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植物系统发育基因组学的实际考量

Practical considerations for plant phylogenomics.

作者信息

McKain Michael R, Johnson Matthew G, Uribe-Convers Simon, Eaton Deren, Yang Ya

机构信息

Department of Biological Sciences The University of Alabama Box 870344 Tuscaloosa Alabama 35487 USA.

Department of Biological Sciences Texas Tech University 2901 Main Street, Box 43131 Lubbock Texas 79409 USA.

出版信息

Appl Plant Sci. 2018 Apr 2;6(3):e1038. doi: 10.1002/aps3.1038. eCollection 2018 Mar.

Abstract

The past decade has seen a major breakthrough in our ability to easily and inexpensively sequence genome-scale data from diverse lineages. The development of high-throughput sequencing and long-read technologies has ushered in the era of phylogenomics, where hundreds to thousands of nuclear genes and whole organellar genomes are routinely used to reconstruct evolutionary relationships. As a result, understanding which options are best suited for a particular set of questions can be difficult, especially for those just starting in the field. Here, we review the most recent advances in plant phylogenomic methods and make recommendations for project-dependent best practices and considerations. We focus on the costs and benefits of different approaches in regard to the information they provide researchers and the questions they can address. We also highlight unique challenges and opportunities in plant systems, such as polyploidy, reticulate evolution, and the use of herbarium materials, identifying optimal methodologies for each. Finally, we draw attention to lingering challenges in the field of plant phylogenomics, such as reusability of data sets, and look at some up-and-coming technologies that may help propel the field even further.

摘要

在过去十年中,我们在轻松且低成本地对来自不同谱系的基因组规模数据进行测序的能力方面取得了重大突破。高通量测序和长读长技术的发展开启了系统发育基因组学时代,在这个时代,成百上千个核基因和整个细胞器基因组被常规用于重建进化关系。因此,要理解哪些方法最适合特定的一系列问题可能很困难,尤其是对于那些刚进入该领域的人来说。在这里,我们回顾了植物系统发育基因组学方法的最新进展,并针对依赖项目的最佳实践和注意事项提出建议。我们关注不同方法在为研究人员提供的信息以及它们能够解决的问题方面的成本和效益。我们还强调了植物系统中的独特挑战和机遇,例如多倍体、网状进化以及植物标本材料的使用,为每种情况确定最佳方法。最后,我们提请注意植物系统发育基因组学领域中仍然存在的挑战,例如数据集的可重用性,并探讨一些可能有助于推动该领域进一步发展的新兴技术。

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