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Abstract：
Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging.
In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches.
This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.
In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches.
This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.
摘要：
基因组结构变异（SV）是许多生物体中基因型和表型变化的重要决定因素。然而,从下一代测序数据中检测SV仍然具有挑战性.
在这项研究中,来自中国家族四重奏的DNA在三个不同的测序中心一式三份进行测序。利用不同的分析管道生成总共288个衍生数据集,并进行比较以识别分析变异性的来源。映射方法为变异性提供了主要贡献，其次是测序中心和重复。有趣的是,仅由一个中心或副本支持的SV通常代表真实阳性，其中47.02％和45.44％与长读数SV调用集重叠，分别。这与中心和复制中的SV调用的总体较高的假阴性率(15.72%)一致。最后,我们观察到SV调用变异性在基因分型方法中也持续存在，表明潜在的测序和制备方法的影响。
这项研究首次详细了解了下一代测序中SV识别变异性的来源，并强调了SV中仍存在的挑战，需要大型队列。我们进一步就如何减少SV调用可变性和对齐方法的选择提出建议。
在这项研究中,来自中国家族四重奏的DNA在三个不同的测序中心一式三份进行测序。利用不同的分析管道生成总共288个衍生数据集,并进行比较以识别分析变异性的来源。映射方法为变异性提供了主要贡献，其次是测序中心和重复。有趣的是,仅由一个中心或副本支持的SV通常代表真实阳性，其中47.02％和45.44％与长读数SV调用集重叠，分别。这与中心和复制中的SV调用的总体较高的假阴性率(15.72%)一致。最后,我们观察到SV调用变异性在基因分型方法中也持续存在，表明潜在的测序和制备方法的影响。
这项研究首次详细了解了下一代测序中SV识别变异性的来源，并强调了SV中仍存在的挑战，需要大型队列。我们进一步就如何减少SV调用可变性和对齐方法的选择提出建议。
