Who is Who in Phylogenetic Networks

Publications related to 'Program PhyloNet' : PhyloNet is aimed mainly at providing a suite of tools for reconstructing and analyzing reticulate (non-treelike) evolutionary relationships. In particular, the software package includes features for inferring horizontal gene transfer from a pair of species/gene trees, and detecting interspecific recombination breakpoints in a sequence alignment. Further, it has the capabilities for reading/storing phylogenetic networks, and comparing phylogenetic networks topologies. http://bioinfo.cs.rice.edu/phylonet/index.html   Order by:   Type | Year

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Associated keywords

abstract-network agreement-forest bayesian bootstrap branch-length coalescent counting evaluation explicit-network from-multistate-characters from-network from-rooted-trees from-sequences from-species-tree heuristic hybridization incomplete-lineage-sorting lateral-gene-transfer likelihood lineage-sorting parsimony phylogenetic-network phylogeny Program-Bio-PhyloNetwork Program-Dendroscope Program-EEEP Program-Hybrid-coal Program-Hybrid-Lambda Program-LatTrans Program-Nepal Program-NetGen Program-PhyloNet Program-PhyloNetworks-SNaQ Program-PIRN Program-Prunier Program-SplitsTree Program-TCS reconstruction simulation software SPR-distance statistical-model survey tripartition-distance unicyclic-network uniqueness visualization

Article (Journal)

