Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.

IF 6.1 1区 生物学 Q1 EVOLUTIONARY BIOLOGY
Daniele Silvestro, Thibault Latrille, Nicolas Salamin
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引用次数: 0

Abstract

Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitutions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or even tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we coupled stochastic simulations of genome evolution with a new supervised deep-learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence without requiring a known phylogenetic tree. The accuracy of our predictions matched that of likelihood-based phylogenetic inference when rate heterogeneity followed a simple gamma distribution, but it strongly exceeded it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we showed on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also showed that the integration of per-site rates obtained by deep learning within a Bayesian framework led to significantly more accurate phylogenetic inference, particularly with respect to the estimated branch lengths. We thus propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessment of statistical support.

系统发育估计的半监督学习方法。
模型一直是推断分子进化和重建系统发生树的核心。使用模型通常需要建立一个机理框架,反映我们对核苷酸取代等基本生物过程的理解,并通过最大似然法或贝叶斯推断法估计模型参数。然而,在复杂的进化情况下,推导和优化数据的似然性并不总是可能的,甚至对于大型数据集来说也不是一件容易的事,这往往会导致拟合模型中出现不切实际的简化假设。为了克服这个问题,我们将基因组进化的随机模拟与新的监督深度学习模型相结合,以推断分子进化的关键参数。我们的模型旨在直接分析多序列比对,并估算每个位点的进化速率和分歧,而无需已知的系统发生树。当速率异质性遵循简单的伽马分布时,我们预测的准确性与基于似然法的系统发育推断相匹配,但在更复杂的速率变异模式(如密码子模型)下,我们预测的准确性大大超过了似然法。我们的方法具有很强的可扩展性,可以高效地应用于基因组数据,正如我们在小丑鱼支系的 2600 万核苷酸数据集上所展示的那样。我们的模拟还表明,在贝叶斯框架内整合通过深度学习获得的每个位点率,可以大大提高系统发育推断的准确率,尤其是在估计分支长度方面。因此,我们建议,未来系统发生分析的进步将受益于半监督学习方法,这种方法结合了深度学习对替代率的估计和系统发生树的概率推断,前者允许更灵活的替代率变化模型,后者保证了可解释性和对统计支持的严格评估。
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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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