比较系统发育网络生成器的拓扑结构。

IF 0.9 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Remie Janssen, Pengyu Liu
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引用次数: 11

摘要

系统发育网络反映了物种的进化史,可以记录基因水平转移和基因重组等自然的网状进化过程。这使得系统发育网络比系统发育树更全面地代表了进化史。用于生成随机树或随机网络的随机过程是进化分析中的重要工具,特别是在系统发育重建中,它们可以用于验证或作为贝叶斯方法的先验。然而,随着越来越多的网络生成器的开发,缺乏对不同生成器的讨论和比较。为了弥补这一差距,我们比较了一组系统发育网络生成器,通过分析生成的网络在网络数量上的拓扑汇总统计数据并比较拓扑概况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing the topology of phylogenetic network generators.

Phylogenetic networks represent evolutionary history of species and can record natural reticulate evolutionary processes such as horizontal gene transfer and gene recombination. This makes phylogenetic networks a more comprehensive representation of evolutionary history compared to phylogenetic trees. Stochastic processes for generating random trees or networks are important tools in evolutionary analysis, especially in phylogeny reconstruction where they can be utilized for validation or serve as priors for Bayesian methods. However, as more network generators are developed, there is a lack of discussion or comparison for different generators. To bridge this gap, we compare a set of phylogenetic network generators by profiling topological summary statistics of the generated networks over the number of reticulations and comparing the topological profiles.

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来源期刊
Journal of Bioinformatics and Computational Biology
Journal of Bioinformatics and Computational Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.10
自引率
0.00%
发文量
57
期刊介绍: The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information. The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.
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