Towards distance-based phylogenetic inference in average-case linear-time

Maxime Crochemore 1 Alexandre Francisco Solon Pissis 2 Cátia Vaz
1 Informatics
2 Department of Informatics [King's College London]
Abstract : Computing genetic evolution distances among a set of taxa dominates the running time of many phylogenetic inference methods. Most of genetic evolution distance definitions rely, even if indirectly , on computing the pairwise Hamming distance among sequences or profiles. We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given distance threshold. This paper includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a widely used phylogenetic inference method.
Keywords : F22 Nonnumerical Algorithms and computational biology phylogenetic inference Hamming distance
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Communication dans un congrès
International Workshop on Algorithms in Bioinformatics, Aug 2017, Boston, MA, United States. 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). 〈10.4230/LIPIcs.WABI.2017.00〉
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Maxime Crochemore, Alexandre Francisco, Solon Pissis, Cátia Vaz. Towards distance-based phylogenetic inference in average-case linear-time. International Workshop on Algorithms in Bioinformatics, Aug 2017, Boston, MA, United States. 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). 〈10.4230/LIPIcs.WABI.2017.00〉. 〈hal-01616499〉

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