PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships

By Cuong V. Than, Derek A. Ruths, Luay Nakhleh. BMC Bioinformatics, 2008

492 citations recorded by the source corpus.

BackgroundPhylogenies, i.e., the evolutionary histories of groups of taxa, play a major role in representing the interrelationships among biological entities. Many software tools for reconstructing and evaluating such phylogenies have been proposed, almost all of which assume the underlying evolutionary history to be a tree. While trees give a satisfactory first-order approximation for many families of organisms, other families exhibit evolutionary mechanisms that cannot be represented by trees. Processes such as horizontal gene transfer (HGT), hybrid speciation, and interspecific recombination, collectively referred to as reticulate evolutionary events, result in networks, rather than trees, of relationships. Various software tools have been recently developed to analyze reticulate evolutionary relationships, which include SplitsTree4, LatTrans, EEEP, HorizStory, and T-REX.ResultsIn this paper, we report on the PhyloNet software package, which is a suite of tools for analyzing reticulate evolutionary relationships, or evolutionary networks, which are rooted, directed, acyclic graphs, leaf-labeled by a set of taxa. These tools can be classified into four categories: (1) evolutionary network representation: reading/writing evolutionary networks in a newly devised compact form; (2) evolutionary network characterization: analyzing evolutionary networks in terms of three basic building blocks – trees, clusters, and tripartitions; (3) evolutionary network comparison: comparing two evolutionary networks in terms of topological dissimilarities, as well as fitness to sequence evolution under a maximum parsimony criterion; and (4) evolutionary network reconstruction: reconstructing an evolutionary network from a species tree and a set of gene trees.ConclusionThe software package, PhyloNet, offers an array of utilities to allow for efficient and accurate analysis of evolutionary networks. The software package will help significantly in analyzing large data sets, as well as in studying the performance of evolutionary network reconstruction methods. Further, the software package supports the proposed eNewick format for compact representation of evolutionary networks, a feature that allows for efficient interoperability of evolutionary network software tools. Currently, all utilities in PhyloNet are invoked on the command line.

Subfields: Genetics, Molecular Biology, Paleontology

View Paper: https://www.semanticscholar.org/paper/a7643f1174cb34aa0f06db7b17f28bd28b9c1088

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Citations in the corpus

A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data (2016, background): “…of Basidiobolus will not be achieved by the addition of sequence data alone but will instead require additional taxon sampling, consideration of episodic events associated with rare genomic changes, and possibly the use of models of evolution that are not strictly bifurcating (Than et al. 2008).”
Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, background): “Various extensions to Newick have been proposed to incorporate more general graph structures (Morin and Moret, 2006; Buendia and Narasimhan, 2006; Cardona et al., 2008; Than et al., 2008), as well as a GraphML extension to encode ARGs directly (McGill et al., 2013).”
Inferring Phylogenetic Networks Using PhyloNet (2017, methodology): “Other Features In addition to the aforementioned inference methods and all the functionalities that existed in the old version of PhyloNet (Than et al., 2008), the software package includes new features that help with other types of analyses.”
PhyloNetworks: A Package for Phylogenetic Networks (2017, methodology): “Other software packages can estimate phylogenetic networks (notably PhyloNet [Than et al. 2008], BEAST2 [Zhang et al. 2017], and TreeMix [Pickrell and Pritchard 2012]) or plot rooted networks (Dendroscope, Huson and Scornavacca, 2012).”
Inferring Phylogenetic Networks with Maximum Pseudolikelihood under Incomplete Lineage Sorting (2015, unknown): “Implicit networks –also called split networks– describe the discrepancy in gene trees, or other sources of data, and methods are well developed to reconstruct these networks [1, 2, 3, 4].”
T-REX: a web server for inferring, validating and visualizing phylogenetic trees and networks (2012, methodology): “This algorithm was shown to be faster and generally more effective than the LatTrans (24) and RIATA-HGT (27) techniques.”
Multi‐gene phylogenetic analysis of south‐east Asian pest members of the Bactrocera dorsalis species complex (Diptera: Tephritidae) does not support current taxonomy (2014, methodology): “…gene trees in this study were unresolved and therefore prevented the use of the speciestree software (e.g.,Ane et al. 2007; Liu 2008; Liu et al. 2009; Kubatko 2009; Kubatko et al. 2009; Heled and Drummond 2010; Than and Nakhleh 2009; Than et al. 2008; Huang et al. 2010; Knowles and Kubatko 2010).”
Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. (2012, methodology): “PhyloNet: a software package for analyzing and reconstructing reticulate evolutionary relationships.”
Ancient hybridizations among the ancestral genomes of bread wheat (2014, methodology): “Estimations of parental genome contributions and species networks from gene tree topologies were calculated using parsimonious inference of hybridization in the presence of ILS (33) implemented in PhyloNet (34) and with a coalescent-based method (described in the supplementary materials).”