A Sublinear-Time Randomized Approximation Scheme for the Robinson-Foulds Metric

Pattengale, Nicholas D.; Moret, Bernard M. E.

doi:10.1007/11732990_19

Cited by 5 publications

(1 citation statement)

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“…Notable exceptions include PAUP* 15 , Phylip 6 , and Split-Dist 9a . Pattengale and Moret provide an approximation algorithm 10 , which provides with high probability a (1 + ) approximation of the true RF distance matrix. Given that Pattengale and Moret's approach provides an approximation of the RF distance, we do not compare our approach to their algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Randomized Algorithm for Comparing Sets of Phylogenetic Trees

Sul

Williams

2007

Proceedings of the 5th Asia-Pacific Bioinformatics Conference

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Phylogenetic analysis often produce a large number of candidate evolutionary trees, each a hypothesis of the "true" tree. Post-processing techniques such as strict consensus trees are widely used to summarize the evolutionary relationships into a single tree. However, valuable information is lost during the summarization process. A more elementary step is to produce estimates of the topological differences that exist among all pairs of trees. We design a new randomized algorithm, called Hash-RF, that computes the all-to-all Robinson-Foulds (RF) distance-the most common distance metric for comparing two phylogenetic trees. Our approach uses a hash table to organize the bipartitions of a tree, and a universal hashing function makes our algorithm randomized. We compare the performance of our Hash-RF algorithm to PAUP*'s implementation of computing the all-to-all RF distance matrix. Our experiments focus on the algorithmic performance of comparing sets of biological trees, where the size of each tree ranged from 500 to 2,000 taxa and the collection of trees varied from 200 to 1,000 trees. Our experimental results clearly show that our Hash-RF algorithm is up to 500 times faster than PAUP*'s approach. Thus, Hash-RF provides an efficient alternative to a single tree summary of a collection of trees and potentially gives researchers the ability to explore their data in new and interesting ways.

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Section: Introductionmentioning

confidence: 99%