Esteban Gabory, Moses Njagi Mwaniki, Nadia Pisanti, Solon P Pissis, Jakub Radoszewski, Michelle Sweering, Wiktor Zuba
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We then show that both measures can be computed efficiently, in both theory and practice, by employing the <i>intersection graph</i> of two ED strings.</p><p><strong>Results: </strong>We also implemented our methods as a software tool for pangenome comparison and evaluated their efficiency and effectiveness using both synthetic and real datasets.</p><p><strong>Discussion: </strong>As for efficiency, we compare the runtime of the intersection graph method against the classic product automaton construction showing that the intersection graph is faster by up to one order of magnitude. 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引用次数: 0
摘要
引言弹性退化(ED)字符串是一组字符串的序列。它也可以看作是一个有向无环图,其边缘用字符串标记。ED 字符串的概念是作为变异图和序列图的一种简单替代方法而提出的,用于表示庞基因组,即需要联合分析或用作参考的基因组序列集合:在这项研究中,我们定义了两个 ED 字符串的匹配统计量概念,将其作为庞基因组之间的相似性度量,并由此推断出相应的距离度量。然后,我们证明了通过使用两个 ED 字符串的交集图,可以在理论和实践中高效计算这两个度量:结果:我们还将我们的方法作为一种软件工具进行了庞基因组比较,并使用合成数据集和真实数据集评估了这些方法的效率和有效性:在效率方面,我们将交集图方法的运行时间与经典的乘积自动机构造进行了比较,结果显示交集图的速度快达一个数量级。在有效性方面,我们使用真实的 SARS-CoV-2 数据集和我们的匹配统计相似性度量重现了 SARS-CoV-2 的一个成熟的支系分类,从而证明我们的方法所获得的分类与现有的分类是一致的。
Introduction: An elastic-degenerate (ED) string is a sequence of sets of strings. It can also be seen as a directed acyclic graph whose edges are labeled by strings. The notion of ED strings was introduced as a simple alternative to variation and sequence graphs for representing a pangenome, that is, a collection of genomic sequences to be analyzed jointly or to be used as a reference.
Methods: In this study, we define notions of matching statistics of two ED strings as similarity measures between pangenomes and, consequently infer a corresponding distance measure. We then show that both measures can be computed efficiently, in both theory and practice, by employing the intersection graph of two ED strings.
Results: We also implemented our methods as a software tool for pangenome comparison and evaluated their efficiency and effectiveness using both synthetic and real datasets.
Discussion: As for efficiency, we compare the runtime of the intersection graph method against the classic product automaton construction showing that the intersection graph is faster by up to one order of magnitude. For showing effectiveness, we used real SARS-CoV-2 datasets and our matching statistics similarity measure to reproduce a well-established clade classification of SARS-CoV-2, thus demonstrating that the classification obtained by our method is in accordance with the existing one.