Museum of Natural Science, Louisiana State University, Baton Rouge, LA 70803, United States.
Mol Phylogenet Evol. 2013 Feb;66(2):526-38. doi: 10.1016/j.ympev.2011.12.007. Epub 2011 Dec 14.
This is a time of unprecedented transition in DNA sequencing technologies. Next-generation sequencing (NGS) clearly holds promise for fast and cost-effective generation of multilocus sequence data for phylogeography and phylogenetics. However, the focus on non-model organisms, in addition to uncertainty about which sample preparation methods and analyses are appropriate for different research questions and evolutionary timescales, have contributed to a lag in the application of NGS to these fields. Here, we outline some of the major obstacles specific to the application of NGS to phylogeography and phylogenetics, including the focus on non-model organisms, the necessity of obtaining orthologous loci in a cost-effective manner, and the predominate use of gene trees in these fields. We describe the most promising methods of sample preparation that address these challenges. Methods that reduce the genome by restriction digest and manual size selection are most appropriate for studies at the intraspecific level, whereas methods that target specific genomic regions (i.e., target enrichment or sequence capture) have wider applicability from the population level to deep-level phylogenomics. Additionally, we give an overview of how to analyze NGS data to arrive at data sets applicable to the standard toolkit of phylogeography and phylogenetics, including initial data processing to alignment and genotype calling (both SNPs and loci involving many SNPs). Even though whole-genome sequencing is likely to become affordable rather soon, because phylogeography and phylogenetics rely on analysis of hundreds of individuals in many cases, methods that reduce the genome to a subset of loci should remain more cost-effective for some time to come.
这是 DNA 测序技术前所未有的转型时期。下一代测序（NGS）显然有望快速、经济有效地生成用于地理遗传学和系统发育学的多位点序列数据。然而，由于重点关注非模式生物，以及不确定哪些样品制备方法和分析适用于不同的研究问题和进化时间尺度，导致 NGS 在这些领域的应用滞后。在这里，我们概述了 NGS 在地理遗传学和系统发育学中应用的一些主要障碍，包括关注非模式生物、以经济有效的方式获得同源基因座的必要性，以及这些领域中基因树的主导地位。我们描述了最有前途的解决这些挑战的样品制备方法。通过限制性内切酶消化和手动大小选择来减少基因组的方法最适合于种内水平的研究，而靶向特定基因组区域的方法（即目标富集或序列捕获）从种群水平到深层系统发育基因组学具有更广泛的适用性。此外，我们概述了如何分析 NGS 数据，以得出适用于地理遗传学和系统发育学标准工具包的数据集，包括从初始数据处理到对齐和基因型调用（包括单核苷酸多态性和涉及多个单核苷酸多态性的基因座）。尽管全基因组测序可能很快就会变得负担得起，但由于地理遗传学和系统发育学通常需要分析数百个个体，因此在一段时间内，将基因组减少到少数基因座的方法仍将更具成本效益。
