用单位 DNA 序列划分物种的界限.
Nicolas Hubert1, Jarrett D Phillips2,3, Robert H Hanner3
1UMR ISEM (IRD, UM, CNRS), Université de Montpellier, Montpellier, France. nicolas.hubert@ird.fr.
Methods in molecular biology (Clifton, N.J.)
|April 29, 2024
概括
通过使用遗传数据进行物种划界,DNA条形码有助于生物多样性库存. 本指南将距离,网络和基于树的方法进行比较,突出显示参数设置以获得可靠的结果.
科学领域:
- 分子生物学分子生物学
- 纳税学是一种分类学.
- 生物信息学是一种生物信息学.
背景情况:
- DNA 序列越来越多地用于大规模的生物多样性库存,为传统的标本分类提供了替代方案.
- 将遗传数据集成到分类学工作流中,刺激了各种物种划分模型的发展.
- 现有的物种划界方法往往产生不一致的结果,因为对谱系历史的不同假设.
研究的目的:
- 为基于DNA的物种划分方法提供全面的逐步指南.
- 通过使用基于距离,网络和树的模型进行分析,汇总必要的信息.
- 根据可靠性,可用性,可扩展性,可理解性和可用性,批判性地评估和分类方法.
主要方法:
- 探索基于距离的方法 (例如,自动条码差距发现 (ABGD),通过自动分区 (ASAP) 组装物种).
- 检查基于网络的方法 (例如,用于条形码索引号码 (BINs) 的精细单一连接 (RESL)).
- 对以树为基础的方法进行审查 (例如,Poisson Tree Processes [PTP],一般混合Yule Coalescent [GMYC]),包括遗传学重建选项 (例如,RAxML,BEAST).
主要成果:
- 详细介绍了准备输入文件,序列对齐和家族遗传重建的详细程序.
- 概述了基于距离,网络和树木的物种划分模型的分析选项.
- 确定参数设置对于实现一致和可靠的物种划分结果至关重要.
结论:
- 该研究提供了一种结构化的方法来解决基于DNA的物种划分的复杂性.
- 提出基于关键指标的比较框架,以帮助DNA条形码社区的方法选择.
- 讨论了当前物种划界方法的未来发展和局限性.
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