Leroy Thibault, Rougemont Quentin
Montpellier Institute of Evolutionary Sciences (ISEM), Université de Montpellier, Montpellier, France.
Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria.
Methods Mol Biol. 2021;2222:287-324. doi: 10.1007/978-1-0716-0997-2_16.
High-throughput sequencing technologies have provided an unprecedented opportunity to study the different evolutionary forces that have shaped present-day patterns of genetic diversity, with important implications for many directions in plant biology research. To manage such massive quantities of sequencing data, biologists, however, need new additional skills in informatics and statistics. In this chapter, our objective is to introduce population genomics methods to beginners following a learning-by-doing strategy in order to help the reader to analyze the sequencing data by themselves. Conducted analyses cover several main areas of evolutionary biology, such as an initial description of the evolutionary history of a given species or the identification of genes targeted by natural or artificial selection. In addition to the practical advices, we performed re-analyses of two cases studies with different kind of data: a domesticated cereal (African rice) and a non-domesticated tree species (sessile oak). All the code needed to replicate this work is publicly available on github ( https://github.com/ThibaultLeroyFr/Intro2PopGenomics/ ).
高通量测序技术为研究塑造当今遗传多样性模式的不同进化力量提供了前所未有的机会,这对植物生物学研究的许多方向都具有重要意义。然而,为了管理如此大量的测序数据,生物学家需要掌握信息学和统计学方面的新技能。在本章中,我们的目标是通过实践学习策略向初学者介绍群体基因组学方法,以帮助读者自行分析测序数据。所进行的分析涵盖了进化生物学的几个主要领域,例如对给定物种进化历史的初步描述,或对自然选择或人工选择靶向基因的鉴定。除了实用建议外,我们还对两个不同类型数据的案例研究进行了重新分析:一种驯化谷物(非洲水稻)和一种非驯化树种(无梗花栎)。复制这项工作所需的所有代码都可在github(https://github.com/ThibaultLeroyFr/Intro2PopGenomics/ )上公开获取。