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Universal Pacemaker of Genome Evolution

Overview of attention for article published in PLoS Computational Biology, November 2012
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Title
Universal Pacemaker of Genome Evolution
Published in
PLoS Computational Biology, November 2012
DOI 10.1371/journal.pcbi.1002785
Pubmed ID
Authors

Sagi Snir, Yuri I. Wolf, Eugene V. Koonin

Abstract

A fundamental observation of comparative genomics is that the distribution of evolution rates across the complete sets of orthologous genes in pairs of related genomes remains virtually unchanged throughout the evolution of life, from bacteria to mammals. The most straightforward explanation for the conservation of this distribution appears to be that the relative evolution rates of all genes remain nearly constant, or in other words, that evolutionary rates of different genes are strongly correlated within each evolving genome. This correlation could be explained by a model that we denoted Universal PaceMaker (UPM) of genome evolution. The UPM model posits that the rate of evolution changes synchronously across genome-wide sets of genes in all evolving lineages. Alternatively, however, the correlation between the evolutionary rates of genes could be a simple consequence of molecular clock (MC). We sought to differentiate between the MC and UPM models by fitting thousands of phylogenetic trees for bacterial and archaeal genes to supertrees that reflect the dominant trend of vertical descent in the evolution of archaea and bacteria and that were constrained according to the two models. The goodness of fit for the UPM model was better than the fit for the MC model, with overwhelming statistical significance, although similarly to the MC, the UPM is strongly overdispersed. Thus, the results of this analysis reveal a universal, genome-wide pacemaker of evolution that could have been in operation throughout the history of life.

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Geographical breakdown

Country Count As %
Spain 2 2%
United States 2 2%
Hong Kong 1 1%
Australia 1 1%
United Kingdom 1 1%
France 1 1%
Japan 1 1%
Israel 1 1%
Unknown 87 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 26%
Researcher 25 26%
Professor 10 10%
Student > Master 9 9%
Student > Bachelor 5 5%
Other 17 18%
Unknown 6 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 69%
Biochemistry, Genetics and Molecular Biology 15 15%
Computer Science 3 3%
Environmental Science 2 2%
Immunology and Microbiology 2 2%
Other 2 2%
Unknown 6 6%