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Polynomial algorithms for the Maximal Pairing Problem: efficient phylogenetic targeting on arbitrary trees.

Arnold C, Stadler PF - Algorithms Mol Biol (2010)

Bottom Line: We describe a relatively simple dynamic programming algorithm for the special case of binary trees.We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n) w.r.t. the number n of leaves.This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.

ABSTRACT

Background: The Maximal Pairing Problem (MPP) is the prototype of a class of combinatorial optimization problems that are of considerable interest in bioinformatics: Given an arbitrary phylogenetic tree T and weights omegaxy for the paths between any two pairs of leaves (x, y), what is the collection of edge-disjoint paths between pairs of leaves that maximizes the total weight? Special cases of the MPP for binary trees and equal weights have been described previously; algorithms to solve the general MPP are still missing, however.

Results: We describe a relatively simple dynamic programming algorithm for the special case of binary trees. We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n) w.r.t. the number n of leaves. The source code of a C implementation can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/Targeting. For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP.

Conclusions: The algorithms introduced here make it possible to solve the MPP also for large trees with high-degree vertices. This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations.

No MeSH data available.


Related in: MedlinePlus

Translation of a path-system on T[u] into a matching on the auxiliary graph Γ(chd(u)).
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Figure 2: Translation of a path-system on T[u] into a matching on the auxiliary graph Γ(chd(u)).

Mentions: For each subtree T[u, C] we therefore face the problem of determining the optimal combination of pairs and isolated children. This task can be reformulated as a weighted matching problem on an auxiliary graph Γ(C) whose vertex set consists of two copies of the elements of C, denoted v and v*. Within one copy of C, there is an edge between any two elements. The remaining /C/ edges of Γ(C) connect each v with its copy v*. The associated edge weights are ωv',v'' = and ωv,v* = Sv, respectively. An example is shown in Fig. 2.


Polynomial algorithms for the Maximal Pairing Problem: efficient phylogenetic targeting on arbitrary trees.

Arnold C, Stadler PF - Algorithms Mol Biol (2010)

Translation of a path-system on T[u] into a matching on the auxiliary graph Γ(chd(u)).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902485&req=5

Figure 2: Translation of a path-system on T[u] into a matching on the auxiliary graph Γ(chd(u)).
Mentions: For each subtree T[u, C] we therefore face the problem of determining the optimal combination of pairs and isolated children. This task can be reformulated as a weighted matching problem on an auxiliary graph Γ(C) whose vertex set consists of two copies of the elements of C, denoted v and v*. Within one copy of C, there is an edge between any two elements. The remaining /C/ edges of Γ(C) connect each v with its copy v*. The associated edge weights are ωv',v'' = and ωv,v* = Sv, respectively. An example is shown in Fig. 2.

Bottom Line: We describe a relatively simple dynamic programming algorithm for the special case of binary trees.We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n) w.r.t. the number n of leaves.This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstrasse 16-18, D-04107 Leipzig, Germany. studla@bioinf.uni-leipzig.de.

ABSTRACT

Background: The Maximal Pairing Problem (MPP) is the prototype of a class of combinatorial optimization problems that are of considerable interest in bioinformatics: Given an arbitrary phylogenetic tree T and weights omegaxy for the paths between any two pairs of leaves (x, y), what is the collection of edge-disjoint paths between pairs of leaves that maximizes the total weight? Special cases of the MPP for binary trees and equal weights have been described previously; algorithms to solve the general MPP are still missing, however.

Results: We describe a relatively simple dynamic programming algorithm for the special case of binary trees. We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n) w.r.t. the number n of leaves. The source code of a C implementation can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/Targeting. For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP.

Conclusions: The algorithms introduced here make it possible to solve the MPP also for large trees with high-degree vertices. This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations.

No MeSH data available.


Related in: MedlinePlus