Estimating Evolutionary Distances from Spaced-Word Matches

Morgenstern, Burkhard; Zhu, Binyao; Horwege, Sebastian; Leimeister, Chris-André

doi:10.1007/978-3-662-44753-6_13

Cited by 1 publication

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“…This paper is an extended version of a manuscript that was first published in the proceedings of Workshop on Algorithms in Bioinformatics (WABI) 2013 in Wroclaw, Poland [ 41 ]. We added two extensions to our WABI paper that are crucial if our method is to be applied to real-world genomic sequences.…”

Section: Introductionmentioning

confidence: 99%

Estimating evolutionary distances between genomic sequences from spaced-word matches

Morgenstern

Zhu

Horwege

et al. 2015

Algorithms Mol Biol

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Alignment-free methods are increasingly used to calculate evolutionary distances between DNA and protein sequences as a basis of phylogeny reconstruction. Most of these methods, however, use heuristic distance functions that are not based on any explicit model of molecular evolution. Herein, we propose a simple estimator dN of the evolutionary distance between two DNA sequences that is calculated from the number N of (spaced) word matches between them. We show that this distance function is more accurate than other distance measures that are used by alignment-free methods. In addition, we calculate the variance of the normalized number N of (spaced) word matches. We show that the variance of N is smaller for spaced words than for contiguous words, and that the variance is further reduced if our spaced-words approach is used with multiple patterns of ‘match positions’ and ‘don’t care positions’. Our software is available online and as downloadable source code at: http://spaced.gobics.de/.

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Section: Introductionmentioning

confidence: 99%