Phyddle:用深度学习探索系统发育模型的软件

IF 6.1 1区 生物学 Q1 EVOLUTIONARY BIOLOGY
Michael J Landis, Ammon Thompson
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引用次数: 0

摘要

系统发生学包含了丰富的关于进化历史和产生生命多样性的过程的信息。这些信息可以通过拟合树的系统发育模型来提取。然而,许多现实的系统发育模型缺乏可处理的似然函数,禁止它们与标准推理方法一起使用。我们提出了基于管道的软件,用于使用无似然深度学习方法在树上执行系统发育建模任务。Phyddle具有灵活的命令行界面,可以轻松地将系统发育的深度学习方法集成到研究工作流程中。phyddle通过五个管道分析步骤(模拟、格式化、训练、估计和绘图)来协调建模任务,这些步骤将原始系统发育数据集作为输入转换为基于数值和可视化模型的输出。我们进行了三个实验来比较基于似然的推断和基于深度学习的推断的准确性。基准测试表明,phyddle可以准确地执行其设计的推理任务,例如估计宏观进化参数,在连续特征进化模型中进行选择,以及通过流行病学模型的覆盖测试,甚至对于缺乏可处理可能性的模型。在https://phyddle.org了解更多关于phyddle的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
phyddle: software for exploring phylogenetic models with deep learning
Phylogenies contain a wealth of information about the evolutionary history and process that gave rise to the diversity of life. This information can be extracted by fitting phylogenetic models to trees. However, many realistic phylogenetic models lack tractable likelihood functions, prohibiting their use with standard inference methods. We present phyddle, pipeline-based software for performing phylogenetic modeling tasks on trees using likelihood-free deep learning approaches. phyddle has a flexible command-line interface, making it easy to integrate deep learning approaches for phylogenetics into research workflows. phyddle coordinates modeling tasks through five pipeline analysis steps (Simulate, Format, Train, Estimate, and Plot) that transform raw phylogenetic datasets as input into numerical and visual model-based output. We conduct three experiments to compare the accuracy of likelihood-based inferences against deep learning-based inferences obtained through phyddle. Benchmarks show that phyddle accurately performs the inference tasks for which it was designed, such as estimating macroevolutionary parameters, selecting among continuous trait evolution models, and passing coverage tests for epidemiological models, even for models that lack tractable likelihoods. Learn more about phyddle at https://phyddle.org.
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来源期刊
Systematic Biology
Systematic Biology 生物-进化生物学
CiteScore
13.00
自引率
7.70%
发文量
70
审稿时长
6-12 weeks
期刊介绍: Systematic Biology is the bimonthly journal of the Society of Systematic Biologists. Papers for the journal are original contributions to the theory, principles, and methods of systematics as well as phylogeny, evolution, morphology, biogeography, paleontology, genetics, and the classification of all living things. A Points of View section offers a forum for discussion, while book reviews and announcements of general interest are also featured.
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