Alignment-free microbial phylogenomics under scenarios of sequence divergence, genome rearrangement and lateral genetic transfer

By Guillaume Bernard, C. Chan, M. Ragan. 2016

63 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology, Plant Science

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [33], clustering [81], motifs [82, 83], and prediction methods [72, 76, 84 – 88].”
Likelihood Ratios for Out-of-Distribution Detection (2019, background): “…distance is calculated based on the similarity between the normalized nucleotide word frequencies ( k -tuples) of the two genomes, and studies have shown that this genetic distance reﬂects true evolutionary distances between genomes (Chan et al., 2014; Bernard et al., 2016; Lu et al., 2017).”
Alignment-free sequence comparison: benefits, applications, and tools (2017, background): “…in a favorable position — as alignment-free methods do not rely on dynamic programming, they are computationally less expensive (as they are generally of a linear complexity depending only on the length of the query sequence [32]) and therefore suitable for whole genome comparisons [33 – 36].”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “This concept resembles 24 that of using k-mers for database searches [33], clustering [74], motifs [75, 76], and prediction 25 methods [66, 70, 77-81].”
Exploring the Limits of Out-of-Distribution Detection (2021, background): “Studies have shown that this genetic distance reﬂects true evolutionary distances [Chan et al., 2014, Bernard et al., 2016].”