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arXiv · 1302.6422

kdetrees: Nonparametric Estimation of Phylogenetic Tree Distributions

Grady Weyenberg·Peter Huggins·Christopher Schardl·Daniel K Howe·Ruriko Yoshida
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Abstract

Motivation: 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 and subsequent neofunctionalization) will cause some genes to exhibit a history quite distinct from those 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. Results: We propose and implement KDETREES, 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 method compares favorably with a similar recently-published method, featuring an improvement of one polynomial order of computational complexity (to quadratic in the number of trees analyzed), with simulation studies suggesting only a small penalty to classification accuracy. Application of KDETREES to a set of Apicomplexa genes identified several unreliable sequence alignments which had escaped previous detection, as well as a gene independently reported as a possible case of horizontal gene transfer. We also analyze a set of Epichloe genes, fungi symbiotic with grasses, successfully identifying a contrived instance of paralogy. Availability: Our method for estimating tree distributions and identifying outlying trees is implemented as the R package KDETREES, and is available for download from CRAN.

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Grady Weyenberg, Peter Huggins, Christopher Schardl, Daniel K Howe, Ruriko Yoshida. 2013-02-26. kdetrees: Nonparametric Estimation of Phylogenetic Tree Distributions. https://arxiv.org/abs/1302.6422

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