On the agreement of many trees
Information Processing Letters
Maximum Agreement Subtree in a Set of Evolutionary Trees: Metrics and Efficient Algorithms
SIAM Journal on Computing
Tree Reconstruction via a Closure Operation on Partial Splits
JOBIM '00 Selected papers from the First International Conference on Computational Biology, Biology, Informatics, and Mathematics
How Many Bootstrap Replicates Are Necessary?
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Efficient algorithms for phylogenetic post-analysis
Efficient algorithms for phylogenetic post-analysis
Uncovering Hidden Phylogenetic Consensus in Large Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A metric for phylogenetic trees based on matching
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
A Metric for Phylogenetic Trees Based on Matching
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Many of the steps in phylogenetic reconstruction can be confounded by “rogue” taxa, taxa that cannot be placed with assurance anywhere within the tree—whose location within the tree, in fact, varies with almost any choice of algorithm or parameters. Phylogenetic consensus methods, in particular, are known to suffer from this problem. In this paper we provide a novel framework in which to define and identify rogue taxa. In this framework, we formulate a bicriterion optimization problem that models the net increase in useful information present in the consensus tree when certain taxa are removed from the input data. We also provide an effective greedy heuristic to identify a subset of rogue taxa and use it in a series of experiments, using both pathological examples described in the literature and a collection of large biological datasets. As the presence of rogue taxa in a set of bootstrap replicates can lead to deceivingly poor support values, we propose a procedure to recompute support values in light of the rogue taxa identified by our algorithm; applying this procedure to our biological datasets caused a large number of edges to change from “unsupported” to “supported” status, indicating that many existing phylogenies should be recomputed and reevaluated to reduce any inaccuracies introduced by rogue taxa.