Literature DB >> 17048405

Phylogenetic networks: modeling, reconstructibility, and accuracy.

Bernard M E Moret1, Luay Nakhleh, Tandy Warnow, C Randal Linder, Anna Tholse, Anneke Padolina, Jerry Sun, Ruth Timme.   

Abstract

Phylogenetic networks model the evolutionary history of sets of organisms when events such as hybrid speciation and horizontal gene transfer occur. In spite of their widely acknowledged importance in evolutionary biology, phylogenetic networks have so far been studied mostly for specific data sets. We present a general definition of phylogenetic networks in terms of directed acyclic graphs (DAGs) and a set of conditions. Further, we distinguish between model networks and reconstructible ones and characterize the effect of extinction and taxon sampling on the reconstructibility of the network. Simulation studies are a standard technique for assessing the performance of phylogenetic methods. A main step in such studies entails quantifying the topological error between the model and inferred phylogenies. While many measures of tree topological accuracy have been proposed, none exist for phylogenetic networks. Previously, we proposed the first such measure, which applied only to a restricted class of networks. In this paper, we extend that measure to apply to all networks, and prove that it is a metric on the space of phylogenetic networks. Our results allow for the systematic study of existing network methods, and for the design of new accurate ones.

Mesh:

Year:  2004        PMID: 17048405     DOI: 10.1109/TCBB.2004.10

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  30 in total

1.  Relaxed neighbor joining: a fast distance-based phylogenetic tree construction method.

Authors:  Jason Evans; Luke Sheneman; James Foster
Journal:  J Mol Evol       Date:  2006-04-28       Impact factor: 2.395

2.  A decomposition theory for phylogenetic networks and incompatible characters.

Authors:  Dan Gusfield; Vikas Bansal; Vineet Bafna; Yun S Song
Journal:  J Comput Biol       Date:  2007-12       Impact factor: 1.479

3.  Analyzing and reconstructing reticulation networks under timing constraints.

Authors:  Simone Linz; Charles Semple; Tanja Stadler
Journal:  J Math Biol       Date:  2010-11       Impact factor: 2.259

4.  On encodings of phylogenetic networks of bounded level.

Authors:  Philippe Gambette; Katharina T Huber
Journal:  J Math Biol       Date:  2011-07-14       Impact factor: 2.259

5.  Reconstruction of LGT networks from tri-LGT-nets.

Authors:  Gabriel Cardona; Joan Carles Pons
Journal:  J Math Biol       Date:  2017-04-27       Impact factor: 2.259

6.  Approximating model probabilities in Bayesian information criterion and decision-theoretic approaches to model selection in phylogenetics.

Authors:  Jason Evans; Jack Sullivan
Journal:  Mol Biol Evol       Date:  2010-07-29       Impact factor: 16.240

7.  Exactly computing the parsimony scores on phylogenetic networks using dynamic programming.

Authors:  Lavanya Kannan; Ward C Wheeler
Journal:  J Comput Biol       Date:  2014-02-21       Impact factor: 1.479

8.  The Multispecies Coalescent Model Outperforms Concatenation Across Diverse Phylogenomic Data Sets.

Authors:  Xiaodong Jiang; Scott V Edwards; Liang Liu
Journal:  Syst Biol       Date:  2020-07-01       Impact factor: 15.683

9.  A distance metric for a class of tree-sibling phylogenetic networks.

Authors:  Gabriel Cardona; Mercè Llabrés; Francesc Rosselló; Gabriel Valiente
Journal:  Bioinformatics       Date:  2008-05-12       Impact factor: 6.937

10.  Bootstrap-based support of HGT inferred by maximum parsimony.

Authors:  Hyun Jung Park; Guohua Jin; Luay Nakhleh
Journal:  BMC Evol Biol       Date:  2010-05-05       Impact factor: 3.260

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