Abstract
A shortcoming of most correlation distance methods based on the composition vectors without alignment developed for phylogenetic analysis using complete genomes is that the “distances” are not proper distance metrics in the strict mathematical sense. In this paper we propose two new correlation-related distance metrics to replace the old one in our dynamical language approach. Four genome datasets are employed to evaluate the effects of this replacement from a biological point of view. We find that the two proper distance metrics yield trees with the same or similar topologies as/to those using the old “distance” and agree with the tree of life based on 16S rRNA in a majority of the basic branches. Hence the two proper correlation-related distance metrics proposed here improve our dynamical language approach for phylogenetic analysis.
Highlights
Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships [1]
The analyses based on the Markov model and dynamical language model without sequence alignment using 103 prokaryotes and 6 eukaryotes have yielded trees separating the three domains of life, Archaea, Eubacteria and Eukarya, with the relationships among the taxa consistent with those based on traditional analyses [5,11]
We evaluate the effects of this replacement on the analysis of a wide range of complete genomes from the biological point of view
Summary
Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships [1]. The analyses based on the Markov model and dynamical language model without sequence alignment using 103 prokaryotes and 6 eukaryotes have yielded trees separating the three domains of life, Archaea, Eubacteria and Eukarya, with the relationships among the taxa consistent with those based on traditional analyses [5,11]. These two methods were used to analyze the complete chloroplast genomes [5,12]. We evaluate the effects of this replacement on the analysis of a wide range of complete genomes from the biological point of view
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