Reconstructing the shape of a tree from observed dissimilarity data
Advances in Applied Mathematics
Symmetric Matrices Representable by Weighted Trees Over a Cancellative Abelian Monoid
SIAM Journal on Discrete Mathematics
Fast and Accurate Phylogeny Reconstruction Algorithms Based on the Minimum-Evolution Principle
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Consistent formulas for estimating the total lengths of trees
Discrete Applied Mathematics
Consistent formulas for estimating the total lengths of trees
Discrete Applied Mathematics
An exact and polynomial distance-based algorithm to reconstruct single copy tandem duplication trees
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
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The goal of phylogenetic inference is the reconstuction of the evolutionary history of various biological entities (taxa) such as genes, proteins, viruses or species. Phylogenetic inference is of major importance in computational biology and has numerous applications ranging from the study of biodiversity to sequence analysis. Given a matrix of pairwise distances between taxa, the minimum evolution (ME) principle consists in selecting the tree whose length is minimal, where the tree length is estimated within the least-squares framework. The ME principle has been shown to be statistically consistent when using the ordinary least-squares criterion (OLS) and inconsistent with the more general weighted least-squares criterion (WLS). Unfortunately, OLS+ME inference method can provide poor results since the variances of the input data are not taken into account. Here we study a model which lies between OLS and WLS, classical in statistics and data analysis, and we prove that the ME principle is statistically consistent within this model. Our proof is inductive and relies on a time optimal recursive algorithm for estimating edge lengths. As a corollary, we obtain a different and simpler proof of the consistency result for OLS+ME.