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.