Abstract
Alignment-free (AF) approaches have recently been highlighted as alternatives to methods based on multiple sequence alignment in phylogenetic inference. However, the sensitivity of AF methods to genome-scale evolutionary scenarios is little known. Here, using simulated microbial genome data we systematically assess the sensitivity of nine AF methods to three important evolutionary scenarios: sequence divergence, lateral genetic transfer (LGT) and genome rearrangement. Among these, AF methods are most sensitive to the extent of sequence divergence, less sensitive to low and moderate frequencies of LGT, and most robust against genome rearrangement. We describe the application of AF methods to three well-studied empirical genome datasets, and introduce a new application of the jackknife to assess node support. Our results demonstrate that AF phylogenomics is computationally scalable to multi-genome data and can generate biologically meaningful phylogenies and insights into microbial evolution.
Highlights
May not be required to match exactly
We independently assessed the sensitivity of each AF approach to evolutionary scenarios of genome divergence, lateral genetic transfer (LGT) and genome rearrangement
In this study we demonstrate that AF phylogenetic approaches can be used to quickly and accurately infer phylogenomic relationships of microbes using whole-genome data
Summary
May not be required to match exactly. In general, match-length methods perform well in the comparison of highly similar sequences, due to the large proportion of exact matches. Bootstrap and subsampling techniques have been proposed in recent studies[21,22,23], but most studies of AF approaches focused only on topologies, with no or little emphasis on node support. Using both simulated and empirical data we systematically assess the sensitivity of nine existing AF methods to genome-scale evolutionary scenarios involving sequence divergence, LGT and rearrangement. We introduce a new application of the jackknife[24] technique to provide node-support values to trees inferred by AF approaches, and demonstrate the scalability and potential of AF approaches in inferring phylogenetic trees quickly and accurately from genome-scale data
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