Factors affecting the performance of phylogenetic methods : a thesis presented in partial fulfilment of the requirements for the degree of Ph.D. in Mathematics at Massey University
type: CreativeWork
description: This thesis comprises several computer simulation experiments in which the performance of a selection of phylogenetic methods was assessed. Data were generated according to a known model and used as input for the phylogenetic methods. Some new methods were introduced, and their performance compared with extant methods. Performance was judged by several criteria, being accuracy, consistency, efficiency, falsifiability and robustness. The experiments were designed to be biologically relevant, and yet computationally tractible. Hence the models of evolution used were simple, to allow a wide range of parameters to be tested for their effects within the bounds of available computing resources. The experiments were divided into two main types, the "small n" with up to 10 taxa, and the "large n" with from 10 to 30 taxa. Parameters which were allowed to vary in the "small n" case included number of taxa (n), sequence length, tree topology, edge length probability distribution, and purity of data. In the "large n" case, number of taxa, sequence length, and edge length probability distribution were varied. The simulation experiments show that the accuracy of phylogenetic methods decreases with increasing n, and that the mean number of internal edges of the generating tree which are incorrectly inferred increases at least linearly with n. The rate at which the sequence length must increase with n, to retain a fixed confidence in the inferred tree, is shown to be at least linear in n. All the methods are approximately as susceptible as each other to sampling error, which is exacerbated by the generating tree having very short or very long internal edges, and by finite sequence length. All the methods are susceptible to random error such as sequencing error, but provided such error is small, the effect is not great. One type of method, using edge lengths inferred by the Hadamard conjugation process, is shown to be much more robust to impure data and to sequencing error than are …
abstract: This thesis comprises several computer simulation experiments in which the performance of a selection of phylogenetic methods was assessed. Data were generated according to a known model and used as input for the phylogenetic methods. Some new methods were introduced, and their performance compared with extant methods. Performance was judged by several criteria, being accuracy, consistency, efficiency, falsifiability and robustness. The experiments were designed to be biologically relevant, and yet computationally tractible. Hence the models of evolution used were simple, to allow a wide range of parameters to be tested for their effects within the bounds of available computing resources. The experiments were divided into two main types, the "small n" with up to 10 taxa, and the "large n" with from 10 to 30 taxa. Parameters which were allowed to vary in the "small n" case included number of taxa (n), sequence length, tree topology, edge length probability distribution, and purity of data. In the "large n" case, number of taxa, sequence length, and edge length probability distribution were varied. The simulation experiments show that the accuracy of phylogenetic methods decreases with increasing n, and that the mean number of internal edges of the generating tree which are incorrectly inferred increases at least linearly with n. The rate at which the sequence length must increase with n, to retain a fixed confidence in the inferred tree, is shown to be at least linear in n. All the methods are approximately as susceptible as each other to sampling error, which is exacerbated by the generating tree having very short or very long internal edges, and by finite sequence length. All the methods are susceptible to random error such as sequencing error, but provided such error is small, the effect is not great. One type of method, using edge lengths inferred by the Hadamard conjugation process, is shown to be much more robust to impure data and to sequencing error than are …
author: Charleston, Michael A.
keywords: Phylogenetic methods, Evolution models
date published: 1994
date modified: 2026-07-27T20:17:42Z
provider: Massey University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massey University