UPGMA (Unweighted Pair Group Method with Arithmetic Mean) is a widely used clustering method. Here we show that UPGMA is a greedy heuristic for the normalized equidistant minimum evolution (NEME) problem, that is, finding a rooted tree that minimizes the minimum evolution score relative to the dissimilarity matrix among all rooted trees with the same leaf-set in which all leaves have the same distance to the root. We prove that the NEME problem is NP-hard. In addition, we present some heuristic and approximation algorithms for solving the NEME problem, including a polynomial time algorithm that yields a binary, rooted tree whose NEME score is within O(log^2 n) of the optimum. We expect that these results to eventually provide further insights into the behavior of the UPGMA algorithm.
Another recent, related direction of work considers the algebraic structure of the space of rooted phylogenetic trees induced by the UPGMA method (see, e.g. @cite_17 @cite_16 ). This algebraic structure is tightly linked with the property of consistency of a tree construction method, that is, those conditions under which the method is able to reconstruct a tree that has been used to generate the input dissimilarity matrix (see, e.g. @cite_12 ). In the context of our work, we are particularly interested in the consistency of methods that perform a local search of the space of all rooted phylogenetic trees on a fixed set of leaves (see, e.g. @cite_19 ). Again, balanced minimum evolution is the variant of minimum evolution for which some consistency results of this type are known @cite_25 @cite_23 .
@cite_19 @cite_23 @cite_16 @cite_25 @cite_12 @cite_17
1847059380 2047401539 2953216280 1986154345
UPGMA is a heuristic method identifying the least squares equidistant phylogenetic tree given empirical distance data among @math taxa. We study this classic algorithm using the geometry of the space of all equidistant trees with @math leaves, also known as the Bergman complex of the graphical matroid for the complete graph @math . We show that UPGMA performs an orthogonal projection of the data onto a maximal cell of the Bergman complex. We also show that the equidistant tree with the least (Euclidean) distance from the data is obtained from such an orthogonal projection, but not necessarily given by UPGMA. Using this geometric information we give an extension of the UPGMA algorithm. We also present a branch and bound method for finding the best equidistant tree. Finally, we prove that there are distance data among @math taxa which project to at least @math equidistant trees. Distance-based methods such as UPGMA (Unweighted Pair Group Method with Arithmetic Mean) continue to play a significant role in phylogenetic research. We use polyhedral combinatorics to analyze the natural subdivision of the positive orthant induced by classifying the input vectors according to tree topologies returned by the algorithm. The partition lattice informs the study of UPGMA trees. We give a closed form for the extreme rays of UPGMA cones on n taxa, and compute the spherical volumes of the UPGMA cones for small n. Given natural limitations on the length DNA sequences, designing phylogenetic reconstruction methods which are reliable under limited information is a crucial endeavor. There have been two approaches to this problem: reconstructing partial but reliable information about the tree ( Mo07, DMR08,DHJ06,GMS08 ), and reaching "deeper" in the tree through reconstruction of ancestral sequences. In the latter category, DMR06 settled an important conjecture of M.Steel, showing that, under the CFN model of evolution, all trees on @math leaves with edge lengths bounded by the Ising model phase transition can be recovered with high probability from genomes of length @math with a polynomial time algorithm. Their methods had a running time of @math . Here we enhance our methods from DHJ06 with the learning of ancestral sequences and provide an algorithm for reconstructing a sub-forest of the tree which is reliable given available data, without requiring a-priori known bounds on the edge lengths of the tree. Our methods are based on an intuitive minimum spanning tree approach and run in @math time. For the case of full reconstruction of trees with edges under the phase transition, we maintain the same sequence length requirements as DMR06 , despite the considerably faster running time. Distance-based phylogenetic methods attempt to reconstruct an accurate phylogenetic tree from an estimated matrix of pairwise distances between taxa. This paper examines two distance-based algorithms (GreedyBME and FastME) that are based on the principle of minimizing the balanced minimum evolution score of the output tree in relation to the given estimated distance matrix. This is also the principle that underlies the neighbor-joining (NJ) algorithm. We show that GreedyBME and FastME both reconstruct the entire correct tree if the input data are quartet consistent, and also that if the maximum error of any distance estimate is @math , then both algorithms output trees containing all sufficiently long edges of the true tree: those having length at least @math . That is to say, the algorithms have edge safety radius 1 3. In contrast, quartet consistency of the data is not sufficient to guarantee the NJ algorithm reconstructs the correct tree, and moreover, the NJ algorithm has edge safety radius of 1 4: Only edges of the true tree of length at least @math can be guaranteed to appear in the output. These results give further theoretical support to the experimental evidence suggesting FastME is a more suitable distance-based phylogeny reconstruction method than the NJ algorithm.