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Efficient algorithms for phylogenetic post-analysis

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/10898

Efficient algorithms for phylogenetic post-analysis

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Title: Efficient algorithms for phylogenetic post-analysis
Author: Pattengale, Nicholas
Advisor(s): Moret, Bernard
Saia, Jared
Committee Member(s): Stamatakis, Alexandros
Moore, Cristopher
Department: University of New Mexico. Dept. of Computer Science
Subject: computational biology
algorithms
LC Subject(s): Cladistic analysis--Data processing.
Decision trees.
Branching processes.
Computational biology.
Degree Level: Doctoral
Abstract: A variety of tasks are typically performed after a phylogenetic reconstruction proper -- tasks which fall under the category phylogenetic post-analysis. In this dissertation, we present novel approaches and efficient algorithms for three post-analysis tasks: taking distances between (typically, all pairs in a set of) trees, bootstrapping, and building consensus trees. For instance, it is often the case that reconstruction finds multiple plausible trees. One basic way of addressing this situation is to take distances between pairs of trees, in order to gain an understanding of the extent to which the trees disagree. The most frequently employed manner for computing the distance between a tree pair is the Robinson-Foulds metric, a natural dissimilarity measure between a pair of phylogenetic trees. We present a novel family of algorithms for efficiently computing the Robinson-Foulds metric. Bootstrapping is a post-analysis technique for drawing support values on tree edges, and is often used for assessing the extent to which the underlying data (e.g., molecular sequences) supports a reconstructed tree. The basis of the approach is to reconstruct many trees, called replicates, based on random subsampling of the original data. However, to date, there has been little treatment in phylogeny regarding the question of how many bootstrap replicates to generate. We propose bootstopping criteria which are designed to provide on-the-fly (i.e., runtime) guidance for determining when enough bootstrap replicates have been reconstructed. Another common post-analysis task is to build a consensus tree, a summary tree that attempts to capture the information agreed upon by bootstrap replicates. Unfortunately, the most popular consensus methods are susceptible to confusion by rogue taxa, i.e., taxa that cannot be placed with assurance anywhere within the tree. We present novel theory and efficient algorithms to identify rogue taxa, as well as a novel technique for interpreting the results (in the context of bootstrapping).
Graduation Date: May 2010
URI: http://hdl.handle.net/1928/10898


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