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IEEE/ACM Transactions on Computational Biology and Bioinformatics
Article . 2012 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2011
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Article . 2011
Data sources: DBLP
DBLP
Article . 2012
Data sources: DBLP
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On the Elusiveness of Clusters

Authors: S.M. Kelk (Steven); C. Scornavacca; L.J.J. van Iersel (Leo);

On the Elusiveness of Clusters

Abstract

Rooted phylogenetic networks are often used to represent conflicting phylogenetic signals. Given a set of clusters, a network is said to represent these clusters in the "softwired" sense if, for each cluster in the input set, at least one tree embedded in the network contains that cluster. Motivated by parsimony we might wish to construct such a network using as few reticulations as possible, or minimizing the "level" of the network, i.e. the maximum number of reticulations used in any "tangled" region of the network. Although these are NP-hard problems, here we prove that, for every fixed k >= 0, it is polynomial-time solvable to construct a phylogenetic network with level equal to k representing a cluster set, or to determine that no such network exists. However, this algorithm does not lend itself to a practical implementation. We also prove that the comparatively efficient Cass algorithm correctly solves this problem (and also minimizes the reticulation number) when input clusters are obtained from two not necessarily binary gene trees on the same set of taxa but does not always minimize level for general cluster sets. Finally, we describe a new algorithm which generates in polynomial-time all binary phylogenetic networks with exactly r reticulations representing a set of input clusters (for every fixed r >= 0).

Submitted

Country
Netherlands
Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Models, Genetic, Populations and Evolution (q-bio.PE), Computational Biology, FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Evolution, Molecular, FOS: Biological sciences, Cluster Analysis, Quantitative Biology - Populations and Evolution, Algorithms, Phylogeny

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Top 10%
Top 10%
Green