Paragraph: a graph-based structural variant genotyper for short-read sequence data
Paragraph: a graph-based structural variant genotyper for short-read sequence data
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DOI：
10.1186/s13059-019-1909-7
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发表时间：
2019-12-19
期刊：
影响因子：
12.3
通讯作者：
Eberle, Michael A.
中科院分区：
文献类型：
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作者：
Chen, Sai;Krusche, Peter;Eberle, Michael A.
从短读段数据准确检测和基因分型结构变异（SV）是基因组学研究和临床测序管道中的长期发展领域。我们介绍段落，一个准确的基因分型模型SV使用序列图和SV注释。我们使用长读SV调用作为真值集，从三个样本的全基因组序列数据上证明了段落的准确性，然后将段落大规模应用于100个不同祖先的短读测序样本的队列。我们的分析表明，段落有更好的准确性比其他现有的基因型，可以应用于人口规模的研究。
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.