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TreeShrink: Efficient Detection of Outlier Tree Leaves

  • Uyen Mai
  • Siavash MirarabEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10562)

Abstract

Phylogenetic trees include errors for a variety of reasons. We argue that one way to detect errors is to build a phylogeny with all the data then detect taxa that artificially inflate the tree diameter. We formulate an optimization problem that seeks to find k leaves that can be removed to reduce the tree diameter maximally. We present a polynomial time solution to this “k-shrink” problem. Given this solution, we then use non-parametric statistics to find an outlier set of taxa that have an unexpectedly high impact on the tree diameter. We test our method, TreeShrink, on five biological datasets, and show that it is more conservative than rogue taxon removal using RogueNaRok. When the amount of filtering is controlled, TreeShrink outperforms RogueNaRok in three out of the five datasets, and they tie in another dataset.

Keywords

Tree diameter Rogue taxon removal Gene tree discordance 

Notes

Acknowledgments

This work was supported by the NSF grant IIS-1565862 to SM and UM. Computations were performed on the San Diego Supercomputer Center (SDSC) through XSEDE allocations, which is supported by the NSF grant ACI-1053575.

Supplementary material

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Science and EngineeringUniversity of California at San DiegoSan DiegoUSA
  2. 2.Electrical and Computer EngineeringUniversity of California at San DiegoSan DiegoUSA

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