选择(遗传算法)
群体基因组学
自然选择
人口历史
适应(眼睛)
人口
数据科学
生物
混乱
基因组学
基因组
进化生物学
上市(财务)
群体遗传学
计算机科学
生态学
遗传变异
人工智能
遗传学
人口学
神经科学
心理学
经济
精神分析
社会学
基因
财务
作者
Yann Bourgeois,Hazzouri Km,Ben Warren
摘要
1. Characterizing species history and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The ongoing and constant improvement of next-generation sequencing (NGS) techniques has facilitated the production of an ever-increasing number of genetic markers across genomes of non-model species. 2. The study of variation in these markers across natural populations has deepened the understanding of how population history and selection act on genomes. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a burst of analytical tools that can confuse users. 3. Such confusion can limit the amount of information effectively retrieved from complex genomic datasets. In addition, the lack of a unified analytical pipeline impairs the diffusion of the most recent analytical tools into fields like conservation biology. 4. To address this need, we describe possible analytical protocols and link these with more than 70 methods dealing with genome-scale datasets. We summarise the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.
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