New methods for inferring population dynamics from microbial sequences.

Marcos Pérez-Losada, Megan L Porter, Loubna Tazi, Keith A Crandall
Author Information
  1. Marcos Pérez-Losada: Department of Integrative Biology, 157 Widtsoe Building, Brigham Young University, Provo, UT 84602, USA. mp323@byu.edu

Abstract

The reduced cost of high throughput sequencing, increasing automation, and the amenability of sequence data for evolutionary analysis are making DNA data (or the corresponding amino acid sequences) the molecular marker of choice for studying microbial population genetics and phylogenetics. Concomitantly, due to the ever-increasing computational power, new, more accurate (and sometimes faster), sequence-based analytical approaches are being developed and applied to these new data. Here we review some commonly used, recently improved, and newly developed methodologies for inferring population dynamics and evolutionary relationships using nucleotide and amino acid sequence data, including: alignment, model selection, bifurcating and network phylogenetic approaches, and methods for estimating demographic history, population structure, and population parameters (recombination, genetic diversity, growth, and natural selection). Because of the extensive literature published on these topics this review cannot be comprehensive in its scope. Instead, for all the methods discussed we introduce the approaches we think are particularly useful for analyses of microbial sequences and where possible, include references to recent and more inclusive reviews.

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Grants

  1. R01 GM066276-01/NIGMS NIH HHS
  2. R01 GM066276-02/NIGMS NIH HHS
  3. R01 AI050217/NIAID NIH HHS
  4. R01 GM066276-03/NIGMS NIH HHS
  5. R01AI50217/NIAID NIH HHS
  6. GM66276/NIGMS NIH HHS
  7. R01 GM066276/NIGMS NIH HHS
  8. R01 GM066276-04/NIGMS NIH HHS

MeSH Term

Bacteria
Genetic Variation
Genomics
Phylogeny
Sequence Analysis, DNA

Word Cloud

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