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Practically all methods for phylogeny reconstruction are based on multiple alignments.
In this work, we have explored the limitations of LS methods for phylogeny testing.
In this study, we used distance methods, Bayesian methods and parsimony methods for phylogeny reconstruction.
Thus, the full range of methods for phylogeny reconstruction, including Maximum Parsimony and Maximum Likelihood, are available, which could also be combined with bootstrapping (as implemented in, for example, PAUP* [ 32]) to obtain branch support values (e.g., [ 54, 61]).
They have been studied before to describe properties of prokaryotic and eukaryotic genomes and to develop methods for phylogeny construction or PCR primer design (Chairungsee and Crochemore, 2012; Falda et al., 2014; Garcia et al., 2011; Herold et al., 2008; Pinho et al., 2009; Wu et al., 2010).
Maximum likelihood [ 20] and Bayesian [ 21] methods for phylogeny inference tend to allow for more realistic and detailed mutational models and to more accurately account for uncertainty in inferences than do MP methods, but at the cost of generally even greater computational time.
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Methods: We present DACTAL, a method for phylogeny estimation that produces trees from unaligned sequence datasets without ever needing to estimate an alignment on the entire dataset.
All three methods used for phylogeny reconstructions produced the same topology with strong support values (Figure 2).
Because species are not independent due to shared ancestry, we performed our analyses using methods accounting for phylogeny [ 43, 44].
Results: We have developed a method for phylogeny-aware alignment of partial-order sequence graphs and apply it here to the extension of alignments with new data.
Here, we outline a new general-purpose method for phylogeny-aware alignment of sequence graphs and apply it to phylogenetic extension of existing alignments.
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