Organellar DNA continues to provide a rich source of information in the genomics era

Organellar DNA continues to provide a rich source of information in the genomics era
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细胞器 DNA 继续为基因组学时代提供丰富的信息来源

DOI:
10.1111/mec.16872
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发表时间:
2023
期刊:
影响因子:
4.9
通讯作者:
Blair, Christopher
Blair, Christopher
中科院分区:
生物学1区
文献类型:
--
作者:
Blair, Christopher

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基因组学革命继续改变生态学家和进化生物学家研究生物多样性进化和维持的方式。现在比以往任何时候都更容易生成由数百到数千个独立进化的核位点组成的大型分子数据集,以估计一套进化和人口参数。然而,任何推论将是不完整或不准确的,如果不正确的分类身份和延续整个分析管道。由于几十年的研究和全面的在线数据库,线粒体DNA (mtDNA),叶绿体DNA (cpDNA)和选择核基因的测序和分析可以为研究人员提供一种成本有效和简单的方法,在随后的系统地理和群体基因组分析之前验证样品的物种身份。将这些序列添加到基因组研究中还可以揭示其他重要的进化问题,如基因树-物种树不一致、物种限制、性别偏倚的扩散模式、适应和mtDNA渗入的解释。尽管mtDNA和cpDNA基因组由于其众所周知的局限性,通常不应该专门用于历史推断,但将这些数据添加到现代基因组研究中只会增加很少的成本和努力,同时提供丰富的有用数据,可以对基础研究和应用研究产生重大影响。
The genomics revolution continues to change how ecologists and evolutionary biologists study the evolution and maintenance of biodiversity. It is now easier than ever to generate large molecular data sets consisting of hundreds to thousands of independently evolving nuclear loci to estimate a suite of evolutionary and demographic parameters. However, any inferences will be incomplete or inaccurate if incorrect taxonomic identities and perpetuated throughout the analytical pipeline. Due to decades of research and comprehensive online databases, sequencing and analysis of mitochondrial DNA (mtDNA), chloroplast DNA (cpDNA) and select nuclear genes can provide researchers with a cost effective and simple means to verify the species identity of samples prior to subsequent phylogeographic and population genomic analysis. The addition of these sequences to genomic studies can also shed light on other important evolutionary questions such as explanations for gene tree‐species tree discordance, species limits, sex‐biased dispersal patterns, adaptation, and mtDNA introgression. Although the mtDNA and cpDNA genomes often should not be used exclusively to make historical inferences given their well‐known limitations, the addition of these data to modern genomic studies adds little cost and effort while simultaneously providing a wealth of useful data that can have significant implications for both basic and applied research.
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