An Experimental Analysis of Consensus Tree Algorithms for Large-Scale Tree Collections

Consensus trees are a popular approach for summarizing the shared evolutionary relationships in a collection of trees. Many popular techniques such as Bayesian analyses produce results that can contain tens of thousands of trees to summarize. We develop a fast consensus algorithm called HashCS to construct large-scale consensus trees. We perform an extensive empirical study for comparing the performance of several consensus tree algorithms implemented in widely-used, phylogenetic software such as PAUP* and MrBayes. Our collections of biological and artificial trees range from 128 to 16,384 trees on 128 to 1,024 taxa. Experimental results show that our HashCS approach is up to 100 times faster than MrBayes and tip to 9 times faster than PAUP*. Fast consensus algorithms such as HashCS can be used in a variety of ways, such as in real-time to detect whether a phylogenetic search has converged.