TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees

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 and 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.

Methods
All the raw data are obtained from other publications as shown below. We further analyzed the data and provide the results of the analyses here. The methods used to analyze the data are described in the paper.

Dataset

Species

Genes

Download

Plants 

104

852

DOI 10.1186/2047-217X-3-17

Mammals

37

424

DOI 10.13012/C5BG2KWG

Insects

144

1478

http://esayyari.github.io/InsectsData

Cannon

78

213

DOI 10.5061/dryad.493b7

Rouse 

26

393

DOI 10.5061/dryad.79dq1

Frogs

164

95

DOI 10.5061/dryad.12546.2