Góes-Neto Aristóteles, Diniz Marcelo V C, Carvalho Daniel S, Bomfim Gilberto C, Duarte Angelo A, Brzozowski Jerzy A, Petit Lobão Thierry C, Pinho Suani T R, El-Hani Charbel N, Andrade Roberto F S
Department of Microbiology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Institute of Biology, Universidade Federal da Bahia, Salvador, Bahia, Brazil.
PeerJ. 2018 Feb 9;6:e4349. doi: 10.7717/peerj.4349. eCollection 2018.
Complex networks have been successfully applied to the characterization and modeling of complex systems in several distinct areas of Biological Sciences. Nevertheless, their utilization in phylogenetic analysis still needs to be widely tested, using different molecular data sets and taxonomic groups, and, also, by comparing complex networks approach to current methods in phylogenetic analysis. In this work, we compare all the four main methods of phylogenetic analysis (distance, maximum parsimony, maximum likelihood, and Bayesian) with a complex networks method that has been used to provide a phylogenetic classification based on a large number of protein sequences as those related to the chitin metabolic pathway and ATP-synthase subunits. In order to perform a close comparison to these methods, we selected Basidiomycota fungi as the taxonomic group and used a high-quality, manually curated and characterized database of chitin synthase sequences. This enzymatic protein plays a key role in the synthesis of one of the exclusive features of the fungal cell wall: the presence of chitin. The communities (modules) detected by the complex network method corresponded exactly to the groups retrieved by the phylogenetic inference methods. Additionally, we propose a bootstrap method for the complex network approach. The statistical results we have obtained with this method were also close to those obtained using traditional bootstrap methods.
复杂网络已成功应用于生物科学多个不同领域中复杂系统的表征和建模。然而，它们在系统发育分析中的应用仍需使用不同的分子数据集和分类群进行广泛测试，并且还需要通过将复杂网络方法与系统发育分析中的当前方法进行比较来验证。在这项工作中，我们将系统发育分析的所有四种主要方法（距离法、最大简约法、最大似然法和贝叶斯法）与一种复杂网络方法进行了比较，该复杂网络方法已被用于基于大量与几丁质代谢途径和ATP合酶亚基相关的蛋白质序列进行系统发育分类。为了与这些方法进行密切比较，我们选择担子菌纲真菌作为分类群，并使用了一个高质量的、经过人工整理和表征的几丁质合酶序列数据库。这种酶蛋白在真菌细胞壁独特特征之一几丁质的合成中起关键作用。通过复杂网络方法检测到的群落（模块）与系统发育推断方法检索到的组完全对应。此外，我们为复杂网络方法提出了一种自展法。我们用这种方法获得的数据结果也与使用传统自展法获得的结果相近。
