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Some methods implementing approximate ML measures, although less accurate, are less resource-intensive.However, the sensitivity of alignment-free methods to different evolutionary scenarios, and the scalability of these methods, have not been systematically investigated.Because the phylogenetic tree is known for each simulated sequence set, we use that as the reference.We simulated nucleotide sequence sets of various size categories N = 8, 32 and 128 (total length, L = 1500 nt).Across all N examined here, performed slightly worse than MSA for T3 and T5, e.g.at k = 8, Q methods between the two sequence types across N = 8, 32 and 128 pooled from all six trees, as shown in Supplementary Fig. For nucleotide sequences, k = 8 yielded the lowest RF distances, with RF = 0 at N = 8 and 32, and RF .Alignment-free methods, in which shared properties of sub-sequences (e.g.

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We demonstrate the scalability and potential of using alignment-free approaches to compute phylogenetic trees quickly and accurately from large-scale DNA or protein data.. Each of these metrics is described in the Supplementary Note.For each method, we transform the scores via logarithmic representation of the geometric mean to estimate evolutionary distances (see Methods).Each resulting distance matrix was then used to calculate phylogenetic relationships using NJ.For each category, six sequence sets were simulated under an unrooted tree topology across distinct situations of relative branch lengths, with α = 1 in an 8-category discrete gamma distribution. 1; shown for 8-taxon trees) represents a fine-scale scenario of sequence divergence, as determined by different combinations of internal (x) and terminal (y) branch lengths.In some simulations, we recognise two subsets of y (y as shown in T2, T3, T5 and T6; these are the reference trees for the corresponding sequence sets.

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