Efficient algorithms for lateral gene transfer problems
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Summarizing Multiple Gene Trees Using Cluster Networks
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Phylogenetic networks do not need to be complex
Bioinformatics
Simultaneous Identification of Duplications and Lateral Gene Transfers
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
Fast FPT algorithms for computing rooted agreement forests: theory and experiments
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Fast computation of the exact hybridization number of two phylogenetic trees
ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
An algorithm for constructing parsimonious hybridization networks with multiple phylogenetic trees
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
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Phylogenetic networks provide a graphical representation of evolutionary histories that involve non-treelike evolutionary events, such as horizontal gene transfer (HGT). One approach for inferring phylogenetic networks is based on reconciling gene trees, assuming all incongruence among the gene trees is due to HGT. Several mathematical results and algorithms, both exact and heuristic, have been introduced to construct and analyze phylogenetic networks. Here, we address the computational problem of inferring phylogenetic networks with minimum reticulations from a collection of gene trees. As this problem is known to be NP-hard even for a pair of gene trees, the problem at hand is very hard. In this paper, we present an efficient heuristic, MURPAR, for inferring a phylogenetic network from a collection of gene trees by using pairwise reconciliations of trees in the collection. Given the development of efficient and accurate methods for pairwise gene tree reconciliations, MURPAR inherits this efficiency and accuracy. Further, the method includes a formulation for combining pairwise reconciliations that is naturally amenable to an efficient integer linear programming (ILP) solution. We show that MURPAR produces more accurate results than other methods and is at least as fast, when run on synthetic and biological data. We believe that our method is especially important for rapidly obtaining estimates of genome-scale evolutionary histories that can be further refined by more detailed and compute-intensive methods.