CLADES: A classification-based machine learning method for species delimitation from population genetic data
CLADES: A classification-based machine learning method for species delimitation from population genetic data
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CLADES：一种基于分类的机器学习方法，用于从种群遗传数据中进行物种界定
DOI：
10.1111/1755-0998.12887
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发表时间：
2018
影响因子：
7.7
通讯作者：
Wu, Yufeng
中科院分区：
文献类型：
--
作者：
Pei, Jingwen;Chu, Chong;Li, Xin;Lu, Bin;Wu, Yufeng
物种被认为是生态和进化研究的基本单位。随着多位点基因组数据越来越多，人们对使用 DNA 序列数据来界定物种产生了很大的兴趣。在这项研究中，我们表明机器学习可用于物种界定。我们的方法将物种界定问题视为分类问题，用于根据训练数据识别新观察的类别。首先出于训练目的对广泛的进化参数进行广泛的模拟。将每对已知种群组合起来形成带有“相同物种”或“不同物种”标签的训练样本。我们使用支持向量机（SVM）来训练分类器，使用一组从训练样本计算得出的汇总统计数据作为特征。经过训练的分类器可以将测试样本分类为两种结果：“相同物种”或“不同物种”。给定多个相关生物体或种群的多位点基因组数据，我们的方法（称为 CLADES）通过首先对种群对进行分类来进行物种定界。然后，CLADES 通过最大化多个种群的物种分配的可能性来界定物种。 CLADES 通过广泛的模拟进行评估，并在真实的遗传数据上进行测试。我们证明，与现有方法相比，CLADES 对于物种定界既准确又有效。 CLADES 非常有用，尤其是当现有方法难以界定时，例如物种分化时间和基因流较短。
Species are considered to be the basic unit of ecological and evolutionary studies. As multilocus genomic data are increasingly available, there have been considerable interests in the use of DNA sequence data to delimit species. In this study, we show that machine learning can be used for species delimitation. Our method treats the species delimitation problem as a classification problem for identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purposes. Each pair of known populations is combined to form training samples with a label of “same species” or “different species”. We use support vector machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: “same species” or “different species”. Given multilocus genomic data of multiple related organisms or populations, our method (called CLADES) performs species delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in delimitation, for example with short species divergence time and gene flow.