4.7 Article

Measurably evolving pathogens in the genomic era

期刊

TRENDS IN ECOLOGY & EVOLUTION
卷 30, 期 6, 页码 306-313

出版社

ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tree.2015.03.009

关键词

bacteria; DNA virus; epidemiological models; evolutionary rate; infectious disease; phylodynamics

资金

  1. RAPIDD program of the Science and Technology Directorate of the US Department of Homeland Security
  2. National Institutes of Health (NIH) Fogarty International Center
  3. NIH [RO1 AI047498]
  4. Biotechnology and Biological Sciences Research Council (BBSRC) [BB/L010569/1]
  5. NSF [EF-0928690, OCE-1335657]
  6. European Research Council under the European Commission [614725-PATHPHYLODYN]
  7. National Institute for Health Research (NIHR) Health Protection Research Unit
  8. Directorate For Geosciences [1335657] Funding Source: National Science Foundation
  9. Division Of Ocean Sciences [1335657] Funding Source: National Science Foundation
  10. Biotechnology and Biological Sciences Research Council [BB/L010569/1, BB/L023458/1] Funding Source: researchfish
  11. Medical Research Council [MR/K010174/1B, MR/K010174/1] Funding Source: researchfish
  12. National Institute for Health Research [HPRU-2012-10080] Funding Source: researchfish
  13. BBSRC [BB/L010569/1, BB/L023458/1] Funding Source: UKRI
  14. MRC [MR/K010174/1] Funding Source: UKRI

向作者/读者索取更多资源

Current sequencing technologies have created unprecedented opportunities for studying microbial populations. For pathogens with comparatively low per-site mutation rates, such as DNA viruses and bacteria, whole-genome sequencing can reveal the accumulation of novel genetic variation between population samples taken at different times. The concept of 'measurably evolving populations' and related analytical approaches have provided powerful insights for fast-evolving RNA viruses, but their application to other pathogens is still in its infancy. We argue that previous distinctions between slow- and fast-evolving pathogens become blurred once evolution is assessed at a genome-wide scale, and we highlight important analytical challenges to be overcome to infer pathogen population dynamics from genomic data.

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