Efficient algorithms for lateral gene transfer problems
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
Reconstructing reticulate evolution in species: theory and practice
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Parsimony Score of Phylogenetic Networks: Hardness Results and a Linear-Time Heuristic
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RIATA-HGT: a fast and accurate heuristic for reconstructing horizontal gene transfer
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Inferring evolutionary scenarios in the duplication, loss and horizontal gene transfer model
Logic and Program Semantics
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In a horizontal gene transfer (HGT) event a gene is transferred between two species that do not share an ancestor-descendant relationship. Typically, no more than a few genes are horizontally transferred between any two species. However, several studies identified pairs of species between which many different genes were horizontally transferred. Such a pair is said to be linked by a highway of gene sharing. We present a method for inferring such highways. Our method is based on the fact that the evolutionary histories of horizontally transferred genes disagree with the corresponding species phylogeny. Specifically, given a set of gene trees and a trusted rooted species tree, each gene tree is first decomposed into its constituent quartet trees and the quartets that are inconsistent with the species tree are identified. Our method finds a pair of species such that a highway between them explains the largest (normalized) fraction of inconsistent quartets. For a problem on n species, our method requires O(n4) time, which is optimal with respect to the quartets input size. An application of our method to a dataset of 1128 genes from 11 cyanobacterial species, as well as to simulated datasets, illustrates the efficacy of our method.