Abstract
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.
Highlights
The complex networks approach has been successfully applied to uncover organizational principles underlying the origin, evolution, and functioning of several and distinct complex systems in all areas of science, in the Biological Sciences (Barábasi & Oltvai, 2004).Among the many developed approaches, we call the attention to the phylogenetic analysis based on complex networks using protein or gene similarity as the only source of information
We conduct further investigations on the reliability of the complex network approach addressing two important issues: (i) we present a direct comparison of its results to those provided by the four phylogenetic methods indicated before; (ii) we propose a bootstrap method that provides a quantitative measure of support values to the branching processes
We developed a software to evaluate all necessary steps of the community detection procedure according to the complex network method explained in this paper, which is available for download at http://projectsn.github.io/scannet/
Summary
The complex networks approach has been successfully applied to uncover organizational principles underlying the origin, evolution, and functioning of several and distinct complex systems in all areas of science, in the Biological Sciences (Barábasi & Oltvai, 2004).Among the many developed approaches, we call the attention to the phylogenetic analysis based on complex networks using protein or gene similarity as the only source of information. Data of four enzymes present in the chitin metabolic pathway for 1695 organisms (with complete genomes) were collected and used to test and validate the method This framework was successfully applied to investigate the evolutionary origins of mitochondria using three ATP synthase subunits and its alphaproteobacterial homologs (Carvalho et al, 2015), and to present an evolutionary study of apolipoprotein-E carrying organisms (Benevides et al, 2016). Despite these advances, with large databases, we understand this approach should be widely tested, using distinct databases and taxonomic groups, and comparing its results with those provided the current methods quoted above. In order to confer confidence to such a comparison, it is reasonable to use a high-quality database of sequences related to a small number of organisms as well as to propose a bootstrap procedure for the complex network method
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