3 research outputs found
Detecting Network Communities: An Application to Phylogenetic Analysis
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
Comparison of complex networks and tree-based methods of phylogenetic analysis and proposal of a bootstrap method
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