Pérez-Losada Marcos, Porter Megan L, Tazi Loubna, Crandall Keith A
Department of Integrative Biology, 157 Widtsoe Building, Brigham Young University, Provo, UT 84602, USA.
Infect Genet Evol. 2007 Jan;7(1):24-43. doi: 10.1016/j.meegid.2006.03.004. Epub 2006 Apr 19.
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.
高通量测序成本的降低、自动化程度的提高以及序列数据在进化分析中的适用性，使得DNA数据（或相应的氨基酸序列）成为研究微生物群体遗传学和系统发育学的首选分子标记。与此同时，由于计算能力的不断提高，新的、更准确（有时更快）的基于序列的分析方法正在被开发并应用于这些新数据。在这里，我们回顾一些常用的、最近改进的以及新开发的方法，这些方法用于利用核苷酸和氨基酸序列数据推断群体动态和进化关系，包括：序列比对、模型选择、二叉树和网络系统发育方法，以及估计群体历史、群体结构和群体参数（重组、遗传多样性、增长和自然选择）的方法。由于关于这些主题已发表了大量文献，本综述在范围上无法做到全面。相反，对于所讨论的所有方法，我们介绍我们认为对微生物序列分析特别有用的方法，并在可能的情况下，提供指向近期更全面综述的参考文献。
