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Activity Number: 424
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #309740
Title: Nonparametric Estimation of Phylogenetic Tree Distributions
Author(s): Grady Weyenberg*+
Companies: University of Kentucky
Keywords: phylogenetics ; gene trees ; R package
Abstract:

While the majority of gene histories found in a clade of organisms are expected to be generated by a common process (e.g. the coalescent process), it is well-known that numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication) will cause some genes to exhibit a history quite distinct from the histories of the majority of genes. Such "outlying" gene trees are considered to be biologically interesting and identifying these genes has become an important problem in phylogenetics. In this talk we propose and implement a nonparametric method of estimating distributions of phylogenetic trees, with the goal of identifying trees which are significantly different from the rest of the trees in the sample.

Our approach mimics the common statistical technique of kernel density estimation, using tree distance metrics to define kernels. Our method demonstrates superior accuracy in outlier detection on simulated data, when compared to a previously published method. We also applied our method to a data set of apicomplexa, identifying a set of putative outlier genes. Our method for estimating tree distributions is implemented as an R package.


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