Estimating evolutionary distances between genomic sequences from spaced-word matches.

Burkhard Morgenstern,Sebastian Horwege,Bingyao Zhu,Chris André Leimeister

doi:10.1186/s13015-015-0032-x

Burkhard Morgenstern, Sebastian Horwege + Show 2 more

Open Access

https://doi.org/10.1186/s13015-015-0032-x

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Abstract

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/.

Highlights

Alignment-free methods are increasingly used for DNA and protein sequence comparison since they are much faster than traditional alignment-based approaches [1]
In this paper, we proposed a new estimator dN for the evolutionary distance between two DNA sequences that is based on the number N of spaced-word matches between them
While most alignment-free methods use ad-hoc distance measures, the distance function that we defined is based on a probabilistic model of evolution and seems to be a good estimator for the number of substitutions per site that have occurred since two sequences have evolved separately

Summary

Introduction

Alignment-free methods are increasingly used for DNA and protein sequence comparison since they are much faster than traditional alignment-based approaches [1]. We propose an alternative estimator of the evolutionary distance between two sequences in terms of the number N of spaced-word matches between them.

Results

Conclusion