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Efficient parsimony-based methods for phylogenetic network reconstruction
Author(s) -
Guohua Jin,
Luay Nakhleh,
Sagi Snir,
Tamir Tuller
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btl313
Subject(s) - phylogenetic tree , tree rearrangement , phylogenetic network , maximum parsimony , inference , tree (set theory) , phylogenetics , heuristics , supermatrix , biology , horizontal gene transfer , computational phylogenetics , computer science , genetic algorithm , evolutionary biology , machine learning , artificial intelligence , mathematics , gene , genetics , clade , mathematical analysis , current algebra , algebra over a field , affine lie algebra , pure mathematics , operating system
Phylogenies--the evolutionary histories of groups of organisms-play a major role in representing relationships among biological entities. Although many biological processes can be effectively modeled as tree-like relationships, others, such as hybrid speciation and horizontal gene transfer (HGT), result in networks, rather than trees, of relationships. Hybrid speciation is a significant evolutionary mechanism in plants, fish and other groups of species. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Maximum parsimony is one of the most commonly used criteria for phylogenetic tree inference. Roughly speaking, inference based on this criterion seeks the tree that minimizes the amount of evolution. In 1990, Jotun Hein proposed using this criterion for inferring the evolution of sequences subject to recombination. Preliminary results on small synthetic datasets. Nakhleh et al. (2005) demonstrated the criterion's application to phylogenetic network reconstruction in general and HGT detection in particular. However, the naive algorithms used by the authors are inapplicable to large datasets due to their demanding computational requirements. Further, no rigorous theoretical analysis of computing the criterion was given, nor was it tested on biological data.

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