Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.
Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.
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使用观察数据和系统发育树自动注释基因功能的贝叶斯参数估计。
DOI：
10.1371/journal.pcbi.1007948
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Marjoram P
中科院分区：
文献类型：
--
作者：
Vega Yon GG;Thomas DC;Morrison J;Mi H;Thomas PD;Marjoram P
基因功能注释对于遗传数据的各种下游分析是重要的。但函数的实验表征仍然昂贵而缓慢，这使得计算预测成为一项重要的努力。已经开发了预测的系统学方法，但实施实用的贝叶斯参数估计框架仍然是一个突出的挑战。我们已经开发了一个基于贝叶斯框架的基因注释进化的计算高效模型，该模型基于贝叶斯框架，使用马尔可夫链蒙特卡罗进行参数估计。与以前的方法不同，我们的方法能够在许多不同的系统发育树和功能上估计参数。由此产生的参数与生物学直觉一致，例如基因复制后功能改变的可能性增加。该方法在留一交叉验证中表现良好，我们进一步验证了实验科学文献中的一些预测。了解基因在生命中扮演的个体角色是生物医学科学中的一个关键问题。虽然关于基因功能的信息在不断增长，但具有未确定生物学功能的基因的数量仍然更多。正因为如此，科学家们花费了大量时间来构建和设计自动推断基因功能的工具。最有希望的方法之一(有时被称为系统基因组学)试图沿着与基因家族不同成员相关的系统发育树的分支构建一个遗传和功能分歧的模型。如果一个或多个家庭成员的功能已经被实验表征，则可以基于进化关系在概率框架中预测其他家庭成员的这些功能的存在或不存在。以前提出的参数估计的贝叶斯方法被证明在计算上是困难的，阻碍了这样的概率模型的发展。在本文中，我们提出了一个简单的、高度可扩展的基因功能进化模型，这意味着不仅可以对一个家族进行参数估计，而且可以同时对包含数千个基因的数百个基因家族进行参数估计。我们得到的参数估计与关于基因功能如何进化的理论所规定的一致。最后，尽管该模型很简单，但其预测质量与其他更复杂的备选方案不相上下。尽管我们相信我们的模型可以进一步改进，但即使是这个简单的模型也做出了可验证的预测，并提出了现有注释显示不一致的领域，这些不一致可能表明错误或争议。
Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature. Understanding the individual roles that genes play in life is a key issue in biomedical science. While information regarding gene functions is continuously growing, the number of genes with uncharacterized biological functions is still greater. Because of this, scientists have dedicated much of their time to build and design tools that automatically infer gene functions. One of the most promising approaches (sometimes called “phylogenomics”) attempts to construct a model of inheritance and divergence of function along branches of the phylogenetic tree that relates different members of a gene family. If the functions of one or more of the family members has been characterized experimentally, the presence or absence of these functions for other family members can be predicted, in a probabilistic framework, based on the evolutionary relationships. Previously proposed Bayesian approaches to parameter estimation have proved to be computationally intractable, preventing development of such a probabilistic model. In this paper, we present a simple model of gene-function evolution that is highly scalable, which means that it is possible to perform parameter estimation not only on one family, but simultaneously for hundreds of gene families, comprising thousands of genes. The parameter estimates we obtain coherently agree with what theory dictates regarding how gene-functions evolved. Finally, notwithstanding its simplicity, the model’s prediction quality is comparable to other more complex alternatives. Although we believe further improvements can be made to our model, even this simple model makes verifiable predictions, and suggests areas in which existing annotations show inconsistencies that may indicate errors or controversies.
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