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    Statistical analysis of species tree inference

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    Author
    Dajles, Andres
    Chair
    Rhodes, John
    Committee
    Allman, Elizabeth
    Goddard, Scott
    Short, Margaret
    Barry, Ron
    Keyword
    Trees
    Cladistic analysis
    Genome mapping
    Identification
    Roots
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/8041
    Abstract
    It is known that the STAR and USTAR algorithms are statistically consistent techniques used to infer species tree topologies from a large set of gene trees. However, if the set of gene trees is small, the accuracy of STAR and USTAR in determining species tree topologies is unknown. Furthermore, it is unknown how introducing roots on the gene trees affects the performance of STAR and USTAR. Therefore, we show that when given a set of gene trees of sizes 1, 3, 6 or 10, the STAR and USTAR algorithms with Neighbor Joining perform relatively well for two different cases: one where the gene trees are rooted at the outgroup and the STAR inferred species tree is also rooted at the outgroup, and the other where the gene trees are not rooted at the outgroup, but the USTAR inferred species tree is rooted at the outgroup. It is known that the STAR and USTAR algorithms are statistically consistent techniques used to infer species tree topologies from a large set of gene trees. However, if the set of gene trees is small, the accuracy of STAR and USTAR in determining species tree topologies is unknown. Furthermore, it is unknown how introducing roots on the gene trees affects the performance of STAR and USTAR. Therefore, we show that when given a set of gene trees of sizes 1, 3, 6 or 10, the STAR and USTAR algorithms with Neighbor Joining perform relatively well for two different cases: one where the gene trees are rooted at the outgroup and the STAR inferred species tree is also rooted at the outgroup, and the other where the gene trees are not rooted at the outgroup, but the USTAR inferred species tree is rooted at the outgroup.
    Description
    Master's Project (M.S.) University of Alaska Fairbanks, 2016
    Date
    2016-05
    Type
    Master's Project
    Collections
    Mathematics and Statistics

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