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首页> 外文期刊>Egyptian Journal of Basic and Applied Sciences >A graph theoretic model for prediction of reticulation events and phylogenetic networks for DNA sequences
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A graph theoretic model for prediction of reticulation events and phylogenetic networks for DNA sequences

机译:用于预测网状事件和DNA序列的系统进化网络的图论模型

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Phylogenies are the commonly used tools for the prediction of ancestry of present day organisms from the past decades. Several methods have been developed to construct phylogenetic trees that predict the history of species by direct linkage of edges. Very few studies have been developed for the phylogenetic networks (which is the generalization of trees). Presently, the methods used to determine phylogenetic networks are based on distance measures or character measures of the sequences of species. It is a very challenging task for computational biologists to find the exact method that can predict the accurate networks of organisms. In this study, a phylogenetic network construction model based on basic graph theory concepts is reported. This model finds the distance matrix of every sequence considered in the study. The two features (positioning and stack interactions) of every DNA sequence and their combined effect have been taken into account to calculate the distances. Results suggested that reticulate events can be observed by using the distances obtained by the proposed method and no such event is predicted by using the distances calculated by the previous method. The important results obtained in the form of distances are 1.637300, 2.000000, 0.932700, 2.331300, 2.829200 and the significance of these values is to represent the different reticulation events among the sequences for different features. Hence distances calculated by this model gives better insights to study the phylogenetic networks.
机译:系统发育是过去几十年来预测当今生物祖先的常用工具。已经开发了几种方法来构建通过直接边缘连接来预测物种历史的系统树。对于系统发育网络(这是树木的概括),很少有研究。当前,用于确定系统发育网络的方法是基于物种序列的距离度量或特征度量。对于计算生物学家来说,找到可以预测生物准确网络的精确方法是一项艰巨的任务。在这项研究中,报告了基于基本图论概念的系统进化网络构建模型。该模型找到研究中考虑的每个序列的距离矩阵。在计算距离时,已考虑了每个DNA序列的两个特征(位置和堆叠相互作用)及其组合作用。结果表明,通过使用建议的方法获得的距离可以观察到网状事件,而使用以前的方法计算出的距离则无法预测到此类事件。以距离形式获得的重要结果是1.637300、2000000、0.932700、2.331300、2.829200,这些值的意义在于代表序列中不同特征的不同网状事件。因此,该模型计算出的距离为研究系统进化网络提供了更好的见识。

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