QS-Net:基于四元组和六元组重建系统发育网络
首发时间:2019-04-22
摘要:当生物实体或分类群之间涉及网状事件如水平基因转移,杂交,基因重组和基因重配时,系统发育网络被用于估计它们的进化关系。在过去的十年中,已经提出了许多系统发育树和网络重建方法。尽管它们在重建简单到中等复杂网状事件时非常准确,但是当同时存在几个网状事件时性能降低。在本文中提出一种新的方法QS-Net,一种利用六个分类单元之间关系的信息的系统发育网络重建方法。为了评估QS-Net的性能,我们对分别从进化树,涉及3个网状事件的进化网络和涉及5个网状事件的复杂进化网络模拟的3个人工序列数据集进行了实验。与流行的系统发育分析方法(包括Neighbor-Joining, Split-Decomposition, Neighbor-Net 和 Quartet-Net)的比较表明,QS-Net在重建树状进化历史方面与其他方法相当,而在重建网状事件方面优于其他方法。此外,我们还在真实数据集中运行了QS-Net,这些真实数据集包括由36种细菌物种组成的细菌分类数据和22种H7N9甲型流感病毒的全基因组序列。结果表明,QS-Net能够推断出普遍认为的细菌分类学和流感进化以及识别新的网状事件。
关键词: 计算机科学与技术 系统发育网络 网状进化 细菌分类学 流感重配
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QS-Net: Reconstructing Phylogenetic Networks based on Quartet and Sextet
Abstract:Phylogenetic networks are used to estimate evolutionary relationships among biological entities or taxa involving reticulate events such as horizontal gene transfer, hybridization, recombination and reassortment. In the past decade, many phylogenetic tree and network reconstruction methods have been proposed. Despite they are highly accurate in reconstructing simple to moderate complex reticulate events, the performance decreases when several reticulate events are present simultaneously. In this paper, we proposed QS-Net, a phylogenetic network reconstruction method taking advantage of information on the relationship among six taxa. To evaluate the performance of QS-Net, we conducted experiments on 3 artificial sequence data simulated respectively from an evolutionary tree, an evolutionary network involving 3 reticulate events, and a complex evolutionary network involving 5 reticulate events. Comparison with popular phylogenetic methods including Neighbor-Joining, Split-Decomposition, Neighbor-Net and Quartet-Net suggests that QS-Net is comparable with other methods in reconstructing tree-like evolutionary histories, while outperforms them in reconstructing reticulate events. In addition, we also applied QS-Net in real data including a bacterial taxonomy data consisting of 36 bacterial species and the whole genome sequences of 22 H7N9 influenza A viruses. The results indicate that QS-Net is capable of inferring commonly believed bacterial taxonomy and influenza evolution as well as identifying novel reticulate events.
Keywords: phylogenetic network reticulate evolution sextet bacterial taxonomy influenza reassortment
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