PDF(Pubmed)
Abstract：
The identification of structural variants (SVs) in genomic data represents an ongoing challenge because of difficulties in reliable SV calling leading to reduced sensitivity and specificity. We prepared high-quality DNA from 9 parent-child trios, who had previously undergone short-read whole-genome sequencing (Illumina platform) as part of the Genomics England 100,000 Genomes Project. We reanalysed the genomes using both Bionano optical genome mapping (OGM; 8 probands and one trio) and Nanopore long-read sequencing (Oxford Nanopore Technologies [ONT] platform; all samples). To establish a \"truth\" dataset, we asked whether rare proband SV calls (n = 234) made by the Bionano Access (version 1.6.1)/Solve software (version 3.6.1_11162020) could be verified by individual visualisation using the Integrative Genomics Viewer with either or both of the Illumina and ONT raw sequence. Of these, 222 calls were verified, indicating that Bionano OGM calls have high precision (positive predictive value 95%). We then asked what proportion of the 222 true Bionano SVs had been identified by SV callers in the other two datasets. In the Illumina dataset, sensitivity varied according to variant type, being high for deletions (115/134; 86%) but poor for insertions (13/58; 22%). In the ONT dataset, sensitivity was generally poor using the original Sniffles variant caller (48% overall) but improved substantially with use of Sniffles2 (36/40; 90% and 17/23; 74% for deletions and insertions, respectively). In summary, we show that the precision of OGM is very high. In addition, when applying the Sniffles2 caller, the sensitivity of SV calling using ONT long-read sequence data outperforms Illumina sequencing for most SV types.
摘要：
基因组数据中结构变体(SV)的鉴定代表了持续的挑战,因为可靠的SV调用中的困难导致灵敏度和特异性降低。我们从9个亲子三重奏中制备了高质量的DNA，作为基因组英格兰100,000基因组项目的一部分，他以前接受了短阅读全基因组测序（Illumina平台）。我们使用Bionano光学基因组作图（OGM;8个先证者和一个三人组）和Nanopore长读测序（OxfordNanoporeTechnologies[ONT]平台；所有样品）重新分析了基因组。要建立“真相”数据集，我们询问了由BionanoAccess(1.6.1版)/Solve软件(3.6.1_11162020版)进行的罕见先证者SV调用(n=234)是否可以使用具有Illumina和ONT原始序列之一或两者的IntegrativeGenomicsViewer通过个体可视化进行验证。其中,222个电话被确认,表明BionanoOGM调用具有很高的精度(阳性预测值95%)。然后，我们询问了在其他两个数据集中，SV呼叫者识别出222个真正的BionanoSV的比例。在Illumina数据集中，灵敏度根据变体类型而变化，缺失高（115/134;86％），但插入差（13/58;22％）。在ONT数据集中，使用原始Sniffles变体调用器的灵敏度通常较差（总体为48％），但使用Sniffles2后有了很大提高（36/40;90％和17/23;74％的缺失和插入，分别)。总之,我们表明OGM的精度非常高。此外,应用Sniffles2调用者时，对于大多数SV类型,使用ONT长读序列数据进行SV调用的灵敏度优于Illumina测序.
