A few logs suffice to build (almost) all trees: part II
Theoretical Computer Science
The Structure of Circular Decomposable Metrics
ESA '96 Proceedings of the Fourth Annual European Symposium on Algorithms
Reconstructing reticulate evolution in species: theory and practice
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
Phylogenetic Networks: Modeling, Reconstructibility, and Accuracy
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Seeing the trees and their branches in the network is hard
Theoretical Computer Science
COCOA 2008 Proceedings of the 2nd international conference on Combinatorial Optimization and Applications
Counting Faces in Split Networks
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Fast algorithms for computing the tripartition-based distance between phylogenetic networks
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
The maximum agreement of two nested phylogenetic networks
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Phylogenetic networks, trees, and clusters
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Phylogenetic networks: properties and relationship to trees and clusters
Transactions on Computational Systems Biology II
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We introduce NeighborNet, a network construction and data representation method that combines aspects of the neighbor joining (NJ) and SplitsTree. Like NJ, NeighborNet uses agglomeration: taxa are combined into progressively larger and larger overlapping clusters. Like SPLITSTREE, NeighborNet constructs networks rather than trees, and so can be used to represent multiple phylogenetic hypotheses simultaneously, or to detect complex evolutionary processes like recombination, lateral transfer and hybridization. NeighborNet tends to produce networks that are substantially more resolved than those made with SPLITSTREE. The method is efficient (O(n3) time) and is well suited for the preliminary analyses of complex phylogenetic data. We report results of three case studies: one based on mitochondrial gene order data from early branching eukaryotes, another based on nuclear sequence data from New Zealand alpine buttercups (Ranunculi), and a third on poorly corrected synthetic data.