Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations. We demonstrate the accuracy of Paragraph on whole-genome sequence data from three samples using long-read SV calls as the truth set, and then apply Paragraph at scale to a cohort of 100 short-read sequenced samples of diverse ancestry. Our analysis shows that Paragraph has better accuracy than other existing genotypers and can be applied to population-scale studies.

中文翻译：

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段落：用于短读序列数据的基于图的结构变异基因型

从短读长数据中准确检测结构变异 (SV) 并进行基因分型是基因组学研究和临床测序流程中长期发展的领域。我们引入了 Paragraph，一种精确的基因分型器，它使用序列图和 SV 注释对 SV 进行建模。我们使用长读长 SV 调用作为真值集，证明了 Paragraph 对来自三个样本的全基因组序列数据的准确性，然后将 Paragraph 大规模应用于一组 100 个不同血统的短读长测序样本。我们的分析表明，Paragraph 比其他现有基因分型器具有更好的准确性，可以应用于人群规模的研究。