Abstract

Abstract Background The neighbor-joining method by Saitou and Nei is a widely used method for constructing phylogenetic trees. The formulation of the method gives rise to a canonical Θ( n 3 ) algorithm upon which all existing implementations are based. Results In this paper we present techniques for speeding up the canonical neighbor-joining method. Our algorithms construct the same phylogenetic trees as the canonical neighbor-joining method. The best-case running time of our algorithms are O ( n 2 ) but the worst-case remains O ( n 3 ). We empirically evaluate the performance of our algoritms on distance matrices obtained from the Pfam collection of alignments. The experiments indicate that the running time of our algorithms evolve as Θ( n 2 ) on the examined instance collection. We also compare the running time with that of the QuickTree tool, a widely used efficient implementation of the canonical neighbor-joining method. Conclusion The experiments show that our algorithms also yield a significant speed-up, already for medium sized instances.

Keywords

Computer sciencePhylogenetic treeImplementationConstruct (python library)k-nearest neighbors algorithmAlgorithmRunning timeData miningTheoretical computer scienceArtificial intelligenceBiology

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Publication Info

Year
2006
Type
article
Volume
7
Issue
1
Pages
29-29
Citations
59
Access
Closed

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Thomas Mailund, Gerth Stølting Brodal, Rolf Fagerberg et al. (2006). Recrafting the neighbor-joining method. BMC Bioinformatics , 7 (1) , 29-29. https://doi.org/10.1186/1471-2105-7-29

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DOI
10.1186/1471-2105-7-29