ArticleBMC bioinformatics2019
An efficient exact algorithm for computing all pairwise distances between reconciliations in the duplication-transfer-loss model.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Deciphering the Origin of DNA Viruses (Replication-Associated Parvo-NS1) That Infect Vertebrates from Invertebrate-Infecting Viruses.Microbiology spectrum · 2023Article
- Phylogenetic reconciliation.PLoS computational biology · 2022Article
- Tree Reconciliation Methods for Host-Symbiont Cophylogenetic Analyses.Life (Basel, Switzerland) · 2022Article
- Efficiently sparse listing of classes of optimal cophylogeny reconciliations.Algorithms for molecular biology : AMB · 2022Article
- Hierarchical clustering of maximum parsimony reconciliations.BMC bioinformatics · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundMaximum parsimony reconciliation in the duplication-transfer-loss model is widely used in studying the evolutionary histories of genes and species and in studying coevolution of parasites and their hosts and pairs of symbionts. While efficient algorithms are known for finding maximum parsimony reconciliations, the number of reconciliations can grow exponentially in the size of the trees. An understanding of the space of maximum parsimony reconciliations is necessary to determine whether a single reconciliation can adequately represent the space or whether multiple representative reconciliations are needed.
resultsWe show that for any instance of the reconciliation problem, the distribution of pairwise distances can be computed exactly by an efficient polynomial-time algorithm with respect to several different distance metrics. We describe the algorithm, analyze its asymptotic worst-case running time, and demonstrate its utility and viability on a large biological dataset.
conclusionsThis result provides new insights into the structure of the space of maximum parsimony reconciliations. These insights are likely to be useful in the wide range of applications that employ reconciliation methods.
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