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Evolutionary footprint of coevolving positions in genes

机译:基因中共同进化位置的进化足迹

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Motivation: The analysis of molecular coevolution provides information on the potential functional and structural implication of positions along DNA sequences, and several methods are available to identify coevolving positions using probabilistic or combinatorial approaches. The specific nucleotide or amino acid profile associated with the coevolution process is, however, not estimated, but only known profiles, such as the Watson-Crick constraint, are usually considered a priori in current measures of coevolution.Results: Here, we propose a new probabilistic model, Coev, to identify coevolving positions and their associated profile in DNA sequences while incorporating the underlying phylogenetic relationships. The process of coevolution is modeled by a 16 x 16 instantaneous rate matrix that includes rates of transition as well as a profile of coevolution. We used simulated, empirical and illustrative data to evaluate our model and to compare it with a model of 'independent' evolution using Akaike Information Criterion. We showed that the Coev model is able to discriminate between coevolving and non-coevolving positions and provides better specificity and specificity than other available approaches. We further demonstrate that the identification of the profile of coevolution can shed new light on the process of dependent substitution during lineage evolution. Availability: http://www2.unil.ch/phylo/bioinformatics/coev
机译:动机:分子协同进化的分析提供了有关沿DNA序列的潜在潜在功能和结构含义的信息,并且有几种方法可以使用概率或组合方法来识别协同进化的位置。然而,与协同进化过程相关的特定核苷酸或氨基酸谱未作估算,但通常仅将已知谱(如Watson-Crick约束)视为当前协同进化测量的先验结果。新的概率模型Coev,可在结合潜在的系统发育关系的同时,识别DNA序列中共同进化的位置及其相关的特征。通过16 x 16瞬时速率矩阵对协同进化过程进行建模,该矩阵包括过渡速率以及协同进化曲线。我们使用模拟的,经验的和说明性的数据来​​评估我们的模型,并使用Akaike信息准则将其与“独立”演化模型进行比较。我们表明,Coev模型能够区分共同发展的立场和非共同发展的立场,并提供比其他可用方法更好的特异性。我们进一步证明,协同进化谱的鉴定可以为沿袭进化过程中的依赖取代过程提供新的思路。可用性:http://www2.unil.ch/phylo/bioinformatics/coev

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