COLLEGE OF ARTS AND SCIENCES Department of Mathematics and Statistics


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Colloquium: The Generalized Neighbor Joining method


3:10 PM, Abelson 201

Ruriko Yoshida

Abstract A central task in the study of molecular sequence data from present-day species is the reconstruction of the ancestral relationships. The most established approach to tree reconstruction is the maximum likelihood (ML) method. In this method, evolution is described in terms of a discrete-state continuous-time Markov process on a phylogenetic tree. Unfortunately, an exhaustive search for the ML phylogenetic tree is computationally prohibitive for large data sets. In such situations, the neighbor-joining (NJ) method is frequently used because of its computational speed. The NJ method reconstructs trees by clustering neighboring sequences recursively, based on pairwise comparisons between the sequences. In 2005, D. Levy, L. Pachter and R. Yoshida showed that the NJ method can be generalized such that reconstruction is based on comparisons of subtrees rather than pairwise distances. In this talk, we present an outline of the generalized neighbor-joining (GNJ) method and some simulation result. Also we will explain briefly an algebraic view of discrete statistical models for estimating substitution rates with an example. This is joint work with Dan Levy and Lior Pachter.