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Recapitulating phylogenies using k-mers: from trees to networks

Identifieur interne : 001046 ( Main/Exploration ); précédent : 001045; suivant : 001047

Recapitulating phylogenies using k-mers: from trees to networks

Auteurs : Guillaume Bernard [Australie] ; Mark A. Ragan [Australie] ; Cheong Xin Chan [Australie]

Source :

RBID : PMC:5224691

Abstract

Ernst Haeckel based his landmark Tree of Life on the supposed ontogenic recapitulation of phylogeny, i.e. that successive embryonic stages during the development of an organism re-trace the morphological forms of its ancestors over the course of evolution. Much of this idea has since been discredited. Today, phylogenies are often based on families of molecular sequences. The standard approach starts with a multiple sequence alignment, in which the sequences are arranged relative to each other in a way that maximises a measure of similarity position-by-position along their entire length. A tree (or sometimes a network) is then inferred. Rigorous multiple sequence alignment is computationally demanding, and evolutionary processes that shape the genomes of many microbes (bacteria, archaea and some morphologically simple eukaryotes) can add further complications. In particular, recombination, genome rearrangement and lateral genetic transfer undermine the assumptions that underlie multiple sequence alignment, and imply that a tree-like structure may be too simplistic. Here, using genome sequences of 143 bacterial and archaeal genomes, we construct a network of phylogenetic relatedness based on the number of shared k-mers (subsequences at fixed length k). Our findings suggest that the network captures not only key aspects of microbial genome evolution as inferred from a tree, but also features that are not treelike. The method is highly scalable, allowing for investigation of genome evolution across a large number of genomes. Instead of using specific regions or sequences from genome sequences, or indeed Haeckel’s idea of ontogeny, we argue that genome phylogenies can be inferred using k-mers from whole-genome sequences. Representing these networks dynamically allows biological questions of interest to be formulated and addressed quickly and in a visually intuitive manner.


Url:
DOI: 10.12688/f1000research.10225.2
PubMed: 28105314
PubMed Central: 5224691


Affiliations:


Links toward previous steps (curation, corpus...)


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