Detecting Network Communities: An Application to Phylogenetic Analysis

Roberto F S Andrade,Ivan C Rocha-Neto,Charbel N El-Hani,Aristóteles Goés-Neto,Charles N De Santana,Leonardo B L Santos,Marcelo V C Diniz,Suani T R Pinho,Thierry Petit Lobão

doi:10.1371/journal.pcbi.1001131

Abstract

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis.

Highlights

In networks, module or community structure plays a central role when it comes to understand network topology and dynamics
This work reports a method based on complex network theory that can recover information about the evolutionary relationships between organisms, as expressed in the similarities and differences between their protein sequences, which is useful for phylogenetic inference
Each node in the network is a specific protein sequence and the placement of edges depends on a threshold value s, related to the protein similarity required to such a placement

Summary

Introduction

Module or community structure plays a central role when it comes to understand network topology and dynamics. To advance solutions to many problems related to biological networks, we need to identify, the community structure in datasets. Recent studies using complex network approaches in the fields of both genomics and proteomics have contributed to a better knowledge of the structure and dynamics of the complex webs of interactions of a living cell [5,6,7,8,9,10,11,12]. Several kinds of biologically relevant networks have been studied in the last years, mainly protein interaction, transcriptional, and metabolic networks [1]. We work with another set of relationships, namely, the evolutionary relationships between proteins throughout phylogeny, and introduce a new method to identify communities in generally weighted complex networks

Author Summary

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