





Jittat Fakcharoenphol,
Tanee Kumpijit and
Attakorn Putwattana. A Faster Algorithm for the Tree Containment Problem for Binary Nearly Stable Phylogenetic Networks. In Proceedings of the The 12th International Joint Conference on Computer Science and Software Engineering (JCSSE'15), Pages 337342, IEEE, 2015. Keywords: dynamic programming, explicit network, from network, from rooted trees, nearlystable network, phylogenetic network, phylogeny, polynomial, tree containment.








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/10.1186/1748718879.
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"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 characterbased 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 wellknown 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 inbuilt 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."






JeanPhilippe Doyon,
Celine Scornavacca,
Konstantin Yu Gorbunov,
Gergely J. Szöllösi,
Vincent Ranwez and
Vincent Berry. An efficient algorithm for gene/species trees parsimonious reconciliation with losses, duplications, and transfers. In Proceedings of the Eighth RECOMB Comparative Genomics Satellite Workshop (RECOMBCG'10), Vol. 6398:93108 of LNCS, springer, 2011. Keywords: branch length, duplication, dynamic programming, explicit network, from multilabeled tree, from species tree, from unrooted trees, lateral gene transfer, loss, phylogenetic network, phylogeny, polynomial, Program Mowgli, reconstruction. Note: http://www.lirmm.fr/~vberry/Publis/MPRDoyonEtAl.pdf, software available at http://www.atgcmontpellier.fr/MPR/.
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"Tree reconciliation methods aim at estimating the evolutionary events that cause discrepancy between gene trees and species trees. We provide a discrete computational model that considers duplications, transfers and losses of genes. The model yields a fast and exact algorithm to infer time consistent and most parsimonious reconciliations. Then we study the conditions under which parsimony is able to accurately infer such events. Overall, it performs well even under realistic rates, transfers being in general less accurately recovered than duplications. An implementation is freely available at http://www.atgc montpellier.fr/MPR. © 2010 SpringerVerlag."



Lawrence A. David and
Eric J. Alm. Rapid evolutionary innovation during an Archaean genetic expansion. In Nature, Vol. 469:9396, 2011. Keywords: duplication, dynamic programming, from multilabeled tree, from rooted trees, from species tree, parsimony, phylogenetic network, phylogeny, Program Angst. Note: http://dx.doi.org/10.1038/nature09649, Program Angst described here.



Louxin Zhang,
Yen Kaow Ng,
Taoyang Wu and
Yu Zheng. Network model and efficient method for detecting relative duplications or horizontal gene transfers. In ICCABS11, Pages 214219, 2011. Keywords: dynamic programming, explicit network, from network, from rooted trees, from species tree, phylogenetic network, phylogeny, polynomial, reconstruction.
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"Background: Horizontal gene transfer and gene duplication are two significant forces behind genome evolution. As more and more wellsupported examples of HGTs are being revealed, there is a growing awareness that HGT is more widespread than previously thought, occurring often not only within bacteria, but also between species remotely related such as bacteria and plants or plants and animals. Although a substantial number of genomic sequences are known, HGT inference remains challenging. Parsimonybased inferences of HGT events are typically NPhard under the framework of gene tree and species tree comparison; it is even more timeconsuming if the maximum likelihood approach is used. The fact that gene tree and species tree incongruence can be further confounded by gene duplication and gene loss events motivates us to incorporate considerations for these events into our inference of HGT events. Similarly, it will be beneficial if known HGT events are considered in the inference of gene duplications and gene losses. © 2011 IEEE."






Ali Tofigh. Using Trees to Capture Reticulate Evolution, Lateral Gene Transfers and Cancer Progression. PhD thesis, KTH Royal Institute of Technology, Sweden, 2009. Keywords: duplication, dynamic programming, from multilabeled tree, from rooted trees, from species tree, lateral gene transfer, loss, NP complete, phylogenetic network, phylogeny, reconstruction. Note: http://kth.divaportal.org/smash/record.jsf?pid=diva2:220830&searchId=1.



Bui Quang Minh,
Fabio Pardi,
Steffen Klaere and
Arndt von Haeseler. Budgeted Phylogenetic Diversity on Circular Split Systems. In TCBB, Vol. 6(1):2229, 2009. Keywords: abstract network, circular split system, dynamic programming, from network, phylogenetic network, polynomial, split, split network. Note: http://dx.doi.org/10.1109/TCBB.2008.54.
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"In the last 15 years, Phylogenetic Diversity (PD) has gained interest in the community of conservation biologists as a surrogate measure for assessing biodiversity. We have recently proposed two approaches to select taxa for maximizing PD, namely PD with budget constraints and PD on split systems. In this paper, we will unify these two strategies and present a dynamic programming algorithm to solve the unified framework of selecting taxa with maximal PD under budget constraints on circular split systems. An improved algorithm will also be given if the underlying split system is a tree. © 2006 IEEE."








Charles Choy,
Jesper Jansson,
Kunihiko Sadakane and
WingKin Sung. Computing the maximum agreement of phylogenetic networks. In TCS, Vol. 335(1):93107, 2005. Keywords: dynamic programming, FPT, level k phylogenetic network, MASN, NP complete, phylogenetic network, phylogeny. Note: http://www.df.lth.se/~jj/Publications/masn8_TCS2005.pdf.
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"We introduce the maximum agreement phylogenetic subnetwork problem (MASN) for finding branching structure shared by a set of phylogenetic networks. We prove that the problem is NPhard even if restricted to three phylogenetic networks and give an O(n2)time algorithm for the special case of two level1 phylogenetic networks, where n is the number of leaves in the input networks and where N is called a levelf phylogenetic network if every biconnected component in the underlying undirected graph induces a subgraph of N containing at most f nodes with indegree 2. We also show how to extend our technique to yield a polynomialtime algorithm for any two levelf phylogenetic networks N1,N2 satisfying f=O(logn); more precisely, its running time is O(V(N1)·V(N2)·2f1+f2), where V(Ni) and fi denote the set of nodes in Ni and the level of Ni, respectively, for i∈{1,2}. © 2005 Elsevier B.V. All rights reserved."








Charles Choy,
Jesper Jansson,
Kunihiko Sadakane and
WingKin Sung. Computing the maximum agreement of phylogenetic networks. In Proceedings of Computing: the Tenth Australasian Theory Symposium (CATS'04), Vol. 91:134147 of Electronic Notes in Theoretical Computer Science, 2004. Keywords: dynamic programming, FPT, level k phylogenetic network, MASN, NP complete, phylogenetic network, phylogeny. Note: http://www.df.lth.se/~jj/Publications/masn6_CATS2004.pdf.
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"We introduce the maximum agreement phylogenetic subnetwork problem (MASN) of finding a branching structure shared by a set of phylogenetic networks. We prove that the problem is NPhard even if restricted to three phylogenetic networks and give an O(n2)time algorithm for the special case of two level1 phylogenetic networks, where n is the number of leaves in the input networks and where N is called a levelf phylogenetic network if every biconnected component in the underlying undirected graph contains at most f nodes having indegree 2 in N. Our algorithm can be extended to yield a polynomialtime algorithm for two levelf phylogenetic networks N 1,N2 for any f which is upperbounded by a constant; more precisely, its running time is O(V(N1)·V(N 2)·4f), where V(Ni) denotes the set of nodes of Ni. © 2004 Published by Elsevier B.V."



