Abstract
We present the QuickQuartet algorithm for computing the all-to-all quartet distance for large evolutionary tree collections. By leveraging the relationship between bipartitions and quartets, our approach significantly improves upon the performance of existing quartet distance algorithms. To explore QuickQuartet’s performance, sets of biological data containing 20,000 and 33,306 trees over 150 taxa and 567 taxa, respectively are analyzed. Experimental results show that QuickQuartet is up to 100 times faster than existing methods. With the availability of QuickQuartet, the use of quartet distance as a tool for analysis of evolutionary relationships becomes a practical tool for biologists to use in order to gain new insights regarding their large tree collections.
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Crosby, R.W., Williams, T.L. (2012). A Fast Algorithm for Computing the Quartet Distance for Large Sets of Evolutionary Trees. In: Bleris, L., Măndoiu, I., Schwartz, R., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2012. Lecture Notes in Computer Science(), vol 7292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30191-9_6
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DOI: https://doi.org/10.1007/978-3-642-30191-9_6
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