping of SNVs or Indels, as is highlighted in a comprehensive evaluation by Duan X. et al [ 33 ].…”

“…One of the possible reasons for this is that complex genetic alterations such as translocations and inversions can often be accompanied by additional changes such as deletions or duplications at the site of separation or joining of genetic material. Finally, the conclusion regarding depth of coverage was that analysis at a depth of coverage of 20× produces diminishing returns in the F1 scores [ 33 ].…”

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“…However, since the data did not show a high correlation between characteristics, it was decided to maintain all the original features. After each preprocessing step, cross-validation is performed to assess how it affects model performance [32]. A combination of performance metrics, such as accuracy and F1-score, are used to determine the preprocessing quality in the context of student retention [33].…”

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“…SVs denote significant genomic alterations that typically span at least 50 base pairs ( Duan et al, 2022 ). These genomic variants include inversions, balanced translocations, and genomic imbalances, which involve duplications, insertions, and deletions collectively referred to as DNA gains, losses, or rearrangements ( Sudmant et al, 2015 ; Sedlazeck et al, 2018a ).…”