On the Repeat-Annotated Phylogenetic Tree Reconstruction Problem

2006 ◽  
pp. 141-152 ◽  
Author(s):  
Firas Swidan ◽  
Michal Ziv-Ukelson ◽  
Ron Y. Pinter
Keyword(s):  
Phylogenetic Tree ◽  
Reconstruction Problem ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction

On the Repeat-Annotated Phylogenetic Tree Reconstruction Problem

2006 ◽  
Vol 13 (8) ◽  
pp. 1397-1418 ◽  
Author(s):  
Firas Swidan ◽  
Michal Ziv-Ukelson ◽  
Ron Y. Pinter
Keyword(s):  
Phylogenetic Tree ◽  
Reconstruction Problem ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction

RECONSTRUCTING AN ULTRAMETRIC GALLED PHYLOGENETIC NETWORK FROM A DISTANCE MATRIX

2006 ◽  
Vol 04 (04) ◽  
pp. 807-832 ◽  
Author(s):  
HO-LEUNG CHAN ◽  
JESPER JANSSON ◽  
TAK-WAH LAM ◽  
SIU-MING YIU
Keyword(s):  
Phylogenetic Tree ◽  
Phylogenetic Network ◽  
Distance Matrix ◽  
Time Algorithm ◽  
Phylogenetic Networks ◽  
Np Hard ◽  
Reconstruction Problem ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction

Given a distance matrix M that specifies the pairwise evolutionary distances between n species, the phylogenetic tree reconstruction problem asks for an edge-weighted phylogenetic tree that satisfies M, if one exists. We study some extensions of this problem to rooted phylogenetic networks. Our main result is an O(n2 log n)-time algorithm for determining whether there is an ultrametric galled network that satisfies M, and if so, constructing one. In fact, if such an ultrametric galled network exists, our algorithm is guaranteed to construct one containing the minimum possible number of nodes with more than one parent (hybrid nodes). We also prove that finding a largest possible submatrix M′ of M such that there exists an ultrametric galled network that satisfies M′ is NP-hard. Furthermore, we show that given an incomplete distance matrix (i.e. where some matrix entries are missing), it is also NP-hard to determine whether there exists an ultrametric galled network which satisfies it.

scholarly journals Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks

2020 ◽  
Vol 37 (12) ◽  
pp. 3632-3641
Author(s):  
Alina F Leuchtenberger ◽  
Stephen M Crotty ◽  
Tamara Drucks ◽  
Heiko A Schmidt ◽  
Sebastian Burgstaller-Muehlbacher ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Maximum Likelihood ◽  
Phylogenetic Tree ◽  
Maximum Parsimony ◽  
Reconstruction Method ◽  
Long Branch Attraction ◽  
Tree Reconstruction ◽  
Phylogenetic Tree Reconstruction ◽  
The Neural Network

Abstract Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

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