Who is Who in Phylogenetic Networks

Publications of Ward C Wheeler     Order by:   Type | Year

Associated keywords

dynamic-programming explicit-network exponential-algorithm from-network from-sequences heuristic parsimony phylogenetic-network phylogeny reconstruction

2015

1 Ward C Wheeler. Phylogenetic network analysis as a parsimony optimization problem. In BMCB, Vol. 16(296):1-9, 2015. Keywords: explicit network, from sequences, parsimony, phylogenetic network, phylogeny, reconstruction. Note: http://dx.doi.org/10.1186/s12859-015-0675-0.

2014

2 Ward C Wheeler. Phyletic groups on networks. In Cladistics, Vol. 30(4):447-451, 2014. Keywords: explicit network, from network, phylogenetic network, phylogeny. Note: http://dx.doi.org/10.1111/cla.12062.         Toggle abstract "Three additional phyletic group types, "periphyletic," "epiphyletic", and "anaphyletic" (in addition to Hennigian mono-, para-, and polyphyletic) are defined in terms of trees and phylogenetic networks (trees with directed reticulate edges) via a generalization of the algorithmic definitions of Farris. These designations concern groups defined as monophyletic on trees, but with additional gains or losses of members from network edges. These distinctions should be useful in discussion of systems with non-vertical inheritance such as recombination between viruses, horizontal exchange between bacteria, hybridization in plants and animals, as well as human linguistic evolution. Examples are illustrated with Indo-European language groups. © The Willi Hennig Society 2013." 3 Lavanya Kannan and Ward C Wheeler. Exactly Computing the Parsimony Scores on Phylogenetic Networks Using Dynamic Programming. In JCB, Vol. 21(4):303-319, 2014. Keywords: explicit network, exponential algorithm, from network, from sequences, parsimony, phylogenetic network, phylogeny, reconstruction.         Toggle abstract "Scoring a given phylogenetic network is the first step that is required in searching for the best evolutionary framework for a given dataset. Using the principle of maximum parsimony, we can score phylogenetic networks based on the minimum number of state changes across a subset of edges of the network for each character that are required for a given set of characters to realize the input states at the leaves of the networks. Two such subsets of edges of networks are interesting in light of studying evolutionary histories of datasets: (i) the set of all edges of the network, and (ii) the set of all edges of a spanning tree that minimizes the score. The problems of finding the parsimony scores under these two criteria define slightly different mathematical problems that are both NP-hard. In this article, we show that both problems, with scores generalized to adding substitution costs between states on the endpoints of the edges, can be solved exactly using dynamic programming. We show that our algorithms require O(mpk) storage at each vertex (per character), where k is the number of states the character can take, p is the number of reticulate vertices in the network, m = k for the problem with edge set (i), and m = 2 for the problem with edge set (ii). This establishes an O(nmpk2) algorithm for both the problems (n is the number of leaves in the network), which are extensions of Sankoff's algorithm for finding the parsimony scores for phylogenetic trees. We will discuss improvements in the complexities and show that for phylogenetic networks whose underlying undirected graphs have disjoint cycles, the storage at each vertex can be reduced to O(mk), thus making the algorithm polynomial for this class of networks. We will present some properties of the two approaches and guidance on choosing between the criteria, as well as traverse through the network space using either of the definitions. We show that our methodology provides an effective means to study a wide variety of datasets. © Copyright 2014, Mary Ann Liebert, Inc. 2014."

2012

4 Lavanya Kannan and Ward C Wheeler. Maximum Parsimony on Phylogenetic Networks. In ALMOB, Vol. 7:9, 2012. Keywords: dynamic programming, explicit network, from sequences, heuristic, parsimony, phylogenetic network, phylogeny. Note: http://dx.doi.org/10.1186/1748-7188-7-9.         Toggle abstract "Background: Phylogenetic networks are generalizations of phylogenetic trees, that are used to model evolutionary events in various contexts. Several different methods and criteria have been introduced for reconstructing phylogenetic trees. Maximum Parsimony is a character-based approach that infers a phylogenetic tree by minimizing the total number of evolutionary steps required to explain a given set of data assigned on the leaves. Exact solutions for optimizing parsimony scores on phylogenetic trees have been introduced in the past.Results: In this paper, we define the parsimony score on networks as the sum of the substitution costs along all the edges of the network; and show that certain well-known algorithms that calculate the optimum parsimony score on trees, such as Sankoff and Fitch algorithms extend naturally for networks, barring conflicting assignments at the reticulate vertices. We provide heuristics for finding the optimum parsimony scores on networks. Our algorithms can be applied for any cost matrix that may contain unequal substitution costs of transforming between different characters along different edges of the network. We analyzed this for experimental data on 10 leaves or fewer with at most 2 reticulations and found that for almost all networks, the bounds returned by the heuristics matched with the exhaustively determined optimum parsimony scores.Conclusion: The parsimony score we define here does not directly reflect the cost of the best tree in the network that displays the evolution of the character. However, when searching for the most parsimonious network that describes a collection of characters, it becomes necessary to add additional cost considerations to prefer simpler structures, such as trees over networks. The parsimony score on a network that we describe here takes into account the substitution costs along the additional edges incident on each reticulate vertex, in addition to the substitution costs along the other edges which are common to all the branching patterns introduced by the reticulate vertices. Thus the score contains an in-built cost for the number of reticulate vertices in the network, and would provide a criterion that is comparable among all networks. Although the problem of finding the parsimony score on the network is believed to be computationally hard to solve, heuristics such as the ones described here would be beneficial in our efforts to find a most parsimonious network. © 2012 Kannan and Wheeler; licensee BioMed Central Ltd."