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邻接网络:改进算法与实现

NeighborNet: improved algorithms and implementation.

作者信息

Bryant David, Huson Daniel H

机构信息

Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand.

Algorithms in Bioinformatics, University of Tübingen, Tübingen, Germany.

出版信息

Front Bioinform. 2023 Sep 20;3:1178600. doi: 10.3389/fbinf.2023.1178600. eCollection 2023.

Abstract

NeighborNet constructs phylogenetic networks to visualize distance data. It is a popular method used in a wide range of applications. While several studies have investigated its mathematical features, here we focus on computational aspects. The algorithm operates in three steps. We present a new simplified formulation of the first step, which aims at computing a circular ordering. We provide the first technical description of the second step, the estimation of split weights. We review the third step by constructing and drawing the network. Finally, we discuss how the networks might best be interpreted, review related approaches, and present some open questions.

摘要

邻接网络构建系统发育网络以可视化距离数据。它是一种广泛应用于众多领域的流行方法。虽然已有多项研究探讨了其数学特征,但在此我们关注的是计算方面。该算法分三步运行。我们给出了第一步的一种新的简化表述,其目的是计算一个循环排序。我们对第二步,即分裂权重的估计,进行了首次技术描述。我们通过构建和绘制网络来回顾第三步。最后,我们讨论了如何最好地解读这些网络,回顾了相关方法,并提出了一些开放性问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a78/10548196/86c364c0c05b/fbinf-03-1178600-g001.jpg

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