On the complexity of comparing evolutionary trees
Discrete Applied Mathematics - Special volume on computational molecular biology
The asymmetric median tree—a new model for building consensus trees
Discrete Applied Mathematics - Special volume on computational molecular biology
On distances between phylogenetic trees
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Matching Theory (North-Holland mathematics studies)
Matching Theory (North-Holland mathematics studies)
Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Gene-Duplication Problem: Near-Linear Time Algorithms for NNI-Based Local Searches
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The gene-duplication problem: near-linear time algorithms for NNI based local searches
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
Fast local search for unrooted Robinson-foulds supertrees
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Fast Local Search for Unrooted Robinson-Foulds Supertrees
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The inference of evolutionary trees using approaches which attempt to solve the maximum parsimony (MP) and maximum likelihood (ML) optimization problems is a standard part of much of biological data analysis. However, both problems are hard to solve: MP provably NP-hard, and ML even harder in practice. Consequently, hill-climbing heuristics are used to analyze datasets for phylogeny reconstruction. Two primary topological transformations have been used in the most popular heuristics: TBR (tree-bisection-and-reconnection) and ECR (edge-contractions-and-refinements). While most of the popular heuristics exclusively use TBR moves to explore tree space, some recent methods have used ECR in conjunction with TBR and found significant improvements in the speed and accuracy with which they can analyze datasets. In this paper we analyze ECR moves in detail, and provide results on the diameter of the tree space, the neighborhood intersection with TBR, structural analysis of the ECR operation, and an efficient method for sampling uniformly from the 2-ECR neighborhood of a tree. Our results should lead to a better understanding of the impact of ECR moves on the performance of heuristic searches.