1 Cuong Than, Derek Ruths and Luay Nakhleh. PhyloNet: A Software Package for Analyzing and Reconstructing Reticulate Evolutionary Relationships. In BMCB, Vol. 9(322), 2008. Keywords: Program PhyloNet, software. Note: http://dx.doi.org/10.1186/1471-2105-9-322.         Toggle abstract "Background: Phylogenies, 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. Results: In 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. Conclusion: The 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. © 2008 Than et al; licensee BioMed Central Ltd." 2 Gabriel Cardona, Francesc Rosselló and Gabriel Valiente. Extended Newick: It is Time for a Standard Representation. In BMCB, Vol. 9:532, 2008. Keywords: evaluation, explicit network, phylogenetic network, Program Bio PhyloNetwork, Program Dendroscope, Program NetGen, Program PhyloNet, Program SplitsTree, Program TCS, visualization. Note: http://bioinfo.uib.es/media/uploaded/bmc-2008-enewick-sub.pdf.         3 Cuong Than, Derek Ruths, Hideki Innan and Luay Nakhleh. Confounding Factors in HGT Detection: Statistical Error, Coalescent Effects, and Multiple Solutions. In JCB, Vol. 14(4):517-535, 2007. Keywords: counting, explicit network, from rooted trees, from species tree, lateral gene transfer, phylogenetic network, phylogeny, Program LatTrans, Program PhyloNet. Note: http://www.cs.rice.edu/~nakhleh/Papers/recombcg06-jcb.pdf.         4 Sophie Abby, Eric Tannier, Manolo Gouy and Vincent Daubin. Detecting lateral gene transfers by statistical reconciliation of phylogenetic forests. In BMCB, Vol. 11:324, 2010. Keywords: agreement forest, explicit network, from rooted trees, from species tree, heuristic, lateral gene transfer, phylogenetic network, phylogeny, Program EEEP, Program PhyloNet, Program Prunier, reconstruction, software. Note: http://www.biomedcentral.com/1471-2105/11/324.         Toggle abstract "Background: To understand the evolutionary role of Lateral Gene Transfer (LGT), accurate methods are needed to identify transferred genes and infer their timing of acquisition. Phylogenetic methods are particularly promising for this purpose, but the reconciliation of a gene tree with a reference (species) tree is computationally hard. In addition, the application of these methods to real data raises the problem of sorting out real and artifactual phylogenetic conflict.Results: We present Prunier, a new method for phylogenetic detection of LGT based on the search for a maximum statistical agreement forest (MSAF) between a gene tree and a reference tree. The program is flexible as it can use any definition of "agreement" among trees. We evaluate the performance of Prunier and two other programs (EEEP and RIATA-HGT) for their ability to detect transferred genes in realistic simulations where gene trees are reconstructed from sequences. Prunier proposes a single scenario that compares to the other methods in terms of sensitivity, but shows higher specificity. We show that LGT scenarios carry a strong signal about the position of the root of the species tree and could be used to identify the direction of evolutionary time on the species tree. We use Prunier on a biological dataset of 23 universal proteins and discuss their suitability for inferring the tree of life.Conclusions: The ability of Prunier to take into account branch support in the process of reconciliation allows a gain in complexity, in comparison to EEEP, and in accuracy in comparison to RIATA-HGT. Prunier's greedy algorithm proposes a single scenario of LGT for a gene family, but its quality always compares to the best solutions provided by the other algorithms. When the root position is uncertain in the species tree, Prunier is able to infer a scenario per root at a limited additional computational cost and can easily run on large datasets.Prunier is implemented in C++, using the Bio++ library and the phylogeny program Treefinder. It is available at: http://pbil.univ-lyon1.fr/software/prunier. © 2010 Abby et al; licensee BioMed Central Ltd." 5 Yun Yu, R. Matthew Barnett and Luay Nakhleh. Parsimonious Inference of Hybridization in the Presence of Incomplete Lineage Sorting. In Systematic Biology, Vol. 62(5):738-751, 2013. Keywords: from network, from rooted trees, hybridization, lineage sorting, parsimony, phylogenetic network, phylogeny, Program PhyloNet, reconstruction.         Toggle abstract "Hybridization plays an important evolutionary role in several groups of organisms. A phylogenetic approach to detect hybridization entails sequencing multiple loci across the genomes of a group of species of interest, reconstructing their gene trees, and taking their differences as indicators of hybridization. However, methods that follow this approach mostly ignore population effects, such as incomplete lineage sorting (ILS). Given that hybridization occurs between closely related organisms, ILS may very well be at play and, hence, must be accounted for in the analysis framework. To address this issue, we present a parsimony criterion for reconciling gene trees within the branches of a phylogenetic network, and a local search heuristic for inferring phylogenetic networks from collections of gene-tree topologies under this criterion. This framework enables phylogenetic analyses while accounting for both hybridization and ILS. Further, we propose two techniques for incorporating information about uncertainty in gene-tree estimates. Our simulation studies demonstrate the good performance of our framework in terms of identifying the location of hybridization events, as well as estimating the proportions of genes that underwent hybridization. Also, our framework shows good performance in terms of efficiency on handling large data sets in our experiments. Further, in analysing a yeast data set, we demonstrate issues that arise when analysing real data sets. Although a probabilistic approach was recently introduced for this problem, and although parsimonious reconciliations have accuracy issues under certain settings, our parsimony framework provides a much more computationally efficient technique for this type of analysis. Our framework now allows for genome-wide scans for hybridization, while also accounting for ILS. [Phylogenetic networks; hybridization; incomplete lineage sorting; coalescent; multi-labeled trees.] © 2013 The Author(s). All rights reserved." 6 Dingqiao Wen, Yun Yu and Luay Nakhleh. Bayesian Inference of Reticulate Phylogenies under the Multispecies Network Coalescent. In PLoS Genetics, Vol. 12(5):e1006006, 2016. Keywords: bayesian, coalescent, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software.         7 Hussein A. Hejase and Kevin J. Liu. A scalability study of phylogenetic network inference methods using empirical datasets and simulations involving a single reticulation. Vol. 17(422):1-12, 2016. Keywords: abstract network, evaluation, from sequences, phylogenetic network, phylogeny, Program PhyloNet, Program PhyloNetworks SNaQ, reconstruction, simulation, unicyclic network. Note: http://dx.doi.org/10.1186/s12859-016-1277-1.         8 Sha Zhu and James H. Degnan. Displayed Trees Do Not Determine Distinguishability Under the Network Multispecies Coalescent. In SB, Vol. 66(2):283-298, 2017. Keywords: branch length, coalescent, explicit network, from network, likelihood, phylogenetic network, phylogeny, Program Hybrid-coal, Program Hybrid-Lambda, Program PhyloNet, software, uniqueness. Note: presentation available at https://www.youtube.com/watch?v=JLYGTfEZG7g.         9 Dingqiao Wen, Yun Yu, Jiafan Zhu and Luay Nakhleh. Inferring Phylogenetic Networks Using PhyloNet. In SB, Vol. 67(4):735-740, 2018. Keywords: bayesian, likelihood, parsimony, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software.         10 Jiafan Zhu, Dingqiao Wen, Yun Yu, Heidi M. Meudt and Luay Nakhleh. Bayesian inference of phylogenetic networks from bi-allelic genetic markers. In PLoS Computational Biology, Vol. 14(1):e1005932.1-32, 2018. Keywords: bayesian, explicit network, from multistate characters, phylogenetic network, phylogeny, Program PhyloNet. Note: https://doi.org/10.1371/journal.pcbi.1005932.         11 Dingqiao Wen and Luay Nakhleh. Coestimating Reticulate Phylogenies and Gene Trees from Multilocus Sequence Data. In SB, Vol. 67(3):439-457, 2018. Keywords: bayesian, explicit network, from sequences, incomplete lineage sorting, phylogenetic network, phylogeny, Program PhyloNet, reconstruction. Note: https://doi.org/10.1093/sysbio/syx085.

