ArticleBMC bioinformatics2009
Optimized ancestral state reconstruction using Sankoff parsimony.
Article in BMC bioinformatics, 2009. 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.
- A geographic history of human genetic ancestry.bioRxiv : the preprint server for biology · 2024Article
- B cell phylogenetics in the single cell era.Trends in immunology · 2024Review
- The histone modification H3K27me3 is retained after gene duplication and correlates with conserved noncoding sequences in Arabidopsis.Genome biology and evolution · 2014Article
- Gene network biological validity based on gene-gene interaction relevance.TheScientificWorldJournal · 2014Article
- Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states.BMC bioinformatics · 2011Article
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4 authors.
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Abstract
backgroundParsimony methods are widely used in molecular evolution to estimate the most plausible phylogeny for a set of characters. Sankoff parsimony determines the minimum number of changes required in a given phylogeny when a cost is associated to transitions between character states. Although optimizations exist to reduce the computations in the number of taxa, the original algorithm takes time O(n(2)) in the number of states, making it impractical for large values of n.
resultsIn this study we introduce an optimization of Sankoff parsimony for the reconstruction of ancestral states when ultrametric or additive cost matrices are used. We analyzed its performance for randomly generated matrices, Jukes-Cantor and Kimura's two-parameter models of DNA evolution, and in the reconstruction of elongation factor-1alpha and ancestral metabolic states of a group of eukaryotes, showing that in all cases the execution time is significantly less than with the original implementation.
conclusionThe algorithms here presented provide a fast computation of Sankoff parsimony for a given phylogeny. Problems where the number of states is large, such as reconstruction of ancestral metabolism, are particularly adequate for this optimization. Since we are reducing the computations required to calculate the parsimony cost of a single tree, our method can be combined with optimizations in the number of taxa that aim at finding the most parsimonious tree.
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