ArticleBMC evolutionary biology2013
Inferring explicit weighted consensus networks to represent alternative evolutionary histories.
Article in BMC evolutionary biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A complete theoretical framework for inferring horizontal gene transfers using partial order sets.PloS one · 2023Article
- Assessing parallel gene histories in viral genomes.BMC evolutionary biology · 2016Article
- Adapting simultaneous analysis phylogenomic techniques to study complex disease gene relationships.Journal of biomedical informatics · 2015Article
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3 authors.
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Abstract
backgroundThe advent of molecular biology techniques and constant increase in availability of genetic material have triggered the development of many phylogenetic tree inference methods. However, several reticulate evolution processes, such as horizontal gene transfer and hybridization, have been shown to blur the species evolutionary history by causing discordance among phylogenies inferred from different genes.
methodsTo tackle this problem, we hereby describe a new method for inferring and representing alternative (reticulate) evolutionary histories of species as an explicit weighted consensus network which can be constructed from a collection of gene trees with or without prior knowledge of the species phylogeny.
resultsWe provide a way of building a weighted phylogenetic network for each of the following reticulation mechanisms: diploid hybridization, intragenic recombination and complete or partial horizontal gene transfer. We successfully tested our method on some synthetic and real datasets to infer the above-mentioned evolutionary events which may have influenced the evolution of many species.
conclusionsOur weighted consensus network inference method allows one to infer, visualize and validate statistically major conflicting signals induced by the mechanisms of reticulate evolution. The results provided by the new method can be used to represent the inferred conflicting signals by means of explicit and easy-to-interpret phylogenetic networks.
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