InProceedings

12 Luay Nakhleh, Derek Ruths and Li-San Wang. RIATA-HGT: A Fast and Accurate Heuristic for Reconstructing Horizontal Gene Transfer. In COCOON05, Vol. 3595:84-93 of LNCS, springer, 2005. Keywords: from rooted trees, heuristic, lateral gene transfer, phylogenetic network, phylogeny, Program PhyloNet. Note: http://www.cs.rice.edu/~nakhleh/Papers/COCOON05.pdf.         13 Cuong Than, Derek Ruths, Hideki Innan and Luay Nakhleh. Identifiability Issues in Phylogeny-Based Detection of Horizontal Gene Transfer. In Proceedings of the Fourth RECOMB Comparative Genomics Satellite Workshop (RECOMB-CG'06), Vol. 4205:215-229 of LNCS, springer, 2006. Keywords: explicit network, from rooted trees, from species tree, lateral gene transfer, phylogenetic network, phylogeny, Program LatTrans, Program PhyloNet. Note: http://www.cs.rice.edu/~nakhleh/Papers/recombcg06-final.pdf.         14 Cuong Than and Luay Nakhleh. SPR-based Tree Reconciliation: Non-binary Trees and Multiple Solutions. In APBC08, Pages 251-260, 2008. Keywords: evaluation, from rooted trees, lateral gene transfer, phylogenetic network, phylogeny, Program LatTrans, Program PhyloNet, reconstruction, SPR distance. Note: http://www.cs.rice.edu/~nakhleh/Papers/apbc08.pdf.         15 Cuong Than, Guohua Jin and Luay Nakhleh. Integrating Sequence and Topology for Efficient and Accurate Detection of Horizontal Gene Transfer. In Proceedings of the Sixth RECOMB Comparative Genomics Satellite Workshop (RECOMB-CG'08), Vol. 5267:113-127 of LNCS, springer, 2008. Keywords: bootstrap, explicit network, from rooted trees, from sequences, lateral gene transfer, phylogenetic network, phylogeny, Program Nepal, Program PhyloNet, reconstruction. Note: http://www.cs.rice.edu/~nakhleh/Papers/recombcg-08.pdf, slides available at http://igm.univ-mlv.fr/RCG08/RCG08slides/Cuong_Than_RCG08.pdf.         16 Yun Yu, Nikola Ristic and Luay Nakhleh. Fast algorithms and heuristics for phylogenomics under ILS and hybridization. In RECOMB-CG'13, Vol. 14(Suppl 15):S6 of BMCB, 2013. Keywords: from network, from rooted trees, heuristic, phylogenetic network, phylogeny, Program PhyloNet, reconstruction.         17 Yun Yu and Luay Nakhleh. A maximum pseudo-likelihood approach for phylogenetic networks. In RECOMB-CG15, Vol. 16(Suppl 10)(S10):1-10 of BMC Genomics, BioMed Central, 2015. Keywords: explicit network, from rooted trees, hybridization, incomplete lineage sorting, likelihood, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, tripartition distance. Note: http://dx.doi.org/10.1186/1471-2164-16-S10-S10.

InBook

18 R. A. Leo Elworth, Huw A. Ogilvie, Jiafan Zhu and Luay Nakhleh. Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization. In Tandy Warnow editor, Bioinformatics and Phylogenetics. Seminal Contributions of Bernard Moret, Vol. 29 of Computational Biology, Springer, 2019. Keywords: explicit network, phylogenetic network, phylogeny, Program Dendroscope, Program PhyloNet, Program PhyloNetworks SNaQ, Program PIRN, Program SplitsTree, reconstruction, survey. Note: https://bioinfocs.rice.edu/sites/g/files/bxs266/f/ElworthZhuOgilvieNakhleh.pdf

MastersThesis

19 Dingqiao Wen. Bayesian Inference of Phylogenetic Networks. Master's thesis, Rice University, Texas, 2016. Keywords: bayesian, explicit network, phylogenetic network, phylogeny, Program PhyloNet, reconstruction. Note: https://scholarship.rice.edu/bitstream/handle/1911/96515/WEN-DOCUMENT-2016.pdf?sequence=1&isAllowed=y.

PhdThesis

20 Yun Yu. Models and Methods for Evolutionary Histories Involving Hybridization and Incomplete Lineage Sorting. PhD thesis, Rice University, U.S.A., 2014. Keywords: hybridization, incomplete lineage sorting, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software. Note: http://hdl.handle.net/1911/77583.

Misc

21 Yun Yu and Luay Nakhleh. Fast Algorithms for Reconciliation under Hybridization and Incomplete Lineage Sorting. 2012. Keywords: hybridization, lineage sorting, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, software. Note: http://arxiv.org/abs/1212.1909.         22 Luay Nakhleh. Phylogenetic Networks: From Displayed Trees to a Distribution of Gene Trees. Phyloseminar #70, 2017. Keywords: bayesian, coalescent, explicit network, phylogenetic network, phylogeny, Program PhyloNet, reconstruction, statistical model. Note: https://www.youtube.com/watch?v=dkQsvLUUDcQ.