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一个广义的机制性密码子模型。

A generalized mechanistic codon model.

作者信息

Zaheri Maryam, Dib Linda, Salamin Nicolas

机构信息

Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, SwitzerlandSwiss Institute of Bioinformatics, Genopode, Quartier Sorge, 1015 Lausanne, Switzerland.

Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, SwitzerlandSwiss Institute of Bioinformatics, Genopode, Quartier Sorge, 1015 Lausanne, Switzerland

出版信息

Mol Biol Evol. 2014 Sep;31(9):2528-41. doi: 10.1093/molbev/msu196. Epub 2014 Jun 23.

DOI:10.1093/molbev/msu196
PMID:24958740
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4137716/
Abstract

Models of codon evolution have attracted particular interest because of their unique capabilities to detect selection forces and their high fit when applied to sequence evolution. We described here a novel approach for modeling codon evolution, which is based on Kronecker product of matrices. The 61 × 61 codon substitution rate matrix is created using Kronecker product of three 4 × 4 nucleotide substitution matrices, the equilibrium frequency of codons, and the selection rate parameter. The entities of the nucleotide substitution matrices and selection rate are considered as parameters of the model, which are optimized by maximum likelihood. Our fully mechanistic model allows the instantaneous substitution matrix between codons to be fully estimated with only 19 parameters instead of 3,721, by using the biological interdependence existing between positions within codons. We illustrate the properties of our models using computer simulations and assessed its relevance by comparing the AICc measures of our model and other models of codon evolution on simulations and a large range of empirical data sets. We show that our model fits most biological data better compared with the current codon models. Furthermore, the parameters in our model can be interpreted in a similar way as the exchangeability rates found in empirical codon models.

摘要

密码子进化模型因其在检测选择力方面的独特能力以及应用于序列进化时的高度拟合性而备受关注。我们在此描述了一种基于矩阵克罗内克积的密码子进化建模新方法。61×61的密码子替换率矩阵是通过三个4×4核苷酸替换矩阵的克罗内克积、密码子的平衡频率以及选择率参数创建的。核苷酸替换矩阵的实体和选择率被视为模型的参数,并通过最大似然法进行优化。我们的完全机制模型利用密码子内各位置之间存在的生物学相互依存关系,仅用19个参数而非3721个参数就能全面估计密码子之间的瞬时替换矩阵。我们通过计算机模拟说明了我们模型的特性,并通过比较我们的模型与其他密码子进化模型在模拟数据和大量实证数据集上的AICc值来评估其相关性。我们表明,与当前的密码子模型相比,我们的模型能更好地拟合大多数生物学数据。此外,我们模型中的参数可以与实证密码子模型中的交换率以类似方式进行解释。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f77/4137716/fa6dce7d143d/msu196f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f77/4137716/fa6dce7d143d/msu196f1p.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f77/4137716/fa6dce7d143d/msu196f1p.jpg

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Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril.同义站点间替换率的变化极大地夸大了选择分析的假阳性率:忽视后果自负。
Mol Biol Evol. 2020 Aug 1;37(8):2430-2439. doi: 10.1093/molbev/msaa037.
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Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates.当存在多核苷酸突变和多个非同义速率时,广义参数密码子模型下的位点特异性正选择的推断得到改进。
BMC Evol Biol. 2019 Jan 14;19(1):22. doi: 10.1186/s12862-018-1326-7.
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正选择基因的荟萃分析揭示了人类大脑的近期适应性事件。
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