ArticleJournal of mathematical biology2019
Reconciling event-labeled gene trees with MUL-trees and species networks.
Article in Journal of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Predicting horizontal gene transfers with perfect transfer networks.Algorithms for molecular biology : AMB · 2024Article
- The Theory of Gene Family Histories.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Clustering systems of phylogenetic networks.Theory in biosciences = Theorie in den Biowissenschaften · 2023Article
- Embedding gene trees into phylogenetic networks by conflict resolution algorithms.Algorithms for molecular biology : AMB · 2022Article
- Best match graphs and reconciliation of gene trees with species trees.Journal of mathematical biology · 2020Article
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Authors and funding
3 authors.
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Abstract
Phylogenomics commonly aims to construct evolutionary trees from genomic sequence information. One way to approach this problem is to first estimate event-labeled gene trees (i.e., rooted trees whose non-leaf vertices are labeled by speciation or gene duplication events), and to then look for a species tree which can be reconciled with this tree through a reconciliation map between the trees. In practice, however, it can happen that there is no such map from a given event-labeled tree to any species tree. An important situation where this might arise is where the species evolution is better represented by a network instead of a tree. In this paper, we therefore consider the problem of reconciling event-labeled trees with species networks. In particular, we prove that any event-labeled gene tree can be reconciled with some network and that, under certain mild assumptions on the gene tree, the network can even be assumed to be multi-arc free. To prove this result, we show that we can always reconcile the gene tree with some multi-labeled (MUL-)tree, which can then be "folded up" to produce the desired reconciliation and network. In addition, we study the interplay between reconciliation maps from event-labeled gene trees to MUL-trees and networks. Our results could be useful for understanding how genomes have evolved after undergoing complex evolutionary events such as polyploidy.
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