Detecting Network Communities: An Application to Phylogenetic Analysis

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.

Evolutionary analysis of apolipoprotein E by Maximum Likelihood and complex network methods

“…The construction of a complex network family was based on the method proposed by Andrade et al (2011). It allows a comparison between two networks obtained at close similarity thresholds, using the concept of δ-distance between complex networks (Andrade et al , 2008).…”

“…The similarity values were considered valid for those pairs of sequences with an E-value equal to or smaller than 1.0. Next, the community identification started with the construction of the one-parameter network family, as proposed by Andrade et al (2011).…”

“…It is worthy of note that the construction of the dendrogram in the current study relied only on community separation by changing the σ value. This methodological change could be made because the data set in the current work was much smaller than those data sets used in previous studies (Góes-Neto et al , 2010; Andrade et al , 2011), in which the dendrograms at fixed σ values were obtained by successive link elimination based on Newman-Girvan's algorithm. Nonetheless, we also obtained the same results reported in the next section by applying of the previously two-step procedure.…”

“…For example, some studies suggest that BI can be more prone to long-branch attraction biases than ML techniques, and that there could be some problems with Markov chain Monte Carlo (MCMC) implementations or prior distributions (Kolaczkowski and Thornton, 2009). In addition to the ML method, several complex network-based approaches have been proposed, which allow for the analysis of larger datasets with relatively low computational cost (Andrade et al , 2011). Such methods have been widely applied to biological evolutionary studies (Junker and Schreiber, 2008; Boccaletti, 2009).…”

“…Summarizing, this study aimed at performing a phylogenetic analysis based on a set of 32 apo E amino acid sequences using both the ML method and a reliable complex network based approach (Andrade et al , 2011). The latter does not require further biological assumptions for its application, except for the ones used by BLAST to calculate similarity scores.…”