Literature DB >> 12028728

Reconstruction of biogeographic and evolutionary networks using reticulograms.

Pierre Legendre1, Vladimir Makarenkov.   

Abstract

A reticulogram is a general network capable of representing a reticulate evolutionary structure. It is particularly useful for portraying relationships among organisms that may be related in a nonunique way to their common ancestor - relationships that cannot be represented by a dendrogram or a phylogenetic tree. We propose a new method for constructing reticulograms that represent a given distance matrix. Reticulate evolution applies first to phylogenetic problems; it has been found in nature, for example, in the within-species microevolution of eukaryotes and in lateral gene transfer in bacteria. In this paper, we propose a new method for reconstructing reticulation networks and we develop applications of the reticulate evolution model to ecological biogeographic, population microevolutionary, and hybridization problems. The first example considers a spatially constrained reticulogram representing the postglacial dispersal of freshwater fishes in the Québec peninsula; the reticulogram provides a better model of postglacial dispersal than does a tree model. The second example depicts the morphological similarities among local populations of muskrats in a river valley in Belgium; adding supplementary branches to a tree depicting the river network leads to a better representation of the morphological distances among local populations of muskrats than does a tree structure. A third example involves hybrids between plants of the genus Aphelandra.

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Year:  2002        PMID: 12028728     DOI: 10.1080/10635150252899725

Source DB:  PubMed          Journal:  Syst Biol        ISSN: 1063-5157            Impact factor:   15.683


  10 in total

1.  T-REX: a web server for inferring, validating and visualizing phylogenetic trees and networks.

Authors:  Alix Boc; Alpha Boubacar Diallo; Vladimir Makarenkov
Journal:  Nucleic Acids Res       Date:  2012-06-06       Impact factor: 16.971

2.  Phylogeography and domestication of Chinese swamp buffalo.

Authors:  Xiang-Peng Yue; Ran Li; Wen-Mei Xie; Ping Xu; Ti-Cheng Chang; Li Liu; Feng Cheng; Run-Feng Zhang; Xian-Yong Lan; Hong Chen; Chu-Zhao Lei
Journal:  PLoS One       Date:  2013-02-20       Impact factor: 3.240

3.  Inferring explicit weighted consensus networks to represent alternative evolutionary histories.

Authors:  Mehdi Layeghifard; Pedro R Peres-Neto; Vladimir Makarenkov
Journal:  BMC Evol Biol       Date:  2013-12-23       Impact factor: 3.260

4.  Using hybridization networks to retrace the evolution of Indo-European languages.

Authors:  Matthieu Willems; Etienne Lord; Louise Laforest; Gilbert Labelle; François-Joseph Lapointe; Anna Maria Di Sciullo; Vladimir Makarenkov
Journal:  BMC Evol Biol       Date:  2016-09-06       Impact factor: 3.260

5.  Tree-Based Unrooted Phylogenetic Networks.

Authors:  A Francis; K T Huber; V Moulton
Journal:  Bull Math Biol       Date:  2017-12-13       Impact factor: 1.758

6.  Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations.

Authors:  Violeta Beleva Guthrie; David L Masica; Andrew Fraser; Joseph Federico; Yunfan Fan; Manel Camps; Rachel Karchin
Journal:  Mol Biol Evol       Date:  2018-06-01       Impact factor: 16.240

7.  Horizontal gene transfer and recombination analysis of SARS-CoV-2 genes helps discover its close relatives and shed light on its origin.

Authors:  Vladimir Makarenkov; Bogdan Mazoure; Guillaume Rabusseau; Pierre Legendre
Journal:  BMC Ecol Evol       Date:  2021-01-21

8.  Prokaryotic evolution and the tree of life are two different things.

Authors:  Eric Bapteste; Maureen A O'Malley; Robert G Beiko; Marc Ereshefsky; J Peter Gogarten; Laura Franklin-Hall; François-Joseph Lapointe; John Dupré; Tal Dagan; Yan Boucher; William Martin
Journal:  Biol Direct       Date:  2009-09-29       Impact factor: 4.540

9.  Phylogenetic information content of Copepoda ribosomal DNA repeat units: ITS1 and ITS2 impact.

Authors:  Maxim V Zagoskin; Valentina I Lazareva; Andrey K Grishanin; Dmitry V Mukha
Journal:  Biomed Res Int       Date:  2014-08-18       Impact factor: 3.411

10.  A Mutation Network Method for Transmission Analysis of Human Influenza H3N2.

Authors:  Chi Zhang; Yinghan Wang; Cai Chen; Haoyu Long; Junbo Bai; Jinfeng Zeng; Zicheng Cao; Bing Zhang; Wei Shen; Feng Tang; Shiwen Liang; Caijun Sun; Yuelong Shu; Xiangjun Du
Journal:  Viruses       Date:  2020-10-03       Impact factor: 5.048

  10 in total